diff --git a/build/darknet/YoloWrapper.cs b/build/darknet/YoloWrapper.cs
deleted file mode 100644
index 52c12adb80f..00000000000
--- a/build/darknet/YoloWrapper.cs
+++ /dev/null
@@ -1,89 +0,0 @@
-using System;
-using System.Runtime.InteropServices;
-
-namespace Darknet
-{
-    public class YoloWrapper : IDisposable
-    {
-        private const string YoloLibraryName = "yolo_cpp_dll.dll";
-        private const int MaxObjects = 1000;
-
-        [DllImport(YoloLibraryName, EntryPoint = "init")]
-        private static extern int InitializeYolo(string configurationFilename, string weightsFilename, int gpu);
-
-        [DllImport(YoloLibraryName, EntryPoint = "detect_image")]
-        private static extern int DetectImage(string filename, ref BboxContainer container);
-
-        [DllImport(YoloLibraryName, EntryPoint = "detect_mat")]
-        private static extern int DetectImage(IntPtr pArray, int nSize, ref BboxContainer container);
-
-        [DllImport(YoloLibraryName, EntryPoint = "dispose")]
-        private static extern int DisposeYolo();
-
-        [StructLayout(LayoutKind.Sequential)]
-        public struct bbox_t
-        {
-            public UInt32 x, y, w, h;    // (x,y) - top-left corner, (w, h) - width & height of bounded box
-            public float prob;           // confidence - probability that the object was found correctly
-            public UInt32 obj_id;        // class of object - from range [0, classes-1]
-            public UInt32 track_id;      // tracking id for video (0 - untracked, 1 - inf - tracked object)
-            public UInt32 frames_counter;
-            public float x_3d, y_3d, z_3d;  // 3-D coordinates, if there is used 3D-stereo camera
-        };
-
-        [StructLayout(LayoutKind.Sequential)]
-        public struct BboxContainer
-        {
-            [MarshalAs(UnmanagedType.ByValArray, SizeConst = MaxObjects)]
-            public bbox_t[] candidates;
-        }
-
-        public YoloWrapper(string configurationFilename, string weightsFilename, int gpu)
-        {
-            InitializeYolo(configurationFilename, weightsFilename, gpu);
-        }
-
-        public void Dispose()
-        {
-            DisposeYolo();
-        }
-
-        public bbox_t[] Detect(string filename)
-        {
-            var container = new BboxContainer();
-            var count = DetectImage(filename, ref container);
-
-            return container.candidates;
-        }
-
-        public bbox_t[] Detect(byte[] imageData)
-        {
-            var container = new BboxContainer();
-
-            var size = Marshal.SizeOf(imageData[0]) * imageData.Length;
-            var pnt = Marshal.AllocHGlobal(size);
-
-            try
-            {
-                // Copy the array to unmanaged memory.
-                Marshal.Copy(imageData, 0, pnt, imageData.Length);
-                var count = DetectImage(pnt, imageData.Length, ref container);
-                if (count == -1)
-                {
-                    throw new NotSupportedException($"{YoloLibraryName} has no OpenCV support");
-                }
-            }
-            catch (Exception exception)
-            {
-                return null;
-            }
-            finally
-            {
-                // Free the unmanaged memory.
-                Marshal.FreeHGlobal(pnt);
-            }
-
-            return container.candidates;
-        }
-    }
-}
diff --git a/build/darknet/darknet.sln b/build/darknet/darknet.sln
deleted file mode 100644
index c49e10efa09..00000000000
--- a/build/darknet/darknet.sln
+++ /dev/null
@@ -1,28 +0,0 @@
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-EndProject
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-	GlobalSection(SolutionConfigurationPlatforms) = preSolution
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-		{4CF5694F-12A5-4012-8D94-9A0915E9FEB5}.Debug|x64.Build.0 = Debug|x64
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-		{4CF5694F-12A5-4012-8D94-9A0915E9FEB5}.Release|Win32.Build.0 = Release|Win32
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diff --git a/build/darknet/darknet.vcxproj b/build/darknet/darknet.vcxproj
deleted file mode 100644
index 5c8a7c3e1b9..00000000000
--- a/build/darknet/darknet.vcxproj
+++ /dev/null
@@ -1,309 +0,0 @@
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diff --git a/build/darknet/darknet_no_gpu.sln b/build/darknet/darknet_no_gpu.sln
deleted file mode 100644
index 79d7c0a5daa..00000000000
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-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-######################
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=57
-activation=linear
-
-
-[Gaussian_yolo]
-mask = 6,7,8
-anchors = 7,10, 14,24, 27,43, 32,97, 57,64, 92,109, 73,175, 141,178, 144,291
-classes=10
-num=9
-jitter=.3
-ignore_thresh = .5
-truth_thresh = 1
-iou_thresh=0.213
-uc_normalizer=1.0
-cls_normalizer=1.0
-iou_normalizer=0.5
-iou_loss=giou
-scale_x_y=1.0
-random=1
-
-
-[route]
-layers = -4
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[upsample]
-stride=2
-
-[route]
-layers = -1, 61
-
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=57
-activation=linear
-
-
-[Gaussian_yolo]
-mask = 3,4,5
-anchors = 7,10, 14,24, 27,43, 32,97, 57,64, 92,109, 73,175, 141,178, 144,291
-classes=10
-num=9
-jitter=.3
-ignore_thresh = .5
-truth_thresh = 1
-iou_thresh=0.213
-uc_normalizer=1.0
-cls_normalizer=1.0
-iou_normalizer=0.5
-iou_loss=giou
-scale_x_y=1.0
-random=1
-
-
-
-[route]
-layers = -4
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[upsample]
-stride=2
-
-[route]
-layers = -1, 36
-
-
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=57
-activation=linear
-
-
-[Gaussian_yolo]
-mask = 0,1,2
-anchors = 7,10, 14,24, 27,43, 32,97, 57,64, 92,109, 73,175, 141,178, 144,291
-classes=10
-num=9
-jitter=.3
-ignore_thresh = .5
-truth_thresh = 1
-iou_thresh=0.213
-uc_normalizer=1.0
-cls_normalizer=1.0
-iou_normalizer=0.5
-iou_loss=giou
-scale_x_y=1.0
-random=1
diff --git a/build/darknet/x64/cfg/alexnet.cfg b/build/darknet/x64/cfg/alexnet.cfg
deleted file mode 100644
index 7e5a9b26a57..00000000000
--- a/build/darknet/x64/cfg/alexnet.cfg
+++ /dev/null
@@ -1,95 +0,0 @@
-[net]
-batch=128
-subdivisions=1
-height=227
-width=227
-channels=3
-momentum=0.9
-decay=0.0005
-max_crop=256
-
-learning_rate=0.01
-policy=poly
-power=4
-max_batches=800000
-
-angle=7
-hue = .1
-saturation=.75
-exposure=.75
-aspect=.75
-
-[convolutional]
-filters=96
-size=11
-stride=4
-pad=0
-activation=relu
-
-[maxpool]
-size=3
-stride=2
-padding=0
-
-[convolutional]
-filters=256
-size=5
-stride=1
-pad=1
-activation=relu
-
-[maxpool]
-size=3
-stride=2
-padding=0
-
-[convolutional]
-filters=384
-size=3
-stride=1
-pad=1
-activation=relu
-
-[convolutional]
-filters=384
-size=3
-stride=1
-pad=1
-activation=relu
-
-[convolutional]
-filters=256
-size=3
-stride=1
-pad=1
-activation=relu
-
-[maxpool]
-size=3
-stride=2
-padding=0
-
-[connected]
-output=4096
-activation=relu
-
-[dropout]
-probability=.5
-
-[connected]
-output=4096
-activation=relu
-
-[dropout]
-probability=.5
-
-[connected]
-output=1000
-activation=linear
-
-[softmax]
-groups=1
-
-[cost]
-type=sse
-
diff --git a/build/darknet/x64/cfg/cd53paspp-gamma.cfg b/build/darknet/x64/cfg/cd53paspp-gamma.cfg
deleted file mode 100644
index be3dbff6e5a..00000000000
--- a/build/darknet/x64/cfg/cd53paspp-gamma.cfg
+++ /dev/null
@@ -1,1153 +0,0 @@
-[net]
-# Testing
-#batch=1
-#subdivisions=1
-# Training
-batch=64
-subdivisions=8
-width=512
-height=512
-channels=3
-momentum=0.949
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-learning_rate=0.00261
-burn_in=1000
-max_batches = 500500
-policy=steps
-steps=400000,450000
-scales=.1,.1
-
-#cutmix=1
-mosaic=1
-
-#:104x104 54:52x52 85:26x26 104:13x13 for 416
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers = -1,-7
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers = -1,-10
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers = -1,-28
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers = -1,-28
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers = -1,-16
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=leaky
-
-##########################
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-### SPP ###
-[maxpool]
-stride=1
-size=5
-
-[route]
-layers=-2
-
-[maxpool]
-stride=1
-size=9
-
-[route]
-layers=-4
-
-[maxpool]
-stride=1
-size=13
-
-[route]
-layers=-1,-3,-5,-6
-### End SPP ###
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[upsample]
-stride=2
-
-[route]
-layers = 85
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers = -1, -3
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[upsample]
-stride=2
-
-[route]
-layers = 54
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers = -1, -3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-##########################
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=linear
-
-
-[yolo]
-mask = 0,1,2
-anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
-classes=80
-num=9
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-scale_x_y = 1.2
-iou_thresh=0.213
-cls_normalizer=1.0
-iou_normalizer=0.07
-iou_loss=ciou
-nms_kind=greedynms
-beta_nms=0.6
-
-[route]
-layers = -4
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=2
-pad=1
-filters=256
-activation=leaky
-
-[route]
-layers = -1, -16
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=linear
-
-
-[yolo]
-mask = 3,4,5
-anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
-classes=80
-num=9
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-scale_x_y = 1.1
-iou_thresh=0.213
-cls_normalizer=1.0
-iou_normalizer=0.07
-iou_loss=ciou
-nms_kind=greedynms
-beta_nms=0.6
-
-[route]
-layers = -4
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=2
-pad=1
-filters=512
-activation=leaky
-
-[route]
-layers = -1, -37
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=linear
-
-
-[yolo]
-mask = 6,7,8
-anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
-classes=80
-num=9
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-random=1
-scale_x_y = 1.05
-iou_thresh=0.213
-cls_normalizer=1.0
-iou_normalizer=0.07
-iou_loss=ciou
-nms_kind=greedynms
-beta_nms=0.6
diff --git a/build/darknet/x64/cfg/cifar.cfg b/build/darknet/x64/cfg/cifar.cfg
deleted file mode 100644
index f2c801a5a9e..00000000000
--- a/build/darknet/x64/cfg/cifar.cfg
+++ /dev/null
@@ -1,126 +0,0 @@
-[net]
-batch=128
-subdivisions=1
-height=32
-width=32
-channels=3
-momentum=0.9
-decay=0.0005
-
-learning_rate=0.4
-policy=poly
-power=4
-max_batches = 50000
-
-[crop]
-crop_width=28
-crop_height=28
-flip=1
-angle=0
-saturation = 1
-exposure = 1
-noadjust=1
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[dropout]
-probability=.5
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[dropout]
-probability=.5
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[dropout]
-probability=.5
-
-[convolutional]
-filters=10
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[avgpool]
-
-[softmax]
-groups=1
-
-[cost]
-
diff --git a/build/darknet/x64/cfg/cifar.test.cfg b/build/darknet/x64/cfg/cifar.test.cfg
deleted file mode 100644
index d3afcdd79b7..00000000000
--- a/build/darknet/x64/cfg/cifar.test.cfg
+++ /dev/null
@@ -1,119 +0,0 @@
-[net]
-batch=128
-subdivisions=1
-height=32
-width=32
-channels=3
-momentum=0.9
-decay=0.0005
-
-learning_rate=0.4
-policy=poly
-power=4
-max_batches = 50000
-
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[dropout]
-probability=.5
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[dropout]
-probability=.5
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[dropout]
-probability=.5
-
-[convolutional]
-filters=10
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[avgpool]
-
-[softmax]
-groups=1
-temperature=3
-
-[cost]
-
diff --git a/build/darknet/x64/cfg/coco.data b/build/darknet/x64/cfg/coco.data
deleted file mode 100644
index f51d8428839..00000000000
--- a/build/darknet/x64/cfg/coco.data
+++ /dev/null
@@ -1,8 +0,0 @@
-classes= 80
-train  = E:/MSCOCO/trainvalno5k.txt
-#train = E:/MSCOCO/5k.txt
-valid  = E:/MSCOCO/5k.txt
-names = data/coco.names
-backup = backup
-eval=coco
-
diff --git a/build/darknet/x64/cfg/combine9k.data b/build/darknet/x64/cfg/combine9k.data
deleted file mode 100644
index 06a3e76aefa..00000000000
--- a/build/darknet/x64/cfg/combine9k.data
+++ /dev/null
@@ -1,10 +0,0 @@
-classes= 9418
-#train  = /home/pjreddie/data/coco/trainvalno5k.txt
-train  = data/combine9k.train.list
-valid  = /home/pjreddie/data/imagenet/det.val.files
-labels = data/9k.labels
-names  = data/9k.names
-backup = backup/
-map = data/inet9k.map
-eval = imagenet
-results = results
diff --git a/build/darknet/x64/cfg/crnn.train.cfg b/build/darknet/x64/cfg/crnn.train.cfg
deleted file mode 100644
index e0e0b54c4c9..00000000000
--- a/build/darknet/x64/cfg/crnn.train.cfg
+++ /dev/null
@@ -1,52 +0,0 @@
-[net]
-subdivisions=8
-inputs=256
-batch = 128
-momentum=0.9
-decay=0.001
-max_batches = 2000
-time_steps=576
-learning_rate=0.1
-policy=steps
-steps=1000,1500
-scales=.1,.1
-
-try_fix_nan=1
-
-[connected]
-output=256
-activation=leaky
-
-[crnn]
-batch_normalize=1
-size=1
-pad=0
-output = 1024
-hidden=1024
-activation=leaky
-
-[crnn]
-batch_normalize=1
-size=1
-pad=0
-output = 1024
-hidden=1024
-activation=leaky
-
-[crnn]
-batch_normalize=1
-size=1
-pad=0
-output = 1024
-hidden=1024
-activation=leaky
-
-[connected]
-output=256
-activation=leaky
-
-[softmax]
-
-[cost]
-type=sse
-
diff --git a/build/darknet/x64/cfg/csdarknet53-omega.cfg b/build/darknet/x64/cfg/csdarknet53-omega.cfg
deleted file mode 100644
index 3a3c0730f66..00000000000
--- a/build/darknet/x64/cfg/csdarknet53-omega.cfg
+++ /dev/null
@@ -1,762 +0,0 @@
-[net]
-# Training
-batch=128
-subdivisions=4
-
-label_smooth_eps=0.1
-
-# Testing
-# batch=1
-# subdivisions=1
-
-height=256
-width=256
-channels=3
-min_crop=128
-max_crop=448
-
-mosaic=1
-cutmix=1
-
-burn_in=1000
-learning_rate=0.1
-policy=poly
-power=4
-max_batches=1200000
-momentum=0.9
-decay=0.0005
-
-angle=7
-hue=.1
-saturation=.75
-exposure=.75
-aspect=.75
-
-
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=mish
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=2
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1,-7
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=2
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1,-10
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=2
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1,-28
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=2
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1,-28
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=2
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1,-16
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=mish
-
-[avgpool]
-
-[convolutional]
-filters=1000
-size=1
-stride=1
-pad=1
-activation=linear
-
-[softmax]
-groups=1
diff --git a/build/darknet/x64/cfg/cspx-p7-mish-omega.cfg b/build/darknet/x64/cfg/cspx-p7-mish-omega.cfg
deleted file mode 100644
index 93c4aefa94c..00000000000
--- a/build/darknet/x64/cfg/cspx-p7-mish-omega.cfg
+++ /dev/null
@@ -1,1459 +0,0 @@
-[net]
-# Training
-batch=100
-subdivisions=2
-
-label_smooth_eps=0.1
-
-# Testing
-# batch=1
-# subdivisions=1
-
-height=320
-width=320
-channels=3
-min_crop=320
-max_crop=640
-
-mosaic=1
-cutmix=1
-
-burn_in=2000
-learning_rate=0.1
-policy=poly
-power=4
-max_batches=1200000
-#max_batches=400000
-momentum=0.9
-decay=0.0005
-
-angle=7
-hue=.1
-saturation=.75
-exposure=.75
-aspect=.75
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=3
-stride=1
-pad=1
-activation=mish
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=3
-stride=2
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=40
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=2
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1,-13
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=2
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1,-49
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=2
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1,-49
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=1280
-size=3
-stride=2
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1,-25
-
-[convolutional]
-batch_normalize=1
-filters=1280
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=1280
-size=3
-stride=2
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1,-25
-
-[convolutional]
-batch_normalize=1
-filters=1280
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=1280
-size=3
-stride=2
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1,-25
-
-[convolutional]
-batch_normalize=1
-filters=1600
-size=1
-stride=1
-pad=1
-activation=mish
-
-[avgpool]
-
-[convolutional]
-filters=1000
-size=1
-stride=1
-pad=1
-activation=linear
-
-[softmax]
-groups=1
\ No newline at end of file
diff --git a/build/darknet/x64/cfg/cspx-p7-mish.cfg b/build/darknet/x64/cfg/cspx-p7-mish.cfg
deleted file mode 100644
index 01be7283680..00000000000
--- a/build/darknet/x64/cfg/cspx-p7-mish.cfg
+++ /dev/null
@@ -1,2604 +0,0 @@
-[net]
-# Testing
-#batch=1
-#subdivisions=1
-# Training
-batch=64
-subdivisions=8
-width=1536
-height=1536
-channels=3
-momentum=0.949
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-learning_rate=0.001
-burn_in=1000
-max_batches = 500500
-steps=400000,450000
-#max_batches = 770500
-#steps=700000,750000
-policy=steps
-scales=.1,.1
-
-mosaic=1
-letter_box=1
-
-### Start of Backbone ###
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=3
-stride=1
-pad=1
-activation=mish
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=3
-stride=2
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=40
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=2
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1,-13
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=2
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1,-49
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=2
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1,-49
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=1280
-size=3
-stride=2
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1,-25
-
-[convolutional]
-batch_normalize=1
-filters=1280
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=1280
-size=3
-stride=2
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1,-25
-
-[convolutional]
-batch_normalize=1
-filters=1280
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=1280
-size=3
-stride=2
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1,-25
-
-[convolutional]
-batch_normalize=1
-filters=1280
-size=1
-stride=1
-pad=1
-activation=mish
-
-### End of backbone ###
-
-### Start of CSPSPP ###
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=640
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[maxpool]
-stride=1
-size=5
-
-[route]
-layers=-2
-
-[maxpool]
-stride=1
-size=9
-
-[route]
-layers=-4
-
-[maxpool]
-stride=1
-size=13
-
-[route]
-layers=-1,-3,-5,-6
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=640
-activation=mish
-
-[route]
-layers = -1, -13
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-### End of CSPSPP ###
-
-### Start of CSPPAN ###
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[upsample]
-stride=2
-
-[route]
-layers = 180 ###P6
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1, -3
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=640
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=640
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=640
-activation=mish
-
-[route]
-layers = -1, -8
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[upsample]
-stride=2
-
-[route]
-layers = 152 ###P5
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1, -3
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=640
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=640
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=640
-activation=mish
-
-[route]
-layers = -1, -8
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[upsample]
-stride=2
-
-[route]
-layers = 124 ###P4
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1, -3
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=320
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=320
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=320
-activation=mish
-
-[route]
-layers = -1, -8
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[upsample]
-stride=2
-
-[route]
-layers = 72 ###P3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1, -3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=128
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=128
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=128
-activation=mish
-
-[route]
-layers = -1, -8
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=2
-pad=1
-filters=320
-activation=mish
-
-[route]
-layers = -1, 271 ###S4
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=320
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=320
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=320
-activation=mish
-
-[route]
-layers = -1, -8
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=2
-pad=1
-filters=640
-activation=mish
-
-[route]
-layers = -1, 255 ###S5
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=640
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=640
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=640
-activation=mish
-
-[route]
-layers = -1, -8
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=2
-pad=1
-filters=640
-activation=mish
-
-[route]
-layers = -1, 239 ###S6
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=640
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=640
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=640
-activation=mish
-
-[route]
-layers = -1, -8
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=2
-pad=1
-filters=640
-activation=mish
-
-[route]
-layers = -1, 223 ###S7
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=640
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=640
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=640
-activation=mish
-
-[route]
-layers = -1, -8
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-### End of CSPPAN ###
-
-### Start of YOLO ###
-
-[route]
-layers = 287 ###
-
-[convolutional]
-batch_normalize=1
-size=1
-stride=1
-pad=1
-filters=320
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=1
-stride=1
-pad=1
-filters=320
-activation=logistic
-
-[sam]
-from=-2
-
-[convolutional]
-batch_normalize=1
-size=1
-stride=1
-pad=1
-filters=320
-activation=mish
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=340
-activation=logistic
-
-
-[yolo]
-mask = 0,1,2,3
-anchors = 13,17,  22,25,  27,66,  55,41, 57,88,  112,69,  69,177,  136,138, 136,138,  287,114,  134,275,  268,248, 268,248,  232,504,  445,416,  640,640, 812,393,  477,808,  1070,908,  1408,1408
-classes=80
-num=16
-ignore_thresh = .7
-truth_thresh = 1
-#random=1
-resize=1.5
-scale_x_y = 2.0
-jitter=.1
-objectness_smooth=1
-iou_thresh=0.2
-iou_normalizer=0.05
-cls_normalizer=0.5
-obj_normalizer=4.0
-iou_loss=ciou
-nms_kind=diounms
-beta_nms=0.6
-max_delta=40
-new_coords=1
-
-[route]
-layers = 300 ###
-
-[convolutional]
-batch_normalize=1
-size=1
-stride=1
-pad=1
-filters=640
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=1
-stride=1
-pad=1
-filters=640
-activation=logistic
-
-[sam]
-from=-2
-
-[convolutional]
-batch_normalize=1
-size=1
-stride=1
-pad=1
-filters=640
-activation=mish
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=340
-activation=logistic
-
-[yolo]
-mask = 4,5,6,7
-anchors = 13,17,  22,25,  27,66,  55,41, 57,88,  112,69,  69,177,  136,138, 136,138,  287,114,  134,275,  268,248, 268,248,  232,504,  445,416,  640,640, 812,393,  477,808,  1070,908,  1408,1408
-classes=80
-num=16
-ignore_thresh = .7
-truth_thresh = 1
-#random=1
-resize=1.5
-scale_x_y = 2.0
-jitter=.1
-objectness_smooth=1
-iou_thresh=0.2
-iou_normalizer=0.05
-cls_normalizer=0.5
-obj_normalizer=1.0
-iou_loss=ciou
-nms_kind=diounms
-beta_nms=0.6
-max_delta=40
-new_coords=1
-
-[route]
-layers = 313 ###
-
-[convolutional]
-batch_normalize=1
-size=1
-stride=1
-pad=1
-filters=1280
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=1
-stride=1
-pad=1
-filters=1280
-activation=logistic
-
-[sam]
-from=-2
-
-[convolutional]
-batch_normalize=1
-size=1
-stride=1
-pad=1
-filters=1280
-activation=mish
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=340
-activation=logistic
-
-[yolo]
-mask = 8,9,10,11
-anchors = 13,17,  22,25,  27,66,  55,41, 57,88,  112,69,  69,177,  136,138, 136,138,  287,114,  134,275,  268,248, 268,248,  232,504,  445,416,  640,640, 812,393,  477,808,  1070,908,  1408,1408
-classes=80
-num=16
-ignore_thresh = .7
-truth_thresh = 1
-#random=1
-resize=1.5
-scale_x_y = 2.0
-jitter=.1
-objectness_smooth=1
-iou_thresh=0.2
-iou_normalizer=0.05
-cls_normalizer=0.5
-obj_normalizer=0.5
-iou_loss=ciou
-nms_kind=diounms
-beta_nms=0.6
-max_delta=40
-new_coords=1
-
-[route]
-layers = 326 ###
-
-[convolutional]
-batch_normalize=1
-size=1
-stride=1
-pad=1
-filters=1280
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=1
-stride=1
-pad=1
-filters=1280
-activation=logistic
-
-[sam]
-from=-2
-
-[convolutional]
-batch_normalize=1
-size=1
-stride=1
-pad=1
-filters=1280
-activation=mish
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=340
-activation=logistic
-
-[yolo]
-mask = 12,13,14,15
-anchors = 13,17,  22,25,  27,66,  55,41, 57,88,  112,69,  69,177,  136,138, 136,138,  287,114,  134,275,  268,248, 268,248,  232,504,  445,416,  640,640, 812,393,  477,808,  1070,908,  1408,1408
-classes=80
-num=16
-ignore_thresh = .7
-truth_thresh = 1
-#random=1
-resize=1.5
-scale_x_y = 2.0
-jitter=.1
-objectness_smooth=1
-iou_thresh=0.2
-iou_normalizer=0.05
-cls_normalizer=0.5
-obj_normalizer=0.4
-iou_loss=ciou
-nms_kind=diounms
-beta_nms=0.6
-max_delta=40
-new_coords=1
-
-[route]
-layers = 339 ###
-
-[convolutional]
-batch_normalize=1
-size=1
-stride=1
-pad=1
-filters=1280
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=1
-stride=1
-pad=1
-filters=1280
-activation=logistic
-
-[sam]
-from=-2
-
-[convolutional]
-batch_normalize=1
-size=1
-stride=1
-pad=1
-filters=1280
-activation=mish
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=340
-activation=logistic
-
-[yolo]
-mask = 16,17,18,19
-anchors = 13,17,  22,25,  27,66,  55,41, 57,88,  112,69,  69,177,  136,138, 136,138,  287,114,  134,275,  268,248, 268,248,  232,504,  445,416,  640,640, 812,393,  477,808,  1070,908,  1408,1408
-classes=80
-num=16
-ignore_thresh = .7
-truth_thresh = 1
-#random=1
-resize=1.5
-scale_x_y = 2.0
-jitter=.1
-objectness_smooth=1
-iou_thresh=0.2
-iou_normalizer=0.05
-cls_normalizer=0.5
-obj_normalizer=0.1
-iou_loss=ciou
-nms_kind=diounms
-beta_nms=0.6
-max_delta=40
-new_coords=1
-
-### End of YOLO ###
\ No newline at end of file
diff --git a/build/darknet/x64/cfg/cspx-p7-mish_hp.cfg b/build/darknet/x64/cfg/cspx-p7-mish_hp.cfg
deleted file mode 100644
index 209fa8f81cb..00000000000
--- a/build/darknet/x64/cfg/cspx-p7-mish_hp.cfg
+++ /dev/null
@@ -1,2638 +0,0 @@
-[net]
-# Testing
-#batch=1
-#subdivisions=1
-# Training
-batch=64
-subdivisions=16
-#width=1536
-#height=1536
-width=896
-height=896
-channels=3
-momentum=0.949
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-learning_rate=0.001
-burn_in=1000
-max_batches = 500500
-policy=steps
-steps=400000,450000
-scales=.1,.1
-
-mosaic=1
-letter_box=1
-
-### Start of Backbone ###
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=3
-stride=1
-pad=1
-activation=mish
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=3
-stride=2
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=40
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=2
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1,-13
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=2
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1,-49
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=2
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1,-49
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=1280
-size=3
-stride=2
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1,-25
-
-[convolutional]
-batch_normalize=1
-filters=1280
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=1280
-size=3
-stride=2
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1,-25
-
-[convolutional]
-batch_normalize=1
-filters=1280
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=1280
-size=3
-stride=2
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1,-25
-
-[convolutional]
-batch_normalize=1
-filters=1280
-size=1
-stride=1
-pad=1
-activation=mish
-
-### End of backbone ###
-
-### Start of CSPSPP ###
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=640
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[maxpool]
-stride=1
-size=5
-
-[route]
-layers=-2
-
-[maxpool]
-stride=1
-size=9
-
-[route]
-layers=-4
-
-[maxpool]
-stride=1
-size=13
-
-[route]
-layers=-1,-3,-5,-6
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=640
-activation=mish
-
-[route]
-layers = -1, -13
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-### End of CSPSPP ###
-
-### Start of CSPPAN ###
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[upsample]
-stride=2
-
-[route]
-layers = 180 ###P6
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1, -3
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=640
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=640
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=640
-activation=mish
-
-[route]
-layers = -1, -8
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[upsample]
-stride=2
-
-[route]
-layers = 152 ###P5
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1, -3
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=640
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=640
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=640
-activation=mish
-
-[route]
-layers = -1, -8
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[upsample]
-stride=2
-
-[route]
-layers = 124 ###P4
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1, -3
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=320
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=320
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=320
-activation=mish
-
-[route]
-layers = -1, -8
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[upsample]
-stride=2
-
-[route]
-layers = 72 ###P3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1, -3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=128
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=128
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=128
-activation=mish
-
-[route]
-layers = -1, -8
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=2
-pad=1
-filters=320
-activation=mish
-
-[route]
-layers = -1, 271 ###S4
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=320
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=320
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=320
-activation=mish
-
-[route]
-layers = -1, -8
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=2
-pad=1
-filters=640
-activation=mish
-
-[route]
-layers = -1, 255 ###S5
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=640
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=640
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=640
-activation=mish
-
-[route]
-layers = -1, -8
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=2
-pad=1
-filters=640
-activation=mish
-
-[route]
-layers = -1, 239 ###S6
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=640
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=640
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=640
-activation=mish
-
-[route]
-layers = -1, -8
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=2
-pad=1
-filters=640
-activation=mish
-
-[route]
-layers = -1, 223 ###S7
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=640
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=640
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=640
-activation=mish
-
-[route]
-layers = -1, -8
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-### End of CSPPAN ###
-
-### Start of YOLO ###
-
-[route]
-layers = 287 ###
-
-[convolutional]
-batch_normalize=1
-size=1
-stride=1
-pad=1
-filters=320
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=1
-stride=1
-pad=1
-filters=320
-activation=logistic
-
-[sam]
-from=-2
-
-[convolutional]
-batch_normalize=1
-size=1
-stride=1
-pad=1
-filters=320
-activation=mish
-
-[convolutional]
-size=1
-stride=1
-pad=1
-#filters=340
-filters=170
-activation=linear
-
-#[route]
-#layers=-1
-
-[yolo]
-mask = 0,1
-anchors = 13,17,  31,25,   24,51, 61,45    61,45,  48,102,  119,96, 97,189,    97,189,  217,184,  171,384, 324,451,    324,451, 545,357, 616,618, 800,800
-classes=80
-num=16
-#jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-resize=1.5
-scale_x_y = 1.05
-##iou_thresh=0.213
-#cls_normalizer=1.0
-#iou_normalizer=0.07
-
-jitter=.1
-#objectness_smooth=1
-##iou_thresh=0.2
-iou_normalizer=0.05
-cls_normalizer=0.5
-obj_normalizer=4.0
-
-iou_loss=ciou
-nms_kind=greedynms
-beta_nms=0.6
-
-#counters_per_class = 26575, 7113, 44338, 8730, 5154, 6100, 4550, 10038, 10832, 13016, 1883, 1974, 1287, 9787, 10883, 4776, 5507, 6562, 9724, 8171, 5491, 1312, 5296, 5193, 8707, 11531, 12415, 6470, 6214, 2688, 6605, 2665, 6393, 9159, 3300, 3756, 5508, 6143, 4854, 24516, 7914, 20710, 5466, 7810, 6168, 14398, 9603, 5941,  4403, 6561, 7308, 7885, 2908, 5892, 7401, 6466, 38878, 5804, 8585, 4160, 15789,  4157, 5836, 4964, 2273, 5746, 2891, 6411, 1641, 3335, 223, 5624, 2658, 25244, 6320, 6539, 1464, 4775, 198, 1937
-#max_delta=3
-
-[route]
-layers = 300 ###
-
-[convolutional]
-batch_normalize=1
-size=1
-stride=1
-pad=1
-filters=640
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=1
-stride=1
-pad=1
-filters=640
-activation=logistic
-
-[sam]
-from=-2
-
-[convolutional]
-batch_normalize=1
-size=1
-stride=1
-pad=1
-filters=640
-activation=mish
-
-[convolutional]
-size=1
-stride=1
-pad=1
-#filters=340
-filters=255
-activation=linear
-
-[yolo]
-mask = 1,2,3
-anchors = 13,17,  31,25,   24,51, 61,45    61,45,  48,102,  119,96, 97,189,    97,189,  217,184,  171,384, 324,451,    324,451, 545,357, 616,618, 800,800
-classes=80
-num=16
-#jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-resize=1.5
-scale_x_y = 1.05
-##iou_thresh=0.213
-#cls_normalizer=1.0
-#iou_normalizer=0.07
-
-jitter=.1
-#objectness_smooth=1
-#iou_thresh=0.2
-iou_normalizer=0.05
-cls_normalizer=0.5
-obj_normalizer=1.0
-
-iou_loss=ciou
-nms_kind=greedynms
-beta_nms=0.6
-
-#counters_per_class = 26575, 7113, 44338, 8730, 5154, 6100, 4550, 10038, 10832, 13016, 1883, 1974, 1287, 9787, 10883, 4776, 5507, 6562, 9724, 8171, 5491, 1312, 5296, 5193, 8707, 11531, 12415, 6470, 6214, 2688, 6605, 2665, 6393, 9159, 3300, 3756, 5508, 6143, 4854, 24516, 7914, 20710, 5466, 7810, 6168, 14398, 9603, 5941,  4403, 6561, 7308, 7885, 2908, 5892, 7401, 6466, 38878, 5804, 8585, 4160, 15789,  4157, 5836, 4964, 2273, 5746, 2891, 6411, 1641, 3335, 223, 5624, 2658, 25244, 6320, 6539, 1464, 4775, 198, 1937
-#max_delta=3
-
-[route]
-layers = 313 ###
-
-[convolutional]
-batch_normalize=1
-size=1
-stride=1
-pad=1
-filters=1280
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=1
-stride=1
-pad=1
-filters=1280
-activation=logistic
-
-[sam]
-from=-2
-
-[convolutional]
-batch_normalize=1
-size=1
-stride=1
-pad=1
-filters=1280
-activation=mish
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=340
-activation=linear
-
-[yolo]
-mask = 4,5,6,7
-anchors = 13,17,  31,25,   24,51, 61,45    61,45,  48,102,  119,96, 97,189,    97,189,  217,184,  171,384, 324,451,    324,451, 545,357, 616,618, 800,800
-classes=80
-num=16
-#jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-resize=1.5
-scale_x_y = 1.05
-##iou_thresh=0.213
-#cls_normalizer=1.0
-#iou_normalizer=0.07
-
-jitter=.1
-objectness_smooth=1
-#iou_thresh=0.2
-iou_normalizer=0.05
-cls_normalizer=0.5
-obj_normalizer=0.5
-
-iou_loss=ciou
-nms_kind=greedynms
-beta_nms=0.6
-
-counters_per_class = 26575, 7113, 44338, 8730, 5154, 6100, 4550, 10038, 10832, 13016, 1883, 1974, 1287, 9787, 10883, 4776, 5507, 6562, 9724, 8171, 5491, 1312, 5296, 5193, 8707, 11531, 12415, 6470, 6214, 2688, 6605, 2665, 6393, 9159, 3300, 3756, 5508, 6143, 4854, 24516, 7914, 20710, 5466, 7810, 6168, 14398, 9603, 5941,  4403, 6561, 7308, 7885, 2908, 5892, 7401, 6466, 38878, 5804, 8585, 4160, 15789,  4157, 5836, 4964, 2273, 5746, 2891, 6411, 1641, 3335, 223, 5624, 2658, 25244, 6320, 6539, 1464, 4775, 198, 1937
-max_delta=3
-
-[route]
-layers = 326 ###
-
-[convolutional]
-batch_normalize=1
-size=1
-stride=1
-pad=1
-filters=1280
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=1
-stride=1
-pad=1
-filters=1280
-activation=logistic
-
-[sam]
-from=-2
-
-[convolutional]
-batch_normalize=1
-size=1
-stride=1
-pad=1
-filters=1280
-activation=mish
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=340
-activation=linear
-
-[yolo]
-mask = 8,9,10,11
-anchors = 13,17,  31,25,   24,51, 61,45    61,45,  48,102,  119,96, 97,189,    97,189,  217,184,  171,384, 324,451,    324,451, 545,357, 616,618, 800,800
-classes=80
-num=16
-#jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-resize=1.5
-scale_x_y = 1.05
-##iou_thresh=0.213
-#cls_normalizer=1.0
-#iou_normalizer=0.07
-
-jitter=.1
-objectness_smooth=1
-#iou_thresh=0.2
-iou_normalizer=0.05
-cls_normalizer=0.5
-obj_normalizer=0.4
-
-iou_loss=ciou
-nms_kind=greedynms
-beta_nms=0.6
-
-counters_per_class = 26575, 7113, 44338, 8730, 5154, 6100, 4550, 10038, 10832, 13016, 1883, 1974, 1287, 9787, 10883, 4776, 5507, 6562, 9724, 8171, 5491, 1312, 5296, 5193, 8707, 11531, 12415, 6470, 6214, 2688, 6605, 2665, 6393, 9159, 3300, 3756, 5508, 6143, 4854, 24516, 7914, 20710, 5466, 7810, 6168, 14398, 9603, 5941,  4403, 6561, 7308, 7885, 2908, 5892, 7401, 6466, 38878, 5804, 8585, 4160, 15789,  4157, 5836, 4964, 2273, 5746, 2891, 6411, 1641, 3335, 223, 5624, 2658, 25244, 6320, 6539, 1464, 4775, 198, 1937
-max_delta=3
-
-[route]
-layers = 339 ###
-
-[convolutional]
-batch_normalize=1
-size=1
-stride=1
-pad=1
-filters=1280
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=1
-stride=1
-pad=1
-filters=1280
-activation=logistic
-
-[sam]
-from=-2
-
-[convolutional]
-batch_normalize=1
-size=1
-stride=1
-pad=1
-filters=1280
-activation=mish
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=340
-activation=linear
-
-[yolo]
-mask = 12,13,14,15
-anchors = 13,17,  31,25,   24,51, 61,45    61,45,  48,102,  119,96, 97,189,    97,189,  217,184,  171,384, 324,451,    324,451, 545,357, 616,618, 800,800
-classes=80
-num=16
-#jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-resize=1.5
-scale_x_y = 1.05
-##iou_thresh=0.213
-#cls_normalizer=1.0
-#iou_normalizer=0.07
-
-jitter=.1
-objectness_smooth=1
-#iou_thresh=0.2
-iou_normalizer=0.05
-cls_normalizer=0.5
-obj_normalizer=0.1
-
-iou_loss=ciou
-nms_kind=greedynms
-beta_nms=0.6
-
-#counters_per_class = 26575, 7113, 44338, 8730, 5154, 6100, 4550, 10038, 10832, 13016, 1883, 1974, 1287, 9787, 10883, 4776, 5507, 6562, 9724, 8171, 5491, 1312, 5296, 5193, 8707, 11531, 12415, 6470, 6214, 2688, 6605, 2665, 6393, 9159, 3300, 3756, 5508, 6143, 4854, 24516, 7914, 20710, 5466, 7810, 6168, 14398, 9603, 5941,  4403, 6561, 7308, 7885, 2908, 5892, 7401, 6466, 38878, 5804, 8585, 4160, 15789,  4157, 5836, 4964, 2273, 5746, 2891, 6411, 1641, 3335, 223, 5624, 2658, 25244, 6320, 6539, 1464, 4775, 198, 1937
-max_delta=3
-
-### End of YOLO ###
\ No newline at end of file
diff --git a/build/darknet/x64/cfg/csresnext50-panet-spp-original-optimal.cfg b/build/darknet/x64/cfg/csresnext50-panet-spp-original-optimal.cfg
deleted file mode 100644
index f0b78f973ef..00000000000
--- a/build/darknet/x64/cfg/csresnext50-panet-spp-original-optimal.cfg
+++ /dev/null
@@ -1,1042 +0,0 @@
-[net]
-# Testing
-#batch=1
-#subdivisions=1
-# Training
-batch=64
-subdivisions=8
-width=608
-height=608
-channels=3
-momentum=0.949
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-learning_rate=0.00261
-burn_in=1000
-max_batches = 500500
-policy=steps
-steps=400000,450000
-scales=.1,.1
-
-#cutmix=1
-mosaic=1
-
-#19:104x104 38:52x52 65:26x26 80:13x13 for 416
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=7
-stride=2
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-# 1-1
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-groups=32
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-# 1-2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-groups=32
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-# 1-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-groups=32
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-# 1-T
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers = -1,-16
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-groups=32
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=linear
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=linear
-
-# 2-1
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-groups=32
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-# 2-2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-groups=32
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-# 2-3
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-groups=32
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-# 2-T
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers = -1,-16
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-groups=32
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=linear
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=linear
-
-# 3-1
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-groups=32
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-# 3-2
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-groups=32
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-# 3-3
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-groups=32
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-# 3-4
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-groups=32
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-# 3-5
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-groups=32
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-# 3-T
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers = -1,-24
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-groups=32
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=leaky
-
-# 4-1
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-groups=32
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-# 4-2
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-groups=32
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-# 4-T
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers = -1,-12
-
-[convolutional]
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=leaky
-
-##########################
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-### SPP ###
-[maxpool]
-stride=1
-size=5
-
-[route]
-layers=-2
-
-[maxpool]
-stride=1
-size=9
-
-[route]
-layers=-4
-
-[maxpool]
-stride=1
-size=13
-
-[route]
-layers=-1,-3,-5,-6
-### End SPP ###
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[upsample]
-stride=2
-
-[route]
-layers = 65
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers = -1, -3
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[upsample]
-stride=2
-
-[route]
-layers = 38
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers = -1, -3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-##########################
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=linear
-
-
-[yolo]
-mask = 0,1,2
-anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
-classes=80
-num=9
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-scale_x_y = 1.2
-iou_thresh=0.213
-cls_normalizer=1.0
-iou_normalizer=0.07
-iou_loss=ciou
-nms_kind=greedynms
-beta_nms=0.6
-
-[route]
-layers = -4
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=2
-pad=1
-filters=256
-activation=leaky
-
-[route]
-layers = -1, -16
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=linear
-
-
-[yolo]
-mask = 3,4,5
-anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
-classes=80
-num=9
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-scale_x_y = 1.1
-iou_thresh=0.213
-cls_normalizer=1.0
-iou_normalizer=0.07
-iou_loss=ciou
-nms_kind=greedynms
-beta_nms=0.6
-
-
-[route]
-layers = -4
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=2
-pad=1
-filters=512
-activation=leaky
-
-[route]
-layers = -1, -37
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=linear
-
-
-[yolo]
-mask = 6,7,8
-anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
-classes=80
-num=9
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-random=1
-scale_x_y = 1.05
-iou_thresh=0.213
-cls_normalizer=1.0
-iou_normalizer=0.07
-iou_loss=ciou
-nms_kind=greedynms
-beta_nms=0.6
-
diff --git a/build/darknet/x64/cfg/csresnext50-panet-spp.cfg b/build/darknet/x64/cfg/csresnext50-panet-spp.cfg
deleted file mode 100644
index 261d35b96d7..00000000000
--- a/build/darknet/x64/cfg/csresnext50-panet-spp.cfg
+++ /dev/null
@@ -1,1018 +0,0 @@
-[net]
-# Testing
-#batch=1
-#subdivisions=1
-# Training
-batch=64
-subdivisions=16
-width=512
-height=512
-channels=3
-momentum=0.9
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-learning_rate=0.001
-burn_in=1000
-max_batches = 500500
-policy=steps
-steps=400000,450000
-scales=.1,.1
-
-#19:104x104 38:52x52 65:26x26 80:13x13 for 416
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=7
-stride=2
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-# 1-1
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-groups=32
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-# 1-2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-groups=32
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-# 1-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-groups=32
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-# 1-T
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers = -1,-16
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-groups=32
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=linear
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=linear
-
-# 2-1
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-groups=32
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-# 2-2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-groups=32
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-# 2-3
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-groups=32
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-# 2-T
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers = -1,-16
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-groups=32
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=linear
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=linear
-
-# 3-1
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-groups=32
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-# 3-2
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-groups=32
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-# 3-3
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-groups=32
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-# 3-4
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-groups=32
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-# 3-5
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-groups=32
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-# 3-T
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers = -1,-24
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-groups=32
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=leaky
-
-# 4-1
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-groups=32
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-# 4-2
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-groups=32
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-# 4-T
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers = -1,-12
-
-[convolutional]
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=leaky
-
-##########################
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-### SPP ###
-[maxpool]
-stride=1
-size=5
-
-[route]
-layers=-2
-
-[maxpool]
-stride=1
-size=9
-
-[route]
-layers=-4
-
-[maxpool]
-stride=1
-size=13
-
-[route]
-layers=-1,-3,-5,-6
-### End SPP ###
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[upsample]
-stride=2
-
-[route]
-layers = 65
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers = -1, -3
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[upsample]
-stride=2
-
-[route]
-layers = 38
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers = -1, -3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-##########################
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=linear
-
-
-[yolo]
-mask = 0,1,2
-anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
-classes=80
-num=9
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-random=1
-
-[route]
-layers = -4
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=2
-pad=1
-filters=256
-activation=leaky
-
-[route]
-layers = -1, -16
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=linear
-
-
-[yolo]
-mask = 3,4,5
-anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
-classes=80
-num=9
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-random=1
-
-[route]
-layers = -4
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=2
-pad=1
-filters=512
-activation=leaky
-
-[route]
-layers = -1, -37
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=linear
-
-
-[yolo]
-mask = 6,7,8
-anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
-classes=80
-num=9
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-random=1
diff --git a/build/darknet/x64/cfg/darknet.cfg b/build/darknet/x64/cfg/darknet.cfg
deleted file mode 100644
index 60b939a38ff..00000000000
--- a/build/darknet/x64/cfg/darknet.cfg
+++ /dev/null
@@ -1,111 +0,0 @@
-[net]
-batch=128
-subdivisions=1
-height=224
-width=224
-channels=3
-momentum=0.9
-decay=0.0005
-max_crop=320
-
-learning_rate=0.1
-policy=poly
-power=4
-max_batches=1600000
-
-[convolutional]
-batch_normalize=1
-filters=16
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-padding=1
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-filters=1000
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[avgpool]
-
-[softmax]
-groups=1
-
-[cost]
-type=sse
-
diff --git a/build/darknet/x64/cfg/darknet19.cfg b/build/darknet/x64/cfg/darknet19.cfg
deleted file mode 100644
index bf73fb7b48a..00000000000
--- a/build/darknet/x64/cfg/darknet19.cfg
+++ /dev/null
@@ -1,194 +0,0 @@
-[net]
-batch=128
-subdivisions=1
-height=224
-width=224
-channels=3
-momentum=0.9
-decay=0.0005
-max_crop=448
-
-learning_rate=0.1
-policy=poly
-power=4
-max_batches=1600000
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-filters=1000
-size=1
-stride=1
-pad=1
-activation=linear
-
-[avgpool]
-
-[softmax]
-groups=1
-
-[cost]
-type=sse
-
diff --git a/build/darknet/x64/cfg/darknet19_448.cfg b/build/darknet/x64/cfg/darknet19_448.cfg
deleted file mode 100644
index 1918a8b2d62..00000000000
--- a/build/darknet/x64/cfg/darknet19_448.cfg
+++ /dev/null
@@ -1,202 +0,0 @@
-[net]
-#batch=128
-#subdivisions=4
-batch=1
-subdivisions=1
-height=448
-width=448
-max_crop=512
-channels=3
-momentum=0.9
-decay=0.0005
-
-learning_rate=0.001
-policy=poly
-power=4
-max_batches=100000
-
-angle=7
-hue = .1
-saturation=.75
-exposure=.75
-aspect=.75
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-filters=1000
-size=1
-stride=1
-pad=1
-activation=linear
-
-[avgpool]
-
-[softmax]
-groups=1
-
-[cost]
-type=sse
-
diff --git a/build/darknet/x64/cfg/darknet53.cfg b/build/darknet/x64/cfg/darknet53.cfg
deleted file mode 100644
index a1f084a943f..00000000000
--- a/build/darknet/x64/cfg/darknet53.cfg
+++ /dev/null
@@ -1,566 +0,0 @@
-[net]
-# Training
-batch=128
-subdivisions=8
-
-# Testing
-#batch=1
-#subdivisions=1
-
-height=256
-width=256
-channels=3
-min_crop=128
-max_crop=448
-
-burn_in=1000
-learning_rate=0.1
-policy=poly
-power=4
-max_batches=800000
-momentum=0.9
-decay=0.0005
-
-angle=7
-hue=.1
-saturation=.75
-exposure=.75
-aspect=.75
-
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[avgpool]
-
-[convolutional]
-filters=1000
-size=1
-stride=1
-pad=1
-activation=linear
-
-[softmax]
-groups=1
-
diff --git a/build/darknet/x64/cfg/darknet53_448_xnor.cfg b/build/darknet/x64/cfg/darknet53_448_xnor.cfg
deleted file mode 100644
index a48ef698a47..00000000000
--- a/build/darknet/x64/cfg/darknet53_448_xnor.cfg
+++ /dev/null
@@ -1,619 +0,0 @@
-[net]
-# Training - start training with darknet53.weights
-batch=120
-subdivisions=20
-
-# Testing
-#batch=1
-#subdivisions=1
-
-height=448
-width=448
-channels=3
-min_crop=448
-max_crop=512
-
-burn_in=1000
-learning_rate=0.1
-policy=poly
-power=4
-max_batches=100000
-momentum=0.9
-decay=0.0005
-
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-# Downsample
-
-[convolutional]
-xnor=1
-batch_normalize=1
-filters=64
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-xnor=1
-batch_normalize=1
-filters=32
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-xnor=1
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-# Downsample
-
-[convolutional]
-xnor=1
-batch_normalize=1
-filters=128
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-xnor=1
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-xnor=1
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-xnor=1
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-xnor=1
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-# Downsample
-
-[convolutional]
-xnor=1
-batch_normalize=1
-filters=256
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-xnor=1
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-xnor=1
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-xnor=1
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-xnor=1
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-xnor=1
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-xnor=1
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-xnor=1
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-xnor=1
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-xnor=1
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-xnor=1
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-xnor=1
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-xnor=1
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-xnor=1
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-xnor=1
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-xnor=1
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-xnor=1
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-# Downsample
-
-[convolutional]
-xnor=1
-batch_normalize=1
-filters=512
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-xnor=1
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-xnor=1
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-xnor=1
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-xnor=1
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-xnor=1
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-xnor=1
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-xnor=1
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-xnor=1
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-xnor=1
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-xnor=1
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-xnor=1
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-xnor=1
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-xnor=1
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-xnor=1
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-xnor=1
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-xnor=1
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-# Downsample
-
-[convolutional]
-xnor=1
-batch_normalize=1
-filters=1024
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-xnor=1
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-xnor=1
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-xnor=1
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-xnor=1
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-xnor=1
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-xnor=1
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-xnor=1
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-xnor=1
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[avgpool]
-
-[convolutional]
-filters=1000
-size=1
-stride=1
-pad=1
-activation=linear
-
-[softmax]
-groups=1
-
diff --git a/build/darknet/x64/cfg/densenet201.cfg b/build/darknet/x64/cfg/densenet201.cfg
deleted file mode 100644
index 5e1e7dd1fda..00000000000
--- a/build/darknet/x64/cfg/densenet201.cfg
+++ /dev/null
@@ -1,1954 +0,0 @@
-[net]
-# Training
-# batch=128
-# subdivisions=4
-
-# Testing
-batch=1
-subdivisions=1
-
-height=256
-width=256
-max_crop=448
-channels=3
-momentum=0.9
-decay=0.0005
-
-burn_in=1000
-learning_rate=0.1
-policy=poly
-power=4
-max_batches=1600000
-
-angle=7
-hue=.1
-saturation=.75
-exposure=.75
-aspect=.75
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=7
-stride=2
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
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-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-
-[convolutional]
-filters=1000
-size=1
-stride=1
-pad=1
-activation=linear
-
-[avgpool]
-
-[softmax]
-groups=1
-
-[cost]
-type=sse
-
diff --git a/build/darknet/x64/cfg/efficientnet-lite3.cfg b/build/darknet/x64/cfg/efficientnet-lite3.cfg
deleted file mode 100644
index e76bbe3085d..00000000000
--- a/build/darknet/x64/cfg/efficientnet-lite3.cfg
+++ /dev/null
@@ -1,1009 +0,0 @@
-# https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/lite/efficientnet_lite_builder.py
-# (width_coefficient, depth_coefficient, resolution, dropout_rate)
-# 'efficientnet-lite3': (1.2, 1.4, 280, 0.3),
-#
-#_DEFAULT_BLOCKS_ARGS = [
-#    'r1_k3_s11_e1_i32_o16_se0.25', 'r2_k3_s22_e6_i16_o24_se0.25',
-#    'r2_k5_s22_e6_i24_o40_se0.25', 'r3_k3_s22_e6_i40_o80_se0.25',
-#    'r3_k5_s11_e6_i80_o112_se0.25', 'r4_k5_s22_e6_i112_o192_se0.25',
-#    'r1_k3_s11_e6_i192_o320_se0.25',
-#]
-
-[net]
-# Training
-batch=120
-subdivisions=6
-height=288
-width=288
-channels=3
-momentum=0.9
-decay=0.0005
-max_crop=320
-
-cutmix=1
-mosaic=1
-label_smooth_eps=0.1
-
-burn_in=1000
-learning_rate=0.256
-policy=step
-step=10000
-scale=0.96
-max_batches=1600000
-momentum=0.9
-decay=0.00005
-
-angle=7
-hue=.1
-saturation=.75
-exposure=.75
-aspect=.75
-
-
-### CONV1 - 1 (1)
-# conv1
-[convolutional]
-filters=40	#32
-size=3
-pad=1
-stride=2
-batch_normalize=1
-activation=relu6
-
-
-### CONV2 - MBConv1 - 1 (2)
-# conv2_1_expand
-[convolutional]
-filters=40	#32
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=relu6
-
-# conv2_1_dwise
-[convolutional]
-groups=40	#32
-filters=40	#32
-size=3
-stride=1
-pad=1
-batch_normalize=1
-activation=relu6
-
-# conv2_1_linear
-[convolutional]
-filters=16	#16
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=linear
-
-
-### CONV2 - MBConv1 - 2 (2)
-# conv2_1_expand
-[convolutional]
-filters=40	#32
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=relu6
-
-# conv2_1_dwise
-[convolutional]
-groups=40	#32
-filters=40	#32
-size=3
-stride=1
-pad=1
-batch_normalize=1
-activation=relu6
-
-# conv2_1_linear
-[convolutional]
-filters=16	#16
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=linear
-
-
-### CONV3 - MBConv6 - 1 (3)
-# dropout only before residual connection
-[dropout]
-probability=.3
-
-# block_3_1
-[shortcut]
-from=-5
-activation=linear
-
-# conv2_2_expand
-[convolutional]
-filters=112	#96
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=relu6
-
-# conv2_2_dwise
-[convolutional]
-groups=112	#96
-filters=112	#96
-size=3
-pad=1
-stride=2
-batch_normalize=1
-activation=relu6
-
-# conv2_2_linear
-[convolutional]
-filters=32	#24
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=linear
-
-
-### CONV3 - MBConv6 - 2 (3)
-# conv3_1_expand
-[convolutional]
-filters=176	#144
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=relu6
-
-# conv3_1_dwise
-[convolutional]
-groups=176	#144
-filters=176	#144
-size=3
-stride=1
-pad=1
-batch_normalize=1
-activation=relu6
-
-# conv3_1_linear
-[convolutional]
-filters=32	#24
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=linear
-
-
-### CONV3 - MBConv6 - 3 (3)
-# dropout only before residual connection
-[dropout]
-probability=.3
-
-# block_3_1
-[shortcut]
-from=-5
-activation=linear
-
-# conv3_1_expand
-[convolutional]
-filters=176	#144
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=relu6
-
-# conv3_1_dwise
-[convolutional]
-groups=176	#144
-filters=176	#144
-size=3
-stride=1
-pad=1
-batch_normalize=1
-activation=relu6
-
-# conv3_1_linear
-[convolutional]
-filters=32	#24
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=linear
-
-
-
-### CONV4 - MBConv6 - 1 (3)
-# dropout only before residual connection
-[dropout]
-probability=.3
-
-# block_3_1
-[shortcut]
-from=-5
-activation=linear
-
-# conv_3_2_expand
-[convolutional]
-filters=176	#144
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=relu6
-
-# conv_3_2_dwise
-[convolutional]
-groups=176	#144
-filters=176	#144
-size=5
-pad=1
-stride=2
-batch_normalize=1
-activation=relu6
-
-# conv_3_2_linear
-[convolutional]
-filters=48	#40
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=linear
-
-
-### CONV4 - MBConv6 - 2 (3)
-# conv_4_1_expand
-[convolutional]
-filters=232	#192
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=relu6
-
-# conv_4_1_dwise
-[convolutional]
-groups=232	#192
-filters=232	#192
-size=5
-stride=1
-pad=1
-batch_normalize=1
-activation=relu6
-
-# conv_4_1_linear
-[convolutional]
-filters=48	#40
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=linear
-
-
-### CONV4 - MBConv6 - 3 (3)
-# dropout only before residual connection
-[dropout]
-probability=.3
-
-# block_4_2
-[shortcut]
-from=-5
-activation=linear
-
-# conv_4_1_expand
-[convolutional]
-filters=232	#192
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=relu6
-
-# conv_4_1_dwise
-[convolutional]
-groups=232	#192
-filters=232	#192
-size=5
-stride=1
-pad=1
-batch_normalize=1
-activation=relu6
-
-# conv_4_1_linear
-[convolutional]
-filters=48	#40
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=linear
-
-
-
-
-### CONV5 - MBConv6 - 1 (5)
-# dropout only before residual connection
-[dropout]
-probability=.3
-
-# block_4_2
-[shortcut]
-from=-5
-activation=linear
-
-# conv_4_3_expand
-[convolutional]
-filters=232	#192
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=relu6
-
-# conv_4_3_dwise
-[convolutional]
-groups=232	#192
-filters=232	#192
-size=3
-stride=1
-pad=1
-batch_normalize=1
-activation=relu6
-
-# conv_4_3_linear
-[convolutional]
-filters=96	#80
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=linear
-
-
-### CONV5 - MBConv6 - 2 (5)
-# conv_4_4_expand
-[convolutional]
-filters=464	#384
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=relu6
-
-# conv_4_4_dwise
-[convolutional]
-groups=464	#384
-filters=464	#384
-size=3
-stride=1
-pad=1
-batch_normalize=1
-activation=relu6
-
-# conv_4_4_linear
-[convolutional]
-filters=96	#80
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=linear
-
-
-### CONV5 - MBConv6 - 3 (5)
-# dropout only before residual connection
-[dropout]
-probability=.3
-
-# block_4_4
-[shortcut]
-from=-5
-activation=linear
-
-# conv_4_5_expand
-[convolutional]
-filters=464	#384
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=relu6
-
-# conv_4_5_dwise
-[convolutional]
-groups=464	#384
-filters=464	#384
-size=3
-stride=1
-pad=1
-batch_normalize=1
-activation=relu6
-
-# conv_4_5_linear
-[convolutional]
-filters=96	#80
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=linear
-
-
-### CONV5 - MBConv6 - 4 (5)
-# dropout only before residual connection
-[dropout]
-probability=.3
-
-# block_4_4
-[shortcut]
-from=-5
-activation=linear
-
-# conv_4_5_expand
-[convolutional]
-filters=464	#384
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=relu6
-
-# conv_4_5_dwise
-[convolutional]
-groups=464	#384
-filters=464	#384
-size=3
-stride=1
-pad=1
-batch_normalize=1
-activation=relu6
-
-# conv_4_5_linear
-[convolutional]
-filters=96	#80
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=linear
-
-
-
-### CONV5 - MBConv6 - 5 (5)
-# dropout only before residual connection
-[dropout]
-probability=.3
-
-# block_4_4
-[shortcut]
-from=-5
-activation=linear
-
-# conv_4_5_expand
-[convolutional]
-filters=464	#384
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=relu6
-
-# conv_4_5_dwise
-[convolutional]
-groups=464	#384
-filters=464	#384
-size=3
-stride=1
-pad=1
-batch_normalize=1
-activation=relu6
-
-# conv_4_5_linear
-[convolutional]
-filters=96	#80
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=linear
-
-
-
-### CONV6 - MBConv6 - 1 (5)
-# dropout only before residual connection
-[dropout]
-probability=.3
-
-# block_4_6
-[shortcut]
-from=-5
-activation=linear
-
-# conv_4_7_expand
-[convolutional]
-filters=464	#384
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=relu6
-
-# conv_4_7_dwise
-[convolutional]
-groups=464	#384
-filters=464	#384
-size=5
-pad=1
-stride=2
-batch_normalize=1
-activation=relu6
-
-# conv_4_7_linear
-[convolutional]
-filters=136	#112
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=linear
-
-
-### CONV6 - MBConv6 - 2 (5)
-# conv_5_1_expand
-[convolutional]
-filters=688	#576
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=relu6
-
-# conv_5_1_dwise
-[convolutional]
-groups=688	#576
-filters=688	#576
-size=5
-stride=1
-pad=1
-batch_normalize=1
-activation=relu6
-
-# conv_5_1_linear
-[convolutional]
-filters=136	#112
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=linear
-
-
-### CONV6 - MBConv6 - 3 (5)
-# dropout only before residual connection
-[dropout]
-probability=.3
-
-# block_5_1
-[shortcut]
-from=-5
-activation=linear
-
-# conv_5_2_expand
-[convolutional]
-filters=688	#576
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=relu6
-
-# conv_5_2_dwise
-[convolutional]
-groups=688	#576
-filters=688	#576
-size=5
-stride=1
-pad=1
-batch_normalize=1
-activation=relu6
-
-# conv_5_2_linear
-[convolutional]
-filters=136	#112
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=linear
-
-
-
-### CONV6 - MBConv6 - 4 (5)
-# dropout only before residual connection
-[dropout]
-probability=.3
-
-# block_5_1
-[shortcut]
-from=-5
-activation=linear
-
-# conv_5_2_expand
-[convolutional]
-filters=688	#576
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=relu6
-
-# conv_5_2_dwise
-[convolutional]
-groups=688	#576
-filters=688	#576
-size=5
-stride=1
-pad=1
-batch_normalize=1
-activation=relu6
-
-# conv_5_2_linear
-[convolutional]
-filters=136	#112
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=linear
-
-
-### CONV6 - MBConv6 - 5 (5)
-# dropout only before residual connection
-[dropout]
-probability=.3
-
-# block_5_1
-[shortcut]
-from=-5
-activation=linear
-
-# conv_5_2_expand
-[convolutional]
-filters=688	#576
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=relu6
-
-# conv_5_2_dwise
-[convolutional]
-groups=688	#576
-filters=688	#576
-size=5
-stride=1
-pad=1
-batch_normalize=1
-activation=relu6
-
-# conv_5_2_linear
-[convolutional]
-filters=136	#112
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=linear
-
-
-
-### CONV7 - MBConv6 - 1 (6)
-# dropout only before residual connection
-[dropout]
-probability=.3
-
-# block_5_2
-[shortcut]
-from=-5
-activation=linear
-
-# conv_5_3_expand
-[convolutional]
-filters=688	#576
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=relu6
-
-# conv_5_3_dwise
-[convolutional]
-groups=688	#576
-filters=688	#576
-size=5
-pad=1
-stride=2
-batch_normalize=1
-activation=relu6
-
-
-# conv_5_3_linear
-[convolutional]
-filters=232	#192
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=linear
-
-
-### CONV7 - MBConv6 - 2 (6)
-# conv_6_1_expand
-[convolutional]
-filters=1152	#960
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=relu6
-
-# conv_6_1_dwise
-[convolutional]
-groups=1152		#960
-filters=1152	#960
-size=5
-stride=1
-pad=1
-batch_normalize=1
-activation=relu6
-
-# conv_6_1_linear
-[convolutional]
-filters=232	#192
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=linear
-
-
-### CONV7 - MBConv6 - 3 (6)
-# dropout only before residual connection
-[dropout]
-probability=.3
-
-# block_6_1
-[shortcut]
-from=-5
-activation=linear
-
-# conv_6_2_expand
-[convolutional]
-filters=1152	#960
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=relu6
-
-# conv_6_2_dwise
-[convolutional]
-groups=1152	#960
-filters=1152	#960
-size=5
-stride=1
-pad=1
-batch_normalize=1
-activation=relu6
-
-# conv_6_2_linear
-[convolutional]
-filters=232	#192
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=linear
-
-
-### CONV7 - MBConv6 - 4 (6)
-# dropout only before residual connection
-[dropout]
-probability=.3
-
-# block_6_1
-[shortcut]
-from=-5
-activation=linear
-
-# conv_6_2_expand
-[convolutional]
-filters=1152	#960
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=relu6
-
-# conv_6_2_dwise
-[convolutional]
-groups=1152	#960
-filters=1152	#960
-size=5
-stride=1
-pad=1
-batch_normalize=1
-activation=relu6
-
-# conv_6_2_linear
-[convolutional]
-filters=232	#192
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=linear
-
-
-### CONV7 - MBConv6 - 5 (6)
-# dropout only before residual connection
-[dropout]
-probability=.3
-
-# block_6_1
-[shortcut]
-from=-5
-activation=linear
-
-# conv_6_2_expand
-[convolutional]
-filters=1152	#960
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=relu6
-
-# conv_6_2_dwise
-[convolutional]
-groups=1152	#960
-filters=1152	#960
-size=5
-stride=1
-pad=1
-batch_normalize=1
-activation=relu6
-
-# conv_6_2_linear
-[convolutional]
-filters=232	#192
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=linear
-
-
-### CONV7 - MBConv6 - 6 (6)
-# dropout only before residual connection
-[dropout]
-probability=.3
-
-# block_6_1
-[shortcut]
-from=-5
-activation=linear
-
-# conv_6_2_expand
-[convolutional]
-filters=1152	#960
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=relu6
-
-# conv_6_2_dwise
-[convolutional]
-groups=1152	#960
-filters=1152	#960
-size=5
-stride=1
-pad=1
-batch_normalize=1
-activation=relu6
-
-
-
-# conv_6_2_linear
-[convolutional]
-filters=232	#192
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=linear
-
-
-
-### CONV8 - MBConv6 - 1 (1)
-# dropout only before residual connection
-[dropout]
-probability=.3
-
-# block_6_2
-[shortcut]
-from=-5
-activation=linear
-
-# conv_6_3_expand
-[convolutional]
-filters=1152	#960
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=relu6
-
-# conv_6_3_dwise
-[convolutional]
-groups=1152	#960
-filters=1152	#960
-size=3
-stride=1
-pad=1
-batch_normalize=1
-activation=relu6
-
-
-
-# conv_6_3_linear
-[convolutional]
-filters=384	#320
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=linear
-
-
-
-
-### CONV9 - Conv2d 1x1
-# conv_6_4
-[convolutional]
-filters=1536	#1280
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=relu6
-
-
-[avgpool]
-
-[dropout]
-probability=.3
-
-[convolutional]
-filters=1000
-size=1
-stride=1
-pad=0
-activation=linear
-
-[softmax]
-groups=1
-
-#[cost]
-#type=sse
-
diff --git a/build/darknet/x64/cfg/efficientnet_b0.cfg b/build/darknet/x64/cfg/efficientnet_b0.cfg
deleted file mode 100644
index 70f780692a6..00000000000
--- a/build/darknet/x64/cfg/efficientnet_b0.cfg
+++ /dev/null
@@ -1,1009 +0,0 @@
-[net]
-# Training
-batch=120
-subdivisions=4
-# Testing
-#batch=1
-#subdivisions=1
-height=224
-width=224
-channels=3
-momentum=0.9
-decay=0.0005
-max_crop=256
-#mixup=4
-blur=1
-cutmix=1
-mosaic=1
-
-burn_in=1000
-#burn_in=100
-learning_rate=0.256
-policy=poly
-power=4
-max_batches=800000
-momentum=0.9
-decay=0.00005
-
-angle=7
-hue=.1
-saturation=.75
-exposure=.75
-aspect=.75
-
-
-### CONV1 - 1 (1)
-# conv1
-[convolutional]
-filters=32
-size=3
-pad=1
-stride=2
-batch_normalize=1
-activation=swish
-
-
-### CONV2 - MBConv1 - 1 (1)
-# conv2_1_expand
-[convolutional]
-filters=32
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=swish
-
-# conv2_1_dwise
-[convolutional]
-groups=32
-filters=32
-size=3
-stride=1
-pad=1
-batch_normalize=1
-activation=swish
-
-
-#squeeze-n-excitation
-[avgpool]
-
-# squeeze ratio r=4 (recommended r=16)
-[convolutional]
-filters=8
-size=1
-stride=1
-activation=swish
-
-# excitation
-[convolutional]
-filters=32
-size=1
-stride=1
-activation=logistic
-
-# multiply channels
-[scale_channels]
-from=-4
-
-
-# conv2_1_linear
-[convolutional]
-filters=16
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=linear
-
-
-
-### CONV3 - MBConv6 - 1 (2)
-# conv2_2_expand
-[convolutional]
-filters=96
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=swish
-
-# conv2_2_dwise
-[convolutional]
-groups=96
-filters=96
-size=3
-pad=1
-stride=2
-batch_normalize=1
-activation=swish
-
-
-#squeeze-n-excitation
-[avgpool]
-
-# squeeze ratio r=8 (recommended r=16)
-[convolutional]
-filters=16
-size=1
-stride=1
-activation=swish
-
-# excitation
-[convolutional]
-filters=96
-size=1
-stride=1
-activation=logistic
-
-# multiply channels
-[scale_channels]
-from=-4
-
-
-# conv2_2_linear
-[convolutional]
-filters=24
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=linear
-
-
-### CONV3 - MBConv6 - 2 (2)
-# conv3_1_expand
-[convolutional]
-filters=144
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=swish
-
-# conv3_1_dwise
-[convolutional]
-groups=144
-filters=144
-size=3
-stride=1
-pad=1
-batch_normalize=1
-activation=swish
-
-
-#squeeze-n-excitation
-[avgpool]
-
-# squeeze ratio r=16 (recommended r=16)
-[convolutional]
-filters=8
-size=1
-stride=1
-activation=swish
-
-# excitation
-[convolutional]
-filters=144
-size=1
-stride=1
-activation=logistic
-
-# multiply channels
-[scale_channels]
-from=-4
-
-
-# conv3_1_linear
-[convolutional]
-filters=24
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=linear
-
-
-
-### CONV4 - MBConv6 - 1 (2)
-# dropout only before residual connection
-[dropout]
-probability=.2
-
-# block_3_1
-[shortcut]
-from=-9
-activation=linear
-
-# conv_3_2_expand
-[convolutional]
-filters=144
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=swish
-
-# conv_3_2_dwise
-[convolutional]
-groups=144
-filters=144
-size=5
-pad=1
-stride=2
-batch_normalize=1
-activation=swish
-
-
-#squeeze-n-excitation
-[avgpool]
-
-# squeeze ratio r=16 (recommended r=16)
-[convolutional]
-filters=8
-size=1
-stride=1
-activation=swish
-
-# excitation
-[convolutional]
-filters=144
-size=1
-stride=1
-activation=logistic
-
-# multiply channels
-[scale_channels]
-from=-4
-
-
-# conv_3_2_linear
-[convolutional]
-filters=40
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=linear
-
-
-### CONV4 - MBConv6 - 2 (2)
-# conv_4_1_expand
-[convolutional]
-filters=192
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=swish
-
-# conv_4_1_dwise
-[convolutional]
-groups=192
-filters=192
-size=5
-stride=1
-pad=1
-batch_normalize=1
-activation=swish
-
-
-#squeeze-n-excitation
-[avgpool]
-
-# squeeze ratio r=16 (recommended r=16)
-[convolutional]
-filters=16
-size=1
-stride=1
-activation=swish
-
-# excitation
-[convolutional]
-filters=192
-size=1
-stride=1
-activation=logistic
-
-# multiply channels
-[scale_channels]
-from=-4
-
-
-# conv_4_1_linear
-[convolutional]
-filters=40
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=linear
-
-
-
-
-### CONV5 - MBConv6 - 1 (3)
-# dropout only before residual connection
-[dropout]
-probability=.2
-
-# block_4_2
-[shortcut]
-from=-9
-activation=linear
-
-# conv_4_3_expand
-[convolutional]
-filters=192
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=swish
-
-# conv_4_3_dwise
-[convolutional]
-groups=192
-filters=192
-size=3
-stride=1
-pad=1
-batch_normalize=1
-activation=swish
-
-
-#squeeze-n-excitation
-[avgpool]
-
-# squeeze ratio r=16 (recommended r=16)
-[convolutional]
-filters=16
-size=1
-stride=1
-activation=swish
-
-# excitation
-[convolutional]
-filters=192
-size=1
-stride=1
-activation=logistic
-
-# multiply channels
-[scale_channels]
-from=-4
-
-
-# conv_4_3_linear
-[convolutional]
-filters=80
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=linear
-
-
-### CONV5 - MBConv6 - 2 (3)
-# conv_4_4_expand
-[convolutional]
-filters=384
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=swish
-
-# conv_4_4_dwise
-[convolutional]
-groups=384
-filters=384
-size=3
-stride=1
-pad=1
-batch_normalize=1
-activation=swish
-
-
-#squeeze-n-excitation
-[avgpool]
-
-# squeeze ratio r=16 (recommended r=16)
-[convolutional]
-filters=24
-size=1
-stride=1
-activation=swish
-
-# excitation
-[convolutional]
-filters=384
-size=1
-stride=1
-activation=logistic
-
-# multiply channels
-[scale_channels]
-from=-4
-
-
-# conv_4_4_linear
-[convolutional]
-filters=80
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=linear
-
-
-### CONV5 - MBConv6 - 3 (3)
-# dropout only before residual connection
-[dropout]
-probability=.2
-
-# block_4_4
-[shortcut]
-from=-9
-activation=linear
-
-# conv_4_5_expand
-[convolutional]
-filters=384
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=swish
-
-# conv_4_5_dwise
-[convolutional]
-groups=384
-filters=384
-size=3
-stride=1
-pad=1
-batch_normalize=1
-activation=swish
-
-
-#squeeze-n-excitation
-[avgpool]
-
-# squeeze ratio r=16 (recommended r=16)
-[convolutional]
-filters=24
-size=1
-stride=1
-activation=swish
-
-# excitation
-[convolutional]
-filters=384
-size=1
-stride=1
-activation=logistic
-
-# multiply channels
-[scale_channels]
-from=-4
-
-
-# conv_4_5_linear
-[convolutional]
-filters=80
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=linear
-
-
-
-### CONV6 - MBConv6 - 1 (3)
-# dropout only before residual connection
-[dropout]
-probability=.2
-
-# block_4_6
-[shortcut]
-from=-9
-activation=linear
-
-# conv_4_7_expand
-[convolutional]
-filters=384
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=swish
-
-# conv_4_7_dwise
-[convolutional]
-groups=384
-filters=384
-size=5
-pad=1
-stride=2
-batch_normalize=1
-activation=swish
-
-
-#squeeze-n-excitation
-[avgpool]
-
-# squeeze ratio r=16 (recommended r=16)
-[convolutional]
-filters=24
-size=1
-stride=1
-activation=swish
-
-# excitation
-[convolutional]
-filters=384
-size=1
-stride=1
-activation=logistic
-
-# multiply channels
-[scale_channels]
-from=-4
-
-
-# conv_4_7_linear
-[convolutional]
-filters=112
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=linear
-
-
-### CONV6 - MBConv6 - 2 (3)
-# conv_5_1_expand
-[convolutional]
-filters=576
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=swish
-
-# conv_5_1_dwise
-[convolutional]
-groups=576
-filters=576
-size=5
-stride=1
-pad=1
-batch_normalize=1
-activation=swish
-
-
-#squeeze-n-excitation
-[avgpool]
-
-# squeeze ratio r=16 (recommended r=16)
-[convolutional]
-filters=32
-size=1
-stride=1
-activation=swish
-
-# excitation
-[convolutional]
-filters=576
-size=1
-stride=1
-activation=logistic
-
-# multiply channels
-[scale_channels]
-from=-4
-
-
-# conv_5_1_linear
-[convolutional]
-filters=112
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=linear
-
-
-### CONV6 - MBConv6 - 3 (3)
-# dropout only before residual connection
-[dropout]
-probability=.2
-
-# block_5_1
-[shortcut]
-from=-9
-activation=linear
-
-# conv_5_2_expand
-[convolutional]
-filters=576
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=swish
-
-# conv_5_2_dwise
-[convolutional]
-groups=576
-filters=576
-size=5
-stride=1
-pad=1
-batch_normalize=1
-activation=swish
-
-
-#squeeze-n-excitation
-[avgpool]
-
-# squeeze ratio r=16 (recommended r=16)
-[convolutional]
-filters=32
-size=1
-stride=1
-activation=swish
-
-# excitation
-[convolutional]
-filters=576
-size=1
-stride=1
-activation=logistic
-
-# multiply channels
-[scale_channels]
-from=-4
-
-
-# conv_5_2_linear
-[convolutional]
-filters=112
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=linear
-
-
-### CONV7 - MBConv6 - 1 (4)
-# dropout only before residual connection
-[dropout]
-probability=.2
-
-# block_5_2
-[shortcut]
-from=-9
-activation=linear
-
-# conv_5_3_expand
-[convolutional]
-filters=576
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=swish
-
-# conv_5_3_dwise
-[convolutional]
-groups=576
-filters=576
-size=5
-pad=1
-stride=2
-batch_normalize=1
-activation=swish
-
-
-#squeeze-n-excitation
-[avgpool]
-
-# squeeze ratio r=16 (recommended r=16)
-[convolutional]
-filters=32
-size=1
-stride=1
-activation=swish
-
-# excitation
-[convolutional]
-filters=576
-size=1
-stride=1
-activation=logistic
-
-# multiply channels
-[scale_channels]
-from=-4
-
-
-# conv_5_3_linear
-[convolutional]
-filters=192
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=linear
-
-
-### CONV7 - MBConv6 - 2 (4)
-# conv_6_1_expand
-[convolutional]
-filters=960
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=swish
-
-# conv_6_1_dwise
-[convolutional]
-groups=960
-filters=960
-size=5
-stride=1
-pad=1
-batch_normalize=1
-activation=swish
-
-
-#squeeze-n-excitation
-[avgpool]
-
-# squeeze ratio r=16 (recommended r=16)
-[convolutional]
-filters=64
-size=1
-stride=1
-activation=swish
-
-# excitation
-[convolutional]
-filters=960
-size=1
-stride=1
-activation=logistic
-
-# multiply channels
-[scale_channels]
-from=-4
-
-
-# conv_6_1_linear
-[convolutional]
-filters=192
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=linear
-
-
-### CONV7 - MBConv6 - 3 (4)
-# dropout only before residual connection
-[dropout]
-probability=.2
-
-# block_6_1
-[shortcut]
-from=-9
-activation=linear
-
-# conv_6_2_expand
-[convolutional]
-filters=960
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=swish
-
-# conv_6_2_dwise
-[convolutional]
-groups=960
-filters=960
-size=5
-stride=1
-pad=1
-batch_normalize=1
-activation=swish
-
-
-#squeeze-n-excitation
-[avgpool]
-
-# squeeze ratio r=16 (recommended r=16)
-[convolutional]
-filters=64
-size=1
-stride=1
-activation=swish
-
-# excitation
-[convolutional]
-filters=960
-size=1
-stride=1
-activation=logistic
-
-# multiply channels
-[scale_channels]
-from=-4
-
-
-# conv_6_2_linear
-[convolutional]
-filters=192
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=linear
-
-
-### CONV7 - MBConv6 - 4 (4)
-# dropout only before residual connection
-[dropout]
-probability=.2
-
-# block_6_1
-[shortcut]
-from=-9
-activation=linear
-
-# conv_6_2_expand
-[convolutional]
-filters=960
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=swish
-
-# conv_6_2_dwise
-[convolutional]
-groups=960
-filters=960
-size=5
-stride=1
-pad=1
-batch_normalize=1
-activation=swish
-
-
-#squeeze-n-excitation
-[avgpool]
-
-# squeeze ratio r=16 (recommended r=16)
-[convolutional]
-filters=64
-size=1
-stride=1
-activation=swish
-
-# excitation
-[convolutional]
-filters=960
-size=1
-stride=1
-activation=logistic
-
-# multiply channels
-[scale_channels]
-from=-4
-
-
-# conv_6_2_linear
-[convolutional]
-filters=192
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=linear
-
-
-
-### CONV8 - MBConv6 - 1 (1)
-# dropout only before residual connection
-[dropout]
-probability=.2
-
-# block_6_2
-[shortcut]
-from=-9
-activation=linear
-
-# conv_6_3_expand
-[convolutional]
-filters=960
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=swish
-
-# conv_6_3_dwise
-[convolutional]
-groups=960
-filters=960
-size=3
-stride=1
-pad=1
-batch_normalize=1
-activation=swish
-
-
-#squeeze-n-excitation
-[avgpool]
-
-# squeeze ratio r=16 (recommended r=16)
-[convolutional]
-filters=64
-size=1
-stride=1
-activation=swish
-
-# excitation
-[convolutional]
-filters=960
-size=1
-stride=1
-activation=logistic
-
-# multiply channels
-[scale_channels]
-from=-4
-
-
-# conv_6_3_linear
-[convolutional]
-filters=320
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=linear
-
-
-### CONV9 - Conv2d 1x1
-# conv_6_4
-[convolutional]
-filters=1280
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=swish
-
-
-[avgpool]
-
-[dropout]
-probability=.2
-
-[convolutional]
-filters=1000
-size=1
-stride=1
-pad=0
-activation=linear
-
-[softmax]
-groups=1
-
-#[cost]
-#type=sse
-
diff --git a/build/darknet/x64/cfg/enet-coco.cfg b/build/darknet/x64/cfg/enet-coco.cfg
deleted file mode 100644
index b530ed360b3..00000000000
--- a/build/darknet/x64/cfg/enet-coco.cfg
+++ /dev/null
@@ -1,1072 +0,0 @@
-[net]
-# Testing
-#batch=1
-#subdivisions=1
-# Training
-batch=64
-subdivisions=8
-width=416
-height=416
-channels=3
-momentum=0.9
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-learning_rate=0.001
-burn_in=1000
-max_batches = 500200
-policy=steps
-steps=400000,450000
-scales=.1,.1
-
-### CONV1 - 1 (1)
-# conv1
-[convolutional]
-filters=32
-size=3
-pad=1
-stride=2
-batch_normalize=1
-activation=swish
-
-
-### CONV2 - MBConv1 - 1 (1)
-# conv2_1_expand
-[convolutional]
-filters=32
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=swish
-
-# conv2_1_dwise
-[convolutional]
-groups=32
-filters=32
-size=3
-stride=1
-pad=1
-batch_normalize=1
-activation=swish
-
-
-#squeeze-n-excitation
-[avgpool]
-
-# squeeze ratio r=4 (recommended r=16)
-[convolutional]
-filters=8
-size=1
-stride=1
-activation=swish
-
-# excitation
-[convolutional]
-filters=32
-size=1
-stride=1
-activation=logistic
-
-# multiply channels
-[scale_channels]
-from=-4
-
-
-# conv2_1_linear
-[convolutional]
-filters=16
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=linear
-
-
-
-### CONV3 - MBConv6 - 1 (2)
-# conv2_2_expand
-[convolutional]
-filters=96
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=swish
-
-# conv2_2_dwise
-[convolutional]
-groups=96
-filters=96
-size=3
-pad=1
-stride=2
-batch_normalize=1
-activation=swish
-
-
-#squeeze-n-excitation
-[avgpool]
-
-# squeeze ratio r=8 (recommended r=16)
-[convolutional]
-filters=16
-size=1
-stride=1
-activation=swish
-
-# excitation
-[convolutional]
-filters=96
-size=1
-stride=1
-activation=logistic
-
-# multiply channels
-[scale_channels]
-from=-4
-
-
-# conv2_2_linear
-[convolutional]
-filters=24
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=linear
-
-
-### CONV3 - MBConv6 - 2 (2)
-# conv3_1_expand
-[convolutional]
-filters=144
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=swish
-
-# conv3_1_dwise
-[convolutional]
-groups=144
-filters=144
-size=3
-stride=1
-pad=1
-batch_normalize=1
-activation=swish
-
-
-#squeeze-n-excitation
-[avgpool]
-
-# squeeze ratio r=16 (recommended r=16)
-[convolutional]
-filters=8
-size=1
-stride=1
-activation=swish
-
-# excitation
-[convolutional]
-filters=144
-size=1
-stride=1
-activation=logistic
-
-# multiply channels
-[scale_channels]
-from=-4
-
-
-# conv3_1_linear
-[convolutional]
-filters=24
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=linear
-
-
-
-### CONV4 - MBConv6 - 1 (2)
-# dropout only before residual connection
-[dropout]
-probability=.0
-
-# block_3_1
-[shortcut]
-from=-9
-activation=linear
-
-# conv_3_2_expand
-[convolutional]
-filters=144
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=swish
-
-# conv_3_2_dwise
-[convolutional]
-groups=144
-filters=144
-size=5
-pad=1
-stride=2
-batch_normalize=1
-activation=swish
-
-
-#squeeze-n-excitation
-[avgpool]
-
-# squeeze ratio r=16 (recommended r=16)
-[convolutional]
-filters=8
-size=1
-stride=1
-activation=swish
-
-# excitation
-[convolutional]
-filters=144
-size=1
-stride=1
-activation=logistic
-
-# multiply channels
-[scale_channels]
-from=-4
-
-
-# conv_3_2_linear
-[convolutional]
-filters=40
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=linear
-
-
-### CONV4 - MBConv6 - 2 (2)
-# conv_4_1_expand
-[convolutional]
-filters=192
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=swish
-
-# conv_4_1_dwise
-[convolutional]
-groups=192
-filters=192
-size=5
-stride=1
-pad=1
-batch_normalize=1
-activation=swish
-
-
-#squeeze-n-excitation
-[avgpool]
-
-# squeeze ratio r=16 (recommended r=16)
-[convolutional]
-filters=16
-size=1
-stride=1
-activation=swish
-
-# excitation
-[convolutional]
-filters=192
-size=1
-stride=1
-activation=logistic
-
-# multiply channels
-[scale_channels]
-from=-4
-
-
-# conv_4_1_linear
-[convolutional]
-filters=40
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=linear
-
-
-
-
-### CONV5 - MBConv6 - 1 (3)
-# dropout only before residual connection
-[dropout]
-probability=.0
-
-# block_4_2
-[shortcut]
-from=-9
-activation=linear
-
-# conv_4_3_expand
-[convolutional]
-filters=192
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=swish
-
-# conv_4_3_dwise
-[convolutional]
-groups=192
-filters=192
-size=3
-stride=1
-pad=1
-batch_normalize=1
-activation=swish
-
-
-#squeeze-n-excitation
-[avgpool]
-
-# squeeze ratio r=16 (recommended r=16)
-[convolutional]
-filters=16
-size=1
-stride=1
-activation=swish
-
-# excitation
-[convolutional]
-filters=192
-size=1
-stride=1
-activation=logistic
-
-# multiply channels
-[scale_channels]
-from=-4
-
-
-# conv_4_3_linear
-[convolutional]
-filters=80
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=linear
-
-
-### CONV5 - MBConv6 - 2 (3)
-# conv_4_4_expand
-[convolutional]
-filters=384
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=swish
-
-# conv_4_4_dwise
-[convolutional]
-groups=384
-filters=384
-size=3
-stride=1
-pad=1
-batch_normalize=1
-activation=swish
-
-
-#squeeze-n-excitation
-[avgpool]
-
-# squeeze ratio r=16 (recommended r=16)
-[convolutional]
-filters=24
-size=1
-stride=1
-activation=swish
-
-# excitation
-[convolutional]
-filters=384
-size=1
-stride=1
-activation=logistic
-
-# multiply channels
-[scale_channels]
-from=-4
-
-
-# conv_4_4_linear
-[convolutional]
-filters=80
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=linear
-
-
-### CONV5 - MBConv6 - 3 (3)
-# dropout only before residual connection
-[dropout]
-probability=.0
-
-# block_4_4
-[shortcut]
-from=-9
-activation=linear
-
-# conv_4_5_expand
-[convolutional]
-filters=384
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=swish
-
-# conv_4_5_dwise
-[convolutional]
-groups=384
-filters=384
-size=3
-stride=1
-pad=1
-batch_normalize=1
-activation=swish
-
-
-#squeeze-n-excitation
-[avgpool]
-
-# squeeze ratio r=16 (recommended r=16)
-[convolutional]
-filters=24
-size=1
-stride=1
-activation=swish
-
-# excitation
-[convolutional]
-filters=384
-size=1
-stride=1
-activation=logistic
-
-# multiply channels
-[scale_channels]
-from=-4
-
-
-# conv_4_5_linear
-[convolutional]
-filters=80
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=linear
-
-
-
-### CONV6 - MBConv6 - 1 (3)
-# dropout only before residual connection
-[dropout]
-probability=.0
-
-# block_4_6
-[shortcut]
-from=-9
-activation=linear
-
-# conv_4_7_expand
-[convolutional]
-filters=384
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=swish
-
-# conv_4_7_dwise
-[convolutional]
-groups=384
-filters=384
-size=5
-pad=1
-stride=2
-batch_normalize=1
-activation=swish
-
-
-#squeeze-n-excitation
-[avgpool]
-
-# squeeze ratio r=16 (recommended r=16)
-[convolutional]
-filters=24
-size=1
-stride=1
-activation=swish
-
-# excitation
-[convolutional]
-filters=384
-size=1
-stride=1
-activation=logistic
-
-# multiply channels
-[scale_channels]
-from=-4
-
-
-# conv_4_7_linear
-[convolutional]
-filters=112
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=linear
-
-
-### CONV6 - MBConv6 - 2 (3)
-# conv_5_1_expand
-[convolutional]
-filters=576
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=swish
-
-# conv_5_1_dwise
-[convolutional]
-groups=576
-filters=576
-size=5
-stride=1
-pad=1
-batch_normalize=1
-activation=swish
-
-
-#squeeze-n-excitation
-[avgpool]
-
-# squeeze ratio r=16 (recommended r=16)
-[convolutional]
-filters=32
-size=1
-stride=1
-activation=swish
-
-# excitation
-[convolutional]
-filters=576
-size=1
-stride=1
-activation=logistic
-
-# multiply channels
-[scale_channels]
-from=-4
-
-
-# conv_5_1_linear
-[convolutional]
-filters=112
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=linear
-
-
-### CONV6 - MBConv6 - 3 (3)
-# dropout only before residual connection
-[dropout]
-probability=.0
-
-# block_5_1
-[shortcut]
-from=-9
-activation=linear
-
-# conv_5_2_expand
-[convolutional]
-filters=576
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=swish
-
-# conv_5_2_dwise
-[convolutional]
-groups=576
-filters=576
-size=5
-stride=1
-pad=1
-batch_normalize=1
-activation=swish
-
-
-#squeeze-n-excitation
-[avgpool]
-
-# squeeze ratio r=16 (recommended r=16)
-[convolutional]
-filters=32
-size=1
-stride=1
-activation=swish
-
-# excitation
-[convolutional]
-filters=576
-size=1
-stride=1
-activation=logistic
-
-# multiply channels
-[scale_channels]
-from=-4
-
-
-# conv_5_2_linear
-[convolutional]
-filters=112
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=linear
-
-
-### CONV7 - MBConv6 - 1 (4)
-# dropout only before residual connection
-[dropout]
-probability=.0
-
-# block_5_2
-[shortcut]
-from=-9
-activation=linear
-
-# conv_5_3_expand
-[convolutional]
-filters=576
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=swish
-
-# conv_5_3_dwise
-[convolutional]
-groups=576
-filters=576
-size=5
-pad=1
-stride=2
-batch_normalize=1
-activation=swish
-
-
-#squeeze-n-excitation
-[avgpool]
-
-# squeeze ratio r=16 (recommended r=16)
-[convolutional]
-filters=32
-size=1
-stride=1
-activation=swish
-
-# excitation
-[convolutional]
-filters=576
-size=1
-stride=1
-activation=logistic
-
-# multiply channels
-[scale_channels]
-from=-4
-
-
-# conv_5_3_linear
-[convolutional]
-filters=192
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=linear
-
-
-### CONV7 - MBConv6 - 2 (4)
-# conv_6_1_expand
-[convolutional]
-filters=960
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=swish
-
-# conv_6_1_dwise
-[convolutional]
-groups=960
-filters=960
-size=5
-stride=1
-pad=1
-batch_normalize=1
-activation=swish
-
-
-#squeeze-n-excitation
-[avgpool]
-
-# squeeze ratio r=16 (recommended r=16)
-[convolutional]
-filters=64
-size=1
-stride=1
-activation=swish
-
-# excitation
-[convolutional]
-filters=960
-size=1
-stride=1
-activation=logistic
-
-# multiply channels
-[scale_channels]
-from=-4
-
-
-# conv_6_1_linear
-[convolutional]
-filters=192
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=linear
-
-
-### CONV7 - MBConv6 - 3 (4)
-# dropout only before residual connection
-[dropout]
-probability=.0
-
-# block_6_1
-[shortcut]
-from=-9
-activation=linear
-
-# conv_6_2_expand
-[convolutional]
-filters=960
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=swish
-
-# conv_6_2_dwise
-[convolutional]
-groups=960
-filters=960
-size=5
-stride=1
-pad=1
-batch_normalize=1
-activation=swish
-
-
-#squeeze-n-excitation
-[avgpool]
-
-# squeeze ratio r=16 (recommended r=16)
-[convolutional]
-filters=64
-size=1
-stride=1
-activation=swish
-
-# excitation
-[convolutional]
-filters=960
-size=1
-stride=1
-activation=logistic
-
-# multiply channels
-[scale_channels]
-from=-4
-
-
-# conv_6_2_linear
-[convolutional]
-filters=192
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=linear
-
-
-### CONV7 - MBConv6 - 4 (4)
-# dropout only before residual connection
-[dropout]
-probability=.0
-
-# block_6_1
-[shortcut]
-from=-9
-activation=linear
-
-# conv_6_2_expand
-[convolutional]
-filters=960
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=swish
-
-# conv_6_2_dwise
-[convolutional]
-groups=960
-filters=960
-size=5
-stride=1
-pad=1
-batch_normalize=1
-activation=swish
-
-
-#squeeze-n-excitation
-[avgpool]
-
-# squeeze ratio r=16 (recommended r=16)
-[convolutional]
-filters=64
-size=1
-stride=1
-activation=swish
-
-# excitation
-[convolutional]
-filters=960
-size=1
-stride=1
-activation=logistic
-
-# multiply channels
-[scale_channels]
-from=-4
-
-
-# conv_6_2_linear
-[convolutional]
-filters=192
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=linear
-
-
-
-### CONV8 - MBConv6 - 1 (1)
-# dropout only before residual connection
-[dropout]
-probability=.0
-
-# block_6_2
-[shortcut]
-from=-9
-activation=linear
-
-# conv_6_3_expand
-[convolutional]
-filters=960
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=swish
-
-# conv_6_3_dwise
-[convolutional]
-groups=960
-filters=960
-size=3
-stride=1
-pad=1
-batch_normalize=1
-activation=swish
-
-
-#squeeze-n-excitation
-[avgpool]
-
-# squeeze ratio r=16 (recommended r=16)
-[convolutional]
-filters=64
-size=1
-stride=1
-activation=swish
-
-# excitation
-[convolutional]
-filters=960
-size=1
-stride=1
-activation=logistic
-
-# multiply channels
-[scale_channels]
-from=-4
-
-
-# conv_6_3_linear
-[convolutional]
-filters=320
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=linear
-
-
-### CONV9 - Conv2d 1x1
-# conv_6_4
-[convolutional]
-filters=1280
-size=1
-stride=1
-pad=0
-batch_normalize=1
-activation=swish
-
-##########################
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-activation=leaky
-from=-2
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=linear
-
-
-
-[yolo]
-mask = 3,4,5
-anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319
-classes=80
-num=6
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-random=0
-
-[route]
-layers = -4
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[upsample]
-stride=2
-
-[shortcut]
-activation=leaky
-from=90
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-activation=leaky
-from=-3
-
-[shortcut]
-activation=leaky
-from=90
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=linear
-
-[yolo]
-mask = 1,2,3
-anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319
-classes=80
-num=6
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-random=0
-
diff --git a/build/darknet/x64/cfg/extraction.cfg b/build/darknet/x64/cfg/extraction.cfg
deleted file mode 100644
index 94e106754ef..00000000000
--- a/build/darknet/x64/cfg/extraction.cfg
+++ /dev/null
@@ -1,206 +0,0 @@
-[net]
-batch=128
-subdivisions=1
-height=224
-width=224
-max_crop=320
-channels=3
-momentum=0.9
-decay=0.0005
-
-learning_rate=0.1
-policy=poly
-power=4
-max_batches=1600000
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=7
-stride=2
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=192
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-filters=1000
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[avgpool]
-
-[softmax]
-groups=1
-
-[cost]
-type=sse
-
diff --git a/build/darknet/x64/cfg/extraction.conv.cfg b/build/darknet/x64/cfg/extraction.conv.cfg
deleted file mode 100644
index 2a7d09ec80f..00000000000
--- a/build/darknet/x64/cfg/extraction.conv.cfg
+++ /dev/null
@@ -1,179 +0,0 @@
-[net]
-batch=1
-subdivisions=1
-height=256
-width=256
-channels=3
-momentum=0.9
-decay=0.0005
-
-learning_rate=0.5
-policy=poly
-power=6
-max_batches=500000
-
-[convolutional]
-filters=64
-size=7
-stride=2
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-filters=192
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[avgpool]
-
-[connected]
-output=1000
-activation=leaky
-
-[softmax]
-groups=1
-
diff --git a/build/darknet/x64/cfg/extraction22k.cfg b/build/darknet/x64/cfg/extraction22k.cfg
deleted file mode 100644
index 4cec6da9605..00000000000
--- a/build/darknet/x64/cfg/extraction22k.cfg
+++ /dev/null
@@ -1,209 +0,0 @@
-[net]
-batch=128
-subdivisions=1
-height=224
-width=224
-max_crop=320
-channels=3
-momentum=0.9
-decay=0.0005
-
-learning_rate=0.01
-max_batches = 0
-policy=steps
-steps=444000,590000,970000
-scales=.5,.2,.1
-
-#policy=sigmoid
-#gamma=.00008
-#step=100000
-#max_batches=200000
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=7
-stride=2
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=192
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=2048
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=2048
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[avgpool]
-
-[connected]
-output=21842
-activation=leaky
-
-[softmax]
-groups=1
-
-[cost]
-type=sse
-
diff --git a/build/darknet/x64/cfg/go.test.cfg b/build/darknet/x64/cfg/go.test.cfg
deleted file mode 100644
index 6b92d335cd1..00000000000
--- a/build/darknet/x64/cfg/go.test.cfg
+++ /dev/null
@@ -1,131 +0,0 @@
-[net]
-batch=1
-subdivisions=1
-height=19
-width=19
-channels=1
-momentum=0.9
-decay=0.0005
-
-learning_rate=0.1
-policy=poly
-power=4
-max_batches=400000
-
-[convolutional]
-filters=192
-size=3
-stride=1
-pad=1
-activation=relu
-batch_normalize=1
-
-[convolutional]
-filters=192
-size=3
-stride=1
-pad=1
-activation=relu
-batch_normalize=1
-
-[convolutional]
-filters=192
-size=3
-stride=1
-pad=1
-activation=relu
-batch_normalize=1
-
-[convolutional]
-filters=192
-size=3
-stride=1
-pad=1
-activation=relu
-batch_normalize=1
-
-[convolutional]
-filters=192
-size=3
-stride=1
-pad=1
-activation=relu
-batch_normalize=1
-
-[convolutional]
-filters=192
-size=3
-stride=1
-pad=1
-activation=relu
-batch_normalize=1
-
-[convolutional]
-filters=192
-size=3
-stride=1
-pad=1
-activation=relu
-batch_normalize=1
-
-[convolutional]
-filters=192
-size=3
-stride=1
-pad=1
-activation=relu
-batch_normalize=1
-
-[convolutional]
-filters=192
-size=3
-stride=1
-pad=1
-activation=relu
-batch_normalize=1
-
-[convolutional]
-filters=192
-size=3
-stride=1
-pad=1
-activation=relu
-batch_normalize=1
-
-[convolutional]
-filters=192
-size=3
-stride=1
-pad=1
-activation=relu
-batch_normalize=1
-
-[convolutional]
-filters=192
-size=3
-stride=1
-pad=1
-activation=relu
-batch_normalize=1
-
-[convolutional]
-filters=192
-size=3
-stride=1
-pad=1
-activation=relu
-batch_normalize=1
-
-
-[convolutional]
-filters=1
-size=1
-stride=1
-pad=1
-activation=linear
-
-[softmax]
-
-[cost]
-type=sse
-
diff --git a/build/darknet/x64/cfg/gru.cfg b/build/darknet/x64/cfg/gru.cfg
deleted file mode 100644
index f9a06999e62..00000000000
--- a/build/darknet/x64/cfg/gru.cfg
+++ /dev/null
@@ -1,34 +0,0 @@
-[net]
-subdivisions=1
-inputs=256
-batch = 1
-momentum=0.9
-decay=0.001
-time_steps=1
-learning_rate=0.5
-
-policy=poly
-power=4
-max_batches=2000
-
-[gru]
-batch_normalize=1
-output = 1024
-
-[gru]
-batch_normalize=1
-output = 1024
-
-[gru]
-batch_normalize=1
-output = 1024
-
-[connected]
-output=256
-activation=linear
-
-[softmax]
-
-[cost]
-type=sse
-
diff --git a/build/darknet/x64/cfg/imagenet1k.data b/build/darknet/x64/cfg/imagenet1k.data
deleted file mode 100644
index b01f2facec9..00000000000
--- a/build/darknet/x64/cfg/imagenet1k.data
+++ /dev/null
@@ -1,9 +0,0 @@
-classes=1000
-train  = data/imagenet1k.train.list
-#train  = data/inet.val.list
-valid  = data/inet.val.list
-backup = backup
-labels = data/imagenet.labels.list
-names  = data/imagenet.shortnames.list
-top=5
-
diff --git a/build/darknet/x64/cfg/imagenet22k.dataset b/build/darknet/x64/cfg/imagenet22k.dataset
deleted file mode 100644
index 920785d603b..00000000000
--- a/build/darknet/x64/cfg/imagenet22k.dataset
+++ /dev/null
@@ -1,8 +0,0 @@
-classes=21842
-train  = /data/imagenet/imagenet22k.train.list
-valid  = /data/imagenet/imagenet22k.valid.list
-backup = /home/pjreddie/backup/
-labels = data/imagenet.labels.list
-names  = data/imagenet.shortnames.list
-top = 5
-
diff --git a/build/darknet/x64/cfg/imagenet9k.hierarchy.dataset b/build/darknet/x64/cfg/imagenet9k.hierarchy.dataset
deleted file mode 100644
index 41fb71b0655..00000000000
--- a/build/darknet/x64/cfg/imagenet9k.hierarchy.dataset
+++ /dev/null
@@ -1,9 +0,0 @@
-classes=9418
-train  = data/9k.train.list
-valid  = /data/imagenet/imagenet1k.valid.list
-leaves = data/imagenet1k.labels
-backup = /home/pjreddie/backup/
-labels = data/9k.labels
-names  = data/9k.names
-top=5
-
diff --git a/build/darknet/x64/cfg/jnet-conv.cfg b/build/darknet/x64/cfg/jnet-conv.cfg
deleted file mode 100644
index 056f82aa6e2..00000000000
--- a/build/darknet/x64/cfg/jnet-conv.cfg
+++ /dev/null
@@ -1,118 +0,0 @@
-[net]
-batch=1
-subdivisions=1
-height=10
-width=10
-channels=3
-learning_rate=0.01
-momentum=0.9
-decay=0.0005
-
-[convolutional]
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-stride=2
-size=2
-
-[convolutional]
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-stride=2
-size=2
-
-[convolutional]
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-stride=2
-size=2
-
-[convolutional]
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-stride=2
-size=2
-
-[convolutional]
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-stride=2
-size=2
-
-[convolutional]
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
diff --git a/build/darknet/x64/cfg/lstm.train.cfg b/build/darknet/x64/cfg/lstm.train.cfg
deleted file mode 100644
index f0d4721d414..00000000000
--- a/build/darknet/x64/cfg/lstm.train.cfg
+++ /dev/null
@@ -1,35 +0,0 @@
-[net]
-subdivisions=8
-inputs=256
-batch = 128
-momentum=0.9
-decay=0.001
-max_batches = 2000
-time_steps=576
-learning_rate=0.5
-policy=steps
-burn_in=10
-steps=1000,1500
-scales=.1,.1
-
-[lstm]
-batch_normalize=1
-output = 1024
-
-[lstm]
-batch_normalize=1
-output = 1024
-
-[lstm]
-batch_normalize=1
-output = 1024
-
-[connected]
-output=256
-activation=leaky
-
-[softmax]
-
-[cost]
-type=sse
-
diff --git a/build/darknet/x64/cfg/openimages.data b/build/darknet/x64/cfg/openimages.data
deleted file mode 100644
index fa80e5ab7d8..00000000000
--- a/build/darknet/x64/cfg/openimages.data
+++ /dev/null
@@ -1,8 +0,0 @@
-classes= 601
-train  = /home/pjreddie/data/openimsv4/openimages.train.list
-#valid  = coco_testdev
-valid = data/coco_val_5k.list
-names = data/openimages.names
-backup = /home/pjreddie/backup/
-eval=coco
-
diff --git a/build/darknet/x64/cfg/resnet101.cfg b/build/darknet/x64/cfg/resnet101.cfg
deleted file mode 100644
index 48dffbf2ec5..00000000000
--- a/build/darknet/x64/cfg/resnet101.cfg
+++ /dev/null
@@ -1,990 +0,0 @@
-[net]
-# Training
-batch=128
-subdivisions=2
-
-# Testing
-#batch=1
-#subdivisions=1
-
-height=256
-width=256
-channels=3
-min_crop=128
-max_crop=448
-
-burn_in=1000
-learning_rate=0.1
-policy=poly
-power=4
-max_batches=800000
-momentum=0.9
-decay=0.0005
-
-angle=7
-hue=.1
-saturation=.75
-exposure=.75
-aspect=.75
-
-
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=7
-stride=2
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-
-# Conv 4
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-#Conv 5
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-
-
-
-
-
-[convolutional]
-filters=1000
-size=1
-stride=1
-pad=1
-activation=linear
-
-[avgpool]
-
-[softmax]
-groups=1
-
-[cost]
-type=sse
-
diff --git a/build/darknet/x64/cfg/resnet152.cfg b/build/darknet/x64/cfg/resnet152.cfg
deleted file mode 100644
index d5fe90948a2..00000000000
--- a/build/darknet/x64/cfg/resnet152.cfg
+++ /dev/null
@@ -1,1463 +0,0 @@
-[net]
-# Training
-# batch=128
-# subdivisions=8
-
-# Testing
-batch=1
-subdivisions=1
-
-height=256
-width=256
-max_crop=448
-channels=3
-momentum=0.9
-decay=0.0005
-
-burn_in=1000
-learning_rate=0.1
-policy=poly
-power=4
-max_batches=1600000
-
-angle=7
-hue=.1
-saturation=.75
-exposure=.75
-aspect=.75
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=7
-stride=2
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-
-# Conv 4
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-#Conv 5
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-
-
-
-
-
-[convolutional]
-filters=1000
-size=1
-stride=1
-pad=1
-activation=linear
-
-[avgpool]
-
-[softmax]
-groups=1
-
-[cost]
-type=sse
-
diff --git a/build/darknet/x64/cfg/resnet152_trident.cfg b/build/darknet/x64/cfg/resnet152_trident.cfg
deleted file mode 100644
index 1ac8cc73a78..00000000000
--- a/build/darknet/x64/cfg/resnet152_trident.cfg
+++ /dev/null
@@ -1,2177 +0,0 @@
-[net]
-# Testing
-#batch=1
-#subdivisions=1
-# Training
-batch=64
-subdivisions=64
-width=608
-height=608
-channels=3
-momentum=0.9
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-
-learning_rate=0.001
-burn_in=1000
-max_batches = 10000
-
-policy=sgdr
-sgdr_cycle=1000
-sgdr_mult=2
-steps=4000,6000,8000,9000
-#scales=1, 1, 0.1, 0.1
-
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=7
-stride=2
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-2
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-
-# Conv 4
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-2
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-
-
-
-### TridentNet - large objects - Start
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-dilation=3
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-dilation=3
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-dilation=3
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-dilation=3
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-dilation=3
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-dilation=3
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-dilation=3
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-
-## Conv 5
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-dilation=3
-batch_normalize=1
-filters=512
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-2
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-dilation=3
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-dilation=3
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=1
-stride=1
-pad=1
-filters=2048
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=24
-activation=linear
-
-[yolo]
-mask = 8,9,10,11
-anchors = 8,8, 10,13, 16,30, 33,23,  32,32, 30,61, 62,45, 59,119,   80,80, 116,90, 156,198, 373,326
-classes=1
-num=12
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-random=0
-
-### TridentNet - large objects - End
-
-
-
-
-
-
-
-### TridentNet - medium objects - Start
-
-[route]
-layers = 165
-
-[convolutional]
-share_index=166
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-share_index=167
-dilation=2
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-share_index=168
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-share_index=170
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-share_index=171
-dilation=2
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-share_index=172
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-share_index=174
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-share_index=175
-dilation=2
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-share_index=176
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-share_index=178
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-share_index=179
-dilation=2
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-share_index=180
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-share_index=182
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-share_index=183
-dilation=2
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-share_index=184
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-share_index=186
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-share_index=187
-dilation=2
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-share_index=188
-dilation=2
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-
-## Conv 5
-[convolutional]
-share_index=190
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-share_index=191
-dilation=2
-batch_normalize=1
-filters=512
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-share_index=192
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-2
-activation=leaky
-
-[convolutional]
-share_index=194
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-share_index=195
-dilation=2
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-share_index=196
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-share_index=198
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-share_index=199
-dilation=2
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-share_index=200
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=1
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[upsample]
-stride=2
-
-[route]
-layers = -1, 49
-
-[convolutional]
-batch_normalize=1
-size=1
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=24
-activation=linear
-
-[yolo]
-mask = 4,5,6,7
-anchors = 8,8, 10,13, 16,30, 33,23,  32,32, 30,61, 62,45, 64,64,  59,119, 116,90, 156,198, 373,326
-classes=1
-num=12
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-random=0
-
-### TridentNet - medium objects - End
-
-
-
-
-
-
-
-
-
-
-
-### TridentNet - small objects - Start
-
-[route]
-layers = 165
-
-[convolutional]
-share_index=166
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-share_index=167
-dilation=1
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-share_index=168
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-share_index=170
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-share_index=171
-dilation=1
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-share_index=172
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-share_index=174
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-share_index=175
-dilation=1
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-share_index=176
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-share_index=178
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-share_index=179
-dilation=1
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-share_index=180
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-share_index=182
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-share_index=183
-dilation=1
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-share_index=184
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-share_index=186
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-share_index=187
-dilation=1
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-share_index=188
-dilation=1
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-
-## Conv 5
-[convolutional]
-share_index=190
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-share_index=191
-dilation=1
-batch_normalize=1
-filters=512
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-share_index=192
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-2
-activation=leaky
-
-[convolutional]
-share_index=194
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-share_index=195
-dilation=1
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-share_index=196
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-share_index=198
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-share_index=199
-dilation=1
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-share_index=200
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=1
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[upsample]
-stride=4
-
-[route]
-layers = -1, 17
-
-[convolutional]
-batch_normalize=1
-size=1
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=24
-activation=linear
-
-[yolo]
-mask = 0,1,2,3
-anchors = 8,8, 10,13, 16,30, 33,23,  32,32, 30,61, 62,45, 64,64,  59,119, 116,90, 156,198, 373,326
-classes=1
-num=12
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-random=0
-
-### TridentNet - small objects - End
-
diff --git a/build/darknet/x64/cfg/resnet50.cfg b/build/darknet/x64/cfg/resnet50.cfg
deleted file mode 100644
index bfe69b18e3b..00000000000
--- a/build/darknet/x64/cfg/resnet50.cfg
+++ /dev/null
@@ -1,511 +0,0 @@
-[net]
-# Training
-# batch=128
-# subdivisions=4
-
-# Testing
-batch=1
-subdivisions=1
-
-height=256
-width=256
-max_crop=448
-channels=3
-momentum=0.9
-decay=0.0005
-
-burn_in=1000
-learning_rate=0.1
-policy=poly
-power=4
-max_batches=1600000
-
-angle=7
-hue=.1
-saturation=.75
-exposure=.75
-aspect=.75
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=7
-stride=2
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-
-# Conv 4
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-#Conv 5
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-
-
-
-
-
-[convolutional]
-filters=1000
-size=1
-stride=1
-pad=1
-activation=linear
-
-[avgpool]
-
-[softmax]
-groups=1
-
-[cost]
-type=sse
-
diff --git a/build/darknet/x64/cfg/resnext152-32x4d.cfg b/build/darknet/x64/cfg/resnext152-32x4d.cfg
deleted file mode 100644
index 48279fd28eb..00000000000
--- a/build/darknet/x64/cfg/resnext152-32x4d.cfg
+++ /dev/null
@@ -1,1562 +0,0 @@
-[net]
-# Training
-# batch=128
-# subdivisions=16
-
-# Testing
-batch=1
-subdivisions=1
-
-height=256
-width=256
-channels=3
-min_crop=128
-max_crop=448
-
-burn_in=1000
-learning_rate=0.1
-policy=poly
-power=4
-max_batches=800000
-momentum=0.9
-decay=0.0005
-
-angle=7
-hue=.1
-saturation=.75
-exposure=.75
-aspect=.75
-
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=7
-stride=2
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-groups = 32
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-    
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-groups = 32
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-    
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-groups = 32
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-    
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-groups = 32
-batch_normalize=1
-filters=128
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-    
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-groups = 32
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-    
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-groups = 32
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-    
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-groups = 32
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-    
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-groups = 32
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-    
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-groups = 32
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-    
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-groups = 32
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-    
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-groups = 32
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-    
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-groups = 32
-batch_normalize=1
-filters=256
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-    
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-groups = 32
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-    
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-groups = 32
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-    
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-groups = 32
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-    
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-groups = 32
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-    
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-groups = 32
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-    
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-groups = 32
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-    
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-groups = 32
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-    
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-groups = 32
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-    
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-groups = 32
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-    
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-groups = 32
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-    
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-groups = 32
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-    
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-groups = 32
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-    
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-groups = 32
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-    
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-groups = 32
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-    
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-groups = 32
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-    
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-groups = 32
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-    
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-groups = 32
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-    
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-groups = 32
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-    
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-groups = 32
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-    
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-groups = 32
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-    
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-groups = 32
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-    
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-groups = 32
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-    
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-groups = 32
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-    
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-groups = 32
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-    
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-groups = 32
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-    
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-groups = 32
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-    
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-groups = 32
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-    
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-groups = 32
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-    
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-groups = 32
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-    
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-groups = 32
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-    
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-groups = 32
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-    
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-groups = 32
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-    
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-groups = 32
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-    
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-groups = 32
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-    
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-groups = 32
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-    
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-groups = 32
-batch_normalize=1
-filters=512
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=4096
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-    
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-groups = 32
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=4096
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-    
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-groups = 32
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=4096
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-    
-
-
-[avgpool]
-
-[convolutional]
-filters=1000
-size=1
-stride=1
-pad=1
-activation=linear
-
-[softmax]
-groups=1
-
diff --git a/build/darknet/x64/cfg/rnn.cfg b/build/darknet/x64/cfg/rnn.cfg
deleted file mode 100644
index 68c032d27f9..00000000000
--- a/build/darknet/x64/cfg/rnn.cfg
+++ /dev/null
@@ -1,40 +0,0 @@
-[net]
-subdivisions=1
-inputs=256
-batch = 1
-momentum=0.9
-decay=0.001
-max_batches = 2000
-time_steps=1
-learning_rate=0.1
-policy=steps
-steps=1000,1500
-scales=.1,.1
-
-[rnn]
-batch_normalize=1
-output = 1024
-hidden=1024
-activation=leaky
-
-[rnn]
-batch_normalize=1
-output = 1024
-hidden=1024
-activation=leaky
-
-[rnn]
-batch_normalize=1
-output = 1024
-hidden=1024
-activation=leaky
-
-[connected]
-output=256
-activation=leaky
-
-[softmax]
-
-[cost]
-type=sse
-
diff --git a/build/darknet/x64/cfg/rnn.train.cfg b/build/darknet/x64/cfg/rnn.train.cfg
deleted file mode 100644
index 3c63956a249..00000000000
--- a/build/darknet/x64/cfg/rnn.train.cfg
+++ /dev/null
@@ -1,40 +0,0 @@
-[net]
-subdivisions=8
-inputs=256
-batch = 128
-momentum=0.9
-decay=0.001
-max_batches = 2000
-time_steps=576
-learning_rate=0.1
-policy=steps
-steps=1000,1500
-scales=.1,.1
-
-[rnn]
-batch_normalize=1
-output = 1024
-hidden=1024
-activation=leaky
-
-[rnn]
-batch_normalize=1
-output = 1024
-hidden=1024
-activation=leaky
-
-[rnn]
-batch_normalize=1
-output = 1024
-hidden=1024
-activation=leaky
-
-[connected]
-output=256
-activation=leaky
-
-[softmax]
-
-[cost]
-type=sse
-
diff --git a/build/darknet/x64/cfg/strided.cfg b/build/darknet/x64/cfg/strided.cfg
deleted file mode 100644
index a52700b4303..00000000000
--- a/build/darknet/x64/cfg/strided.cfg
+++ /dev/null
@@ -1,185 +0,0 @@
-[net]
-batch=128
-subdivisions=4
-height=256
-width=256
-channels=3
-momentum=0.9
-decay=0.0005
-
-learning_rate=0.01
-policy=steps
-scales=.1,.1,.1
-steps=200000,300000,400000
-max_batches=800000
-
-
-[crop]
-crop_height=224
-crop_width=224
-flip=1
-angle=0
-saturation=1
-exposure=1
-shift=.2
-
-[convolutional]
-filters=64
-size=7
-stride=2
-pad=1
-activation=ramp
-
-[convolutional]
-filters=192
-size=3
-stride=2
-pad=1
-activation=ramp
-
-[convolutional]
-filters=128
-size=1
-stride=1
-pad=1
-activation=ramp
-
-[convolutional]
-filters=256
-size=3
-stride=2
-pad=1
-activation=ramp
-
-[convolutional]
-filters=128
-size=1
-stride=1
-pad=1
-activation=ramp
-
-[convolutional]
-filters=256
-size=3
-stride=1
-pad=1
-activation=ramp
-
-[convolutional]
-filters=128
-size=1
-stride=1
-pad=1
-activation=ramp
-
-[convolutional]
-filters=512
-size=3
-stride=2
-pad=1
-activation=ramp
-
-[convolutional]
-filters=256
-size=1
-stride=1
-pad=1
-activation=ramp
-
-[convolutional]
-filters=512
-size=3
-stride=1
-pad=1
-activation=ramp
-
-[convolutional]
-filters=256
-size=1
-stride=1
-pad=1
-activation=ramp
-
-[convolutional]
-filters=512
-size=3
-stride=1
-pad=1
-activation=ramp
-
-[convolutional]
-filters=256
-size=1
-stride=1
-pad=1
-activation=ramp
-
-[convolutional]
-filters=512
-size=3
-stride=1
-pad=1
-activation=ramp
-
-[convolutional]
-filters=256
-size=1
-stride=1
-pad=1
-activation=ramp
-
-[convolutional]
-filters=512
-size=3
-stride=1
-pad=1
-activation=ramp
-
-[convolutional]
-filters=256
-size=1
-stride=1
-pad=1
-activation=ramp
-
-[convolutional]
-filters=1024
-size=3
-stride=2
-pad=1
-activation=ramp
-
-[convolutional]
-filters=512
-size=1
-stride=1
-pad=1
-activation=ramp
-
-[convolutional]
-filters=1024
-size=3
-stride=1
-pad=1
-activation=ramp
-
-[maxpool]
-size=3
-stride=2
-
-[connected]
-output=4096
-activation=ramp
-
-[dropout]
-probability=0.5
-
-[connected]
-output=1000
-activation=ramp
-
-[softmax]
-
-[cost]
-type=sse
-
diff --git a/build/darknet/x64/cfg/t1.test.cfg b/build/darknet/x64/cfg/t1.test.cfg
deleted file mode 100644
index b3628114e04..00000000000
--- a/build/darknet/x64/cfg/t1.test.cfg
+++ /dev/null
@@ -1,117 +0,0 @@
-[net]
-batch=1
-subdivisions=1
-height=224
-width=224
-channels=3
-momentum=0.9
-decay=0.0005
-
-learning_rate=0.0005
-policy=steps
-steps=200,400,600,20000,30000
-scales=2.5,2,2,.1,.1
-max_batches = 40000
-
-[convolutional]
-filters=16
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[connected]
-output= 1470
-activation=linear
-
-[detection]
-classes=20
-coords=4
-rescore=1
-side=7
-num=2
-softmax=0
-sqrt=1
-jitter=.2
-
-object_scale=1
-noobject_scale=.5
-class_scale=1
-coord_scale=5
-
diff --git a/build/darknet/x64/cfg/tiny-yolo-voc.cfg b/build/darknet/x64/cfg/tiny-yolo-voc.cfg
deleted file mode 100644
index ab2c066a216..00000000000
--- a/build/darknet/x64/cfg/tiny-yolo-voc.cfg
+++ /dev/null
@@ -1,134 +0,0 @@
-[net]
-batch=64
-subdivisions=8
-width=416
-height=416
-channels=3
-momentum=0.9
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-learning_rate=0.001
-max_batches = 40200
-policy=steps
-steps=-1,100,20000,30000
-scales=.1,10,.1,.1
-
-[convolutional]
-batch_normalize=1
-filters=16
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=1
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-###########
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=125
-activation=linear
-
-[region]
-anchors = 1.08,1.19,  3.42,4.41,  6.63,11.38,  9.42,5.11,  16.62,10.52
-bias_match=1
-classes=20
-coords=4
-num=5
-softmax=1
-jitter=.2
-rescore=1
-
-object_scale=5
-noobject_scale=1
-class_scale=1
-coord_scale=1
-
-absolute=1
-thresh = .6
-random=1
diff --git a/build/darknet/x64/cfg/tiny-yolo.cfg b/build/darknet/x64/cfg/tiny-yolo.cfg
deleted file mode 100644
index 5580098b45f..00000000000
--- a/build/darknet/x64/cfg/tiny-yolo.cfg
+++ /dev/null
@@ -1,134 +0,0 @@
-[net]
-batch=64
-subdivisions=8
-width=416
-height=416
-channels=3
-momentum=0.9
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-learning_rate=0.001
-max_batches = 120000
-policy=steps
-steps=-1,100,80000,100000
-scales=.1,10,.1,.1
-
-[convolutional]
-batch_normalize=1
-filters=16
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=1
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-###########
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=425
-activation=linear
-
-[region]
-anchors = 0.738768,0.874946,  2.42204,2.65704,  4.30971,7.04493,  10.246,4.59428,  12.6868,11.8741
-bias_match=1
-classes=80
-coords=4
-num=5
-softmax=1
-jitter=.2
-rescore=1
-
-object_scale=5
-noobject_scale=1
-class_scale=1
-coord_scale=1
-
-absolute=1
-thresh = .6
-random=1
diff --git a/build/darknet/x64/cfg/tiny-yolo_xnor.cfg b/build/darknet/x64/cfg/tiny-yolo_xnor.cfg
deleted file mode 100644
index 42a96309db0..00000000000
--- a/build/darknet/x64/cfg/tiny-yolo_xnor.cfg
+++ /dev/null
@@ -1,148 +0,0 @@
-[net]
-batch=64
-subdivisions=8
-width=416
-height=416
-channels=3
-momentum=0.9
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-learning_rate=0.001
-max_batches = 40200
-policy=steps
-steps=-1,100,20000,30000
-scales=.1,10,.1,.1
-
-[convolutional]
-#xnor=1
-batch_normalize=1
-filters=16
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-xnor=1
-bin_output=1
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-xnor=1
-bin_output=1
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-xnor=1
-bin_output=1
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-xnor=1
-bin_output=1
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-xnor=1
-bin_output=1
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=1
-
-[convolutional]
-xnor=1
-bin_output=1
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-###########
-
-[convolutional]
-xnor=1
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=425
-activation=linear
-
-[region]
-anchors = 0.738768,0.874946,  2.42204,2.65704,  4.30971,7.04493,  10.246,4.59428,  12.6868,11.8741
-bias_match=1
-classes=80
-coords=4
-num=5
-softmax=1
-jitter=.2
-rescore=1
-
-object_scale=5
-noobject_scale=1
-class_scale=1
-coord_scale=1
-
-absolute=1
-thresh = .6
-random=1
diff --git a/build/darknet/x64/cfg/tiny.cfg b/build/darknet/x64/cfg/tiny.cfg
deleted file mode 100644
index 99c260366d7..00000000000
--- a/build/darknet/x64/cfg/tiny.cfg
+++ /dev/null
@@ -1,172 +0,0 @@
-[net]
-batch=128
-subdivisions=1
-height=224
-width=224
-channels=3
-momentum=0.9
-decay=0.0005
-max_crop=320
-
-learning_rate=0.1
-policy=poly
-power=4
-max_batches=1600000
-
-angle=7
-hue=.1
-saturation=.75
-exposure=.75
-aspect=.75
-
-[convolutional]
-batch_normalize=1
-filters=16
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=16
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=16
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-filters=1000
-size=1
-stride=1
-pad=1
-activation=linear
-
-[avgpool]
-
-[softmax]
-groups=1
-
-[cost]
-type=sse
-
diff --git a/build/darknet/x64/cfg/vgg-16.cfg b/build/darknet/x64/cfg/vgg-16.cfg
deleted file mode 100644
index 2b6f7029b70..00000000000
--- a/build/darknet/x64/cfg/vgg-16.cfg
+++ /dev/null
@@ -1,153 +0,0 @@
-[net]
-batch=128
-subdivisions=4
-height=256
-width=256
-channels=3
-learning_rate=0.00001
-momentum=0.9
-decay=0.0005
-
-[crop]
-crop_height=224
-crop_width=224
-flip=1
-exposure=1
-saturation=1
-angle=0
-
-[convolutional]
-filters=64
-size=3
-stride=1
-pad=1
-activation=relu
-
-[convolutional]
-filters=64
-size=3
-stride=1
-pad=1
-activation=relu
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-filters=128
-size=3
-stride=1
-pad=1
-activation=relu
-
-[convolutional]
-filters=128
-size=3
-stride=1
-pad=1
-activation=relu
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-filters=256
-size=3
-stride=1
-pad=1
-activation=relu
-
-[convolutional]
-filters=256
-size=3
-stride=1
-pad=1
-activation=relu
-
-[convolutional]
-filters=256
-size=3
-stride=1
-pad=1
-activation=relu
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-filters=512
-size=3
-stride=1
-pad=1
-activation=relu
-
-[convolutional]
-filters=512
-size=3
-stride=1
-pad=1
-activation=relu
-
-[convolutional]
-filters=512
-size=3
-stride=1
-pad=1
-activation=relu
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-filters=512
-size=3
-stride=1
-pad=1
-activation=relu
-
-[convolutional]
-filters=512
-size=3
-stride=1
-pad=1
-activation=relu
-
-[convolutional]
-filters=512
-size=3
-stride=1
-pad=1
-activation=relu
-
-[maxpool]
-size=2
-stride=2
-
-[connected]
-output=4096
-activation=relu
-
-[dropout]
-probability=.5
-
-[connected]
-output=4096
-activation=relu
-
-[dropout]
-probability=.5
-
-[connected]
-output=1000
-activation=linear
-
-[softmax]
-groups=1
-
-[cost]
-type=sse
-
diff --git a/build/darknet/x64/cfg/vgg-conv.cfg b/build/darknet/x64/cfg/vgg-conv.cfg
deleted file mode 100644
index e173e28bff6..00000000000
--- a/build/darknet/x64/cfg/vgg-conv.cfg
+++ /dev/null
@@ -1,123 +0,0 @@
-[net]
-batch=1
-subdivisions=1
-width=112
-height=112
-#width=224
-#height=224
-channels=3
-learning_rate=0.00001
-momentum=0.9
-decay=0.0005
-
-[convolutional]
-filters=64
-size=3
-stride=1
-pad=1
-activation=relu
-
-[convolutional]
-filters=64
-size=3
-stride=1
-pad=1
-activation=relu
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-filters=128
-size=3
-stride=1
-pad=1
-activation=relu
-
-[convolutional]
-filters=128
-size=3
-stride=1
-pad=1
-activation=relu
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-filters=256
-size=3
-stride=1
-pad=1
-activation=relu
-
-[convolutional]
-filters=256
-size=3
-stride=1
-pad=1
-activation=relu
-
-[convolutional]
-filters=256
-size=3
-stride=1
-pad=1
-activation=relu
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-filters=512
-size=3
-stride=1
-pad=1
-activation=relu
-
-[convolutional]
-filters=512
-size=3
-stride=1
-pad=1
-activation=relu
-
-[convolutional]
-filters=512
-size=3
-stride=1
-pad=1
-activation=relu
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-filters=512
-size=3
-stride=1
-pad=1
-activation=relu
-
-[convolutional]
-filters=512
-size=3
-stride=1
-pad=1
-activation=relu
-
-[convolutional]
-filters=512
-size=3
-stride=1
-pad=1
-activation=relu
-
-[maxpool]
-size=2
-stride=2
-
diff --git a/build/darknet/x64/cfg/voc.data b/build/darknet/x64/cfg/voc.data
deleted file mode 100644
index d6775870f65..00000000000
--- a/build/darknet/x64/cfg/voc.data
+++ /dev/null
@@ -1,7 +0,0 @@
-classes= 20
-train  = data/train_voc.txt
-valid  = data/2007_test.txt
-#difficult = data/difficult_2007_test.txt
-names = data/voc.names
-backup = backup/
-
diff --git a/build/darknet/x64/cfg/writing.cfg b/build/darknet/x64/cfg/writing.cfg
deleted file mode 100644
index 1ed899bcd63..00000000000
--- a/build/darknet/x64/cfg/writing.cfg
+++ /dev/null
@@ -1,41 +0,0 @@
-[net]
-batch=128
-subdivisions=2
-height=256
-width=256
-channels=3
-learning_rate=0.00000001
-momentum=0.9
-decay=0.0005
-seen=0
-
-[convolutional]
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-filters=1
-size=3
-stride=1
-pad=1
-activation=logistic
-
-[cost]
-
diff --git a/build/darknet/x64/cfg/yolo-voc.2.0.cfg b/build/darknet/x64/cfg/yolo-voc.2.0.cfg
deleted file mode 100644
index ceb3f2acf0b..00000000000
--- a/build/darknet/x64/cfg/yolo-voc.2.0.cfg
+++ /dev/null
@@ -1,244 +0,0 @@
-[net]
-batch=64
-subdivisions=8
-height=416
-width=416
-channels=3
-momentum=0.9
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-learning_rate=0.0001
-max_batches = 45000
-policy=steps
-steps=100,25000,35000
-scales=10,.1,.1
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-
-#######
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[route]
-layers=-9
-
-[reorg]
-stride=2
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=125
-activation=linear
-
-[region]
-anchors = 1.08,1.19,  3.42,4.41,  6.63,11.38,  9.42,5.11,  16.62,10.52
-bias_match=1
-classes=20
-coords=4
-num=5
-softmax=1
-jitter=.2
-rescore=1
-
-object_scale=5
-noobject_scale=1
-class_scale=1
-coord_scale=1
-
-absolute=1
-thresh = .6
-random=0
diff --git a/build/darknet/x64/cfg/yolo-voc.cfg b/build/darknet/x64/cfg/yolo-voc.cfg
deleted file mode 100644
index dbf2de281c1..00000000000
--- a/build/darknet/x64/cfg/yolo-voc.cfg
+++ /dev/null
@@ -1,258 +0,0 @@
-[net]
-# Testing
-batch=1
-subdivisions=1
-# Training
-# batch=64
-# subdivisions=8
-height=416
-width=416
-channels=3
-momentum=0.9
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-learning_rate=0.001
-burn_in=1000
-max_batches = 80200
-policy=steps
-steps=40000,60000
-scales=.1,.1
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-
-#######
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[route]
-layers=-9
-
-[convolutional]
-batch_normalize=1
-size=1
-stride=1
-pad=1
-filters=64
-activation=leaky
-
-[reorg]
-stride=2
-
-[route]
-layers=-1,-4
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=125
-activation=linear
-
-
-[region]
-anchors =  1.3221, 1.73145, 3.19275, 4.00944, 5.05587, 8.09892, 9.47112, 4.84053, 11.2364, 10.0071
-bias_match=1
-classes=20
-coords=4
-num=5
-softmax=1
-jitter=.3
-rescore=1
-
-object_scale=5
-noobject_scale=1
-class_scale=1
-coord_scale=1
-
-absolute=1
-thresh = .6
-random=1
diff --git a/build/darknet/x64/cfg/yolo.2.0.cfg b/build/darknet/x64/cfg/yolo.2.0.cfg
deleted file mode 100644
index fda339a2b00..00000000000
--- a/build/darknet/x64/cfg/yolo.2.0.cfg
+++ /dev/null
@@ -1,244 +0,0 @@
-[net]
-batch=1
-subdivisions=1
-width=416
-height=416
-channels=3
-momentum=0.9
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-learning_rate=0.001
-max_batches = 120000
-policy=steps
-steps=-1,100,80000,100000
-scales=.1,10,.1,.1
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-
-#######
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[route]
-layers=-9
-
-[reorg]
-stride=2
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=425
-activation=linear
-
-[region]
-anchors = 0.738768,0.874946,  2.42204,2.65704,  4.30971,7.04493,  10.246,4.59428,  12.6868,11.8741
-bias_match=1
-classes=80
-coords=4
-num=5
-softmax=1
-jitter=.2
-rescore=1
-
-object_scale=5
-noobject_scale=1
-class_scale=1
-coord_scale=1
-
-absolute=1
-thresh = .6
-random=0
diff --git a/build/darknet/x64/cfg/yolo.cfg b/build/darknet/x64/cfg/yolo.cfg
deleted file mode 100644
index 7001dfa58a9..00000000000
--- a/build/darknet/x64/cfg/yolo.cfg
+++ /dev/null
@@ -1,258 +0,0 @@
-[net]
-# Testing
-batch=1
-subdivisions=1
-# Training
-# batch=64
-# subdivisions=8
-height=416
-width=416
-channels=3
-momentum=0.9
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-learning_rate=0.001
-burn_in=1000
-max_batches = 500200
-policy=steps
-steps=400000,450000
-scales=.1,.1
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-
-#######
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[route]
-layers=-9
-
-[convolutional]
-batch_normalize=1
-size=1
-stride=1
-pad=1
-filters=64
-activation=leaky
-
-[reorg]
-stride=2
-
-[route]
-layers=-1,-4
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=425
-activation=linear
-
-
-[region]
-anchors =  0.57273, 0.677385, 1.87446, 2.06253, 3.33843, 5.47434, 7.88282, 3.52778, 9.77052, 9.16828
-bias_match=1
-classes=80
-coords=4
-num=5
-softmax=1
-jitter=.3
-rescore=1
-
-object_scale=5
-noobject_scale=1
-class_scale=1
-coord_scale=1
-
-absolute=1
-thresh = .6
-random=1
diff --git a/build/darknet/x64/cfg/yolo9000.cfg b/build/darknet/x64/cfg/yolo9000.cfg
deleted file mode 100644
index e745f78a6e3..00000000000
--- a/build/darknet/x64/cfg/yolo9000.cfg
+++ /dev/null
@@ -1,218 +0,0 @@
-[net]
-# Testing
-batch=1
-subdivisions=1
-# Training
-# batch=64
-# subdivisions=8
-batch=1
-subdivisions=1
-height=544
-width=544
-channels=3
-momentum=0.9
-decay=0.0005
-
-learning_rate=0.001
-burn_in=1000
-max_batches = 500200
-policy=steps
-steps=400000,450000
-scales=.1,.1
-
-hue=.1
-saturation=.75
-exposure=.75
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-filters=28269
-size=1
-stride=1
-pad=1
-activation=linear
-
-[region]
-anchors = 0.77871, 1.14074, 3.00525, 4.31277, 9.22725, 9.61974
-bias_match=1
-classes=9418
-coords=4
-num=3
-softmax=1
-jitter=.2
-rescore=1
-
-object_scale=5
-noobject_scale=1
-class_scale=1
-coord_scale=1
-
-thresh = .6
-absolute=1
-random=1
-
-tree=data/9k.tree
-map = data/coco9k.map
diff --git a/build/darknet/x64/cfg/yolov2-tiny-voc.cfg b/build/darknet/x64/cfg/yolov2-tiny-voc.cfg
deleted file mode 100644
index c4c127cdd35..00000000000
--- a/build/darknet/x64/cfg/yolov2-tiny-voc.cfg
+++ /dev/null
@@ -1,138 +0,0 @@
-[net]
-# Testing
-batch=1
-subdivisions=1
-# Training
-# batch=64
-# subdivisions=2
-width=416
-height=416
-channels=3
-momentum=0.9
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-learning_rate=0.001
-max_batches = 40200
-policy=steps
-steps=-1,100,20000,30000
-scales=.1,10,.1,.1
-
-[convolutional]
-batch_normalize=1
-filters=16
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=1
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-###########
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=125
-activation=linear
-
-[region]
-anchors = 1.08,1.19,  3.42,4.41,  6.63,11.38,  9.42,5.11,  16.62,10.52
-bias_match=1
-classes=20
-coords=4
-num=5
-softmax=1
-jitter=.2
-rescore=1
-
-object_scale=5
-noobject_scale=1
-class_scale=1
-coord_scale=1
-
-absolute=1
-thresh = .6
-random=1
diff --git a/build/darknet/x64/cfg/yolov2-tiny.cfg b/build/darknet/x64/cfg/yolov2-tiny.cfg
deleted file mode 100644
index 81d0ac45d6d..00000000000
--- a/build/darknet/x64/cfg/yolov2-tiny.cfg
+++ /dev/null
@@ -1,139 +0,0 @@
-[net]
-# Testing
-batch=1
-subdivisions=1
-# Training
-# batch=64
-# subdivisions=2
-width=416
-height=416
-channels=3
-momentum=0.9
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-learning_rate=0.001
-burn_in=1000
-max_batches = 500200
-policy=steps
-steps=400000,450000
-scales=.1,.1
-
-[convolutional]
-batch_normalize=1
-filters=16
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=1
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-###########
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=425
-activation=linear
-
-[region]
-anchors =  0.57273, 0.677385, 1.87446, 2.06253, 3.33843, 5.47434, 7.88282, 3.52778, 9.77052, 9.16828
-bias_match=1
-classes=80
-coords=4
-num=5
-softmax=1
-jitter=.2
-rescore=0
-
-object_scale=5
-noobject_scale=1
-class_scale=1
-coord_scale=1
-
-absolute=1
-thresh = .6
-random=1
diff --git a/build/darknet/x64/cfg/yolov2-voc.cfg b/build/darknet/x64/cfg/yolov2-voc.cfg
deleted file mode 100644
index dbf2de281c1..00000000000
--- a/build/darknet/x64/cfg/yolov2-voc.cfg
+++ /dev/null
@@ -1,258 +0,0 @@
-[net]
-# Testing
-batch=1
-subdivisions=1
-# Training
-# batch=64
-# subdivisions=8
-height=416
-width=416
-channels=3
-momentum=0.9
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-learning_rate=0.001
-burn_in=1000
-max_batches = 80200
-policy=steps
-steps=40000,60000
-scales=.1,.1
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-
-#######
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[route]
-layers=-9
-
-[convolutional]
-batch_normalize=1
-size=1
-stride=1
-pad=1
-filters=64
-activation=leaky
-
-[reorg]
-stride=2
-
-[route]
-layers=-1,-4
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=125
-activation=linear
-
-
-[region]
-anchors =  1.3221, 1.73145, 3.19275, 4.00944, 5.05587, 8.09892, 9.47112, 4.84053, 11.2364, 10.0071
-bias_match=1
-classes=20
-coords=4
-num=5
-softmax=1
-jitter=.3
-rescore=1
-
-object_scale=5
-noobject_scale=1
-class_scale=1
-coord_scale=1
-
-absolute=1
-thresh = .6
-random=1
diff --git a/build/darknet/x64/cfg/yolov2.cfg b/build/darknet/x64/cfg/yolov2.cfg
deleted file mode 100644
index 2a0cd98fbd0..00000000000
--- a/build/darknet/x64/cfg/yolov2.cfg
+++ /dev/null
@@ -1,258 +0,0 @@
-[net]
-# Testing
-batch=1
-subdivisions=1
-# Training
-# batch=64
-# subdivisions=8
-width=416
-height=416
-channels=3
-momentum=0.9
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-learning_rate=0.001
-burn_in=1000
-max_batches = 500200
-policy=steps
-steps=400000,450000
-scales=.1,.1
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-
-#######
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[route]
-layers=-9
-
-[convolutional]
-batch_normalize=1
-size=1
-stride=1
-pad=1
-filters=64
-activation=leaky
-
-[reorg]
-stride=2
-
-[route]
-layers=-1,-4
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=425
-activation=linear
-
-
-[region]
-anchors =  0.57273, 0.677385, 1.87446, 2.06253, 3.33843, 5.47434, 7.88282, 3.52778, 9.77052, 9.16828
-bias_match=1
-classes=80
-coords=4
-num=5
-softmax=1
-jitter=.3
-rescore=1
-
-object_scale=5
-noobject_scale=1
-class_scale=1
-coord_scale=1
-
-absolute=1
-thresh = .6
-random=1
diff --git a/build/darknet/x64/cfg/yolov3-openimages.cfg b/build/darknet/x64/cfg/yolov3-openimages.cfg
deleted file mode 100644
index 65d241a74c4..00000000000
--- a/build/darknet/x64/cfg/yolov3-openimages.cfg
+++ /dev/null
@@ -1,789 +0,0 @@
-[net]
-# Testing
- batch=1
- subdivisions=1
-# Training
-batch=64
-subdivisions=16
-width=608
-height=608
-channels=3
-momentum=0.9
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-learning_rate=0.001
-burn_in=5000
-max_batches = 500200
-policy=steps
-steps=400000,450000
-scales=.1,.1
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-######################
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=1818
-activation=linear
-
-
-[yolo]
-mask = 6,7,8
-anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
-classes=601
-num=9
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-random=1
-
-
-[route]
-layers = -4
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[upsample]
-stride=2
-
-[route]
-layers = -1, 61
-
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=1818
-activation=linear
-
-
-[yolo]
-mask = 3,4,5
-anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
-classes=601
-num=9
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-random=1
-
-
-
-[route]
-layers = -4
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[upsample]
-stride=2
-
-[route]
-layers = -1, 36
-
-
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=1818
-activation=linear
-
-
-[yolo]
-mask = 0,1,2
-anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
-classes=601
-num=9
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-random=1
-
diff --git a/build/darknet/x64/cfg/yolov3-spp.cfg b/build/darknet/x64/cfg/yolov3-spp.cfg
deleted file mode 100644
index 4ad2a052d88..00000000000
--- a/build/darknet/x64/cfg/yolov3-spp.cfg
+++ /dev/null
@@ -1,822 +0,0 @@
-[net]
-# Testing
-batch=1
-subdivisions=1
-# Training
-# batch=64
-# subdivisions=16
-width=608
-height=608
-channels=3
-momentum=0.9
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-learning_rate=0.001
-burn_in=1000
-max_batches = 500200
-policy=steps
-steps=400000,450000
-scales=.1,.1
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-######################
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-### SPP ###
-[maxpool]
-stride=1
-size=5
-
-[route]
-layers=-2
-
-[maxpool]
-stride=1
-size=9
-
-[route]
-layers=-4
-
-[maxpool]
-stride=1
-size=13
-
-[route]
-layers=-1,-3,-5,-6
-
-### End SPP ###
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=linear
-
-
-[yolo]
-mask = 6,7,8
-anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
-classes=80
-num=9
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-random=1
-
-
-[route]
-layers = -4
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[upsample]
-stride=2
-
-[route]
-layers = -1, 61
-
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=linear
-
-
-[yolo]
-mask = 3,4,5
-anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
-classes=80
-num=9
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-random=1
-
-
-
-[route]
-layers = -4
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[upsample]
-stride=2
-
-[route]
-layers = -1, 36
-
-
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=linear
-
-
-[yolo]
-mask = 0,1,2
-anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
-classes=80
-num=9
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-random=1
-
diff --git a/build/darknet/x64/cfg/yolov3-tiny-prn.cfg b/build/darknet/x64/cfg/yolov3-tiny-prn.cfg
deleted file mode 100644
index 215162e973b..00000000000
--- a/build/darknet/x64/cfg/yolov3-tiny-prn.cfg
+++ /dev/null
@@ -1,199 +0,0 @@
-[net]
-# Testing
-#batch=1
-#subdivisions=1
-# Training
-batch=64
-subdivisions=8
-width=416
-height=416
-channels=3
-momentum=0.9
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-learning_rate=0.001
-burn_in=1000
-max_batches = 500200
-policy=steps
-steps=400000,450000
-scales=.1,.1
-
-[convolutional]
-batch_normalize=1
-filters=16
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=1
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-activation=leaky
-from=-3
-
-###########
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-activation=leaky
-from=-2
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=linear
-
-
-
-[yolo]
-mask = 3,4,5
-anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319
-classes=80
-num=6
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-random=1
-
-[route]
-layers = -4
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[upsample]
-stride=2
-
-[shortcut]
-activation=leaky
-from=8
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-activation=leaky
-from=-3
-
-[shortcut]
-activation=leaky
-from=8
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=linear
-
-[yolo]
-mask = 1,2,3
-anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319
-classes=80
-num=6
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-random=1
diff --git a/build/darknet/x64/cfg/yolov3-tiny.cfg b/build/darknet/x64/cfg/yolov3-tiny.cfg
deleted file mode 100644
index cfca3cfa641..00000000000
--- a/build/darknet/x64/cfg/yolov3-tiny.cfg
+++ /dev/null
@@ -1,182 +0,0 @@
-[net]
-# Testing
-batch=1
-subdivisions=1
-# Training
-# batch=64
-# subdivisions=2
-width=416
-height=416
-channels=3
-momentum=0.9
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-learning_rate=0.001
-burn_in=1000
-max_batches = 500200
-policy=steps
-steps=400000,450000
-scales=.1,.1
-
-[convolutional]
-batch_normalize=1
-filters=16
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=1
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-###########
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=linear
-
-
-
-[yolo]
-mask = 3,4,5
-anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319
-classes=80
-num=6
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-random=1
-
-[route]
-layers = -4
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[upsample]
-stride=2
-
-[route]
-layers = -1, 8
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=linear
-
-[yolo]
-mask = 0,1,2
-anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319
-classes=80
-num=6
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-random=1
diff --git a/build/darknet/x64/cfg/yolov3-tiny_3l.cfg b/build/darknet/x64/cfg/yolov3-tiny_3l.cfg
deleted file mode 100644
index 76aaa0d9587..00000000000
--- a/build/darknet/x64/cfg/yolov3-tiny_3l.cfg
+++ /dev/null
@@ -1,227 +0,0 @@
-[net]
-# Testing
-# batch=1
-# subdivisions=1
-# Training
-batch=64
-subdivisions=16
-width=608
-height=608
-channels=3
-momentum=0.9
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-learning_rate=0.001
-burn_in=1000
-max_batches = 200000
-policy=steps
-steps=180000,190000
-scales=.1,.1
-
-
-[convolutional]
-batch_normalize=1
-filters=16
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=1
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-###########
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=21
-activation=linear
-
-
-
-[yolo]
-mask = 6,7,8
-anchors = 4,7, 7,15, 13,25,   25,42, 41,67, 75,94,   91,162, 158,205, 250,332
-classes=2
-num=9
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-random=1
-
-[route]
-layers = -4
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[upsample]
-stride=2
-
-[route]
-layers = -1, 8
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=21
-activation=linear
-
-[yolo]
-mask = 3,4,5
-anchors = 4,7, 7,15, 13,25,   25,42, 41,67, 75,94,   91,162, 158,205, 250,332
-classes=2
-num=9
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-random=1
-
-
-
-[route]
-layers = -3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[upsample]
-stride=2
-
-[route]
-layers = -1, 6
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=21
-activation=linear
-
-[yolo]
-mask = 0,1,2
-anchors = 4,7, 7,15, 13,25,   25,42, 41,67, 75,94,   91,162, 158,205, 250,332
-classes=2
-num=9
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-random=1
\ No newline at end of file
diff --git a/build/darknet/x64/cfg/yolov3-tiny_obj.cfg b/build/darknet/x64/cfg/yolov3-tiny_obj.cfg
deleted file mode 100644
index 8308e442517..00000000000
--- a/build/darknet/x64/cfg/yolov3-tiny_obj.cfg
+++ /dev/null
@@ -1,182 +0,0 @@
-[net]
-# Testing
-#batch=1
-#subdivisions=1
-# Training
-batch=64
-subdivisions=2
-width=416
-height=416
-channels=3
-momentum=0.9
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-learning_rate=0.001
-burn_in=1000
-max_batches = 500200
-policy=steps
-steps=400000,450000
-scales=.1,.1
-
-[convolutional]
-batch_normalize=1
-filters=16
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=1
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-###########
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=linear
-
-
-
-[yolo]
-mask = 3,4,5
-anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319
-classes=80
-num=6
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-random=1
-
-[route]
-layers = -4
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[upsample]
-stride=2
-
-[route]
-layers = -1, 8
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=linear
-
-[yolo]
-mask = 0,1,2
-anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319
-classes=80
-num=6
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-random=1
diff --git a/build/darknet/x64/cfg/yolov3-tiny_occlusion_track.cfg b/build/darknet/x64/cfg/yolov3-tiny_occlusion_track.cfg
deleted file mode 100644
index 2ef25370eea..00000000000
--- a/build/darknet/x64/cfg/yolov3-tiny_occlusion_track.cfg
+++ /dev/null
@@ -1,218 +0,0 @@
-[net]
-# Testing
-#batch=1
-#subdivisions=1
-# Training
-batch=8
-subdivisions=4
-width=416
-height=416
-channels=3
-momentum=0.9
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-track=1
-time_steps=20
-augment_speed=3
-
-learning_rate=0.001
-burn_in=1000
-max_batches = 10000
-policy=steps
-steps=9000,9500
-scales=.1,.1
-
-[convolutional]
-batch_normalize=1
-filters=16
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=1
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-###########
-
-
-[crnn]
-batch_normalize=1
-size=3
-pad=1
-output=512
-hidden=256
-activation=leaky
-
-#[shortcut]
-#from=-2
-#activation=linear
-
-###########
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=18
-activation=linear
-
-
-
-[yolo]
-mask = 3,4,5
-anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319
-classes=1
-num=6
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-random=0
-
-[route]
-layers = -4
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[upsample]
-stride=2
-
-[route]
-layers = -1, 8
-
-[crnn]
-batch_normalize=1
-size=3
-pad=1
-output=256
-hidden=128
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=18
-activation=linear
-
-[yolo]
-mask = 0,1,2
-anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319
-classes=1
-num=6
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-random=0
diff --git a/build/darknet/x64/cfg/yolov3-tiny_xnor.cfg b/build/darknet/x64/cfg/yolov3-tiny_xnor.cfg
deleted file mode 100644
index 725ce079c37..00000000000
--- a/build/darknet/x64/cfg/yolov3-tiny_xnor.cfg
+++ /dev/null
@@ -1,197 +0,0 @@
-[net]
-# Testing
-#batch=1
-#subdivisions=1
-# Training
-batch=64
-subdivisions=2
-width=416
-height=416
-channels=3
-momentum=0.9
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-learning_rate=0.001
-burn_in=1000
-max_batches = 500200
-policy=steps
-steps=400000,450000
-scales=.1,.1
-
-[convolutional]
-batch_normalize=1
-filters=16
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-xnor=1
-bin_output=1
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-xnor=1
-bin_output=1
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-xnor=1
-bin_output=1
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-xnor=1
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-xnor=1
-bin_output=1
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=1
-
-[convolutional]
-xnor=1
-bin_output=1
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-###########
-
-[convolutional]
-xnor=1
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=linear
-
-
-
-[yolo]
-mask = 3,4,5
-anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319
-classes=80
-num=6
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-random=1
-
-[route]
-layers = -4
-
-[convolutional]
-xnor=1
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[upsample]
-stride=2
-
-[route]
-layers = -1, 8
-
-[convolutional]
-xnor=1
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=linear
-
-[yolo]
-mask = 0,1,2
-anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319
-classes=80
-num=6
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-random=1
diff --git a/build/darknet/x64/cfg/yolov3-voc.cfg b/build/darknet/x64/cfg/yolov3-voc.cfg
deleted file mode 100644
index 3f3e8dfb31b..00000000000
--- a/build/darknet/x64/cfg/yolov3-voc.cfg
+++ /dev/null
@@ -1,785 +0,0 @@
-[net]
-# Testing
- batch=1
- subdivisions=1
-# Training
-# batch=64
-# subdivisions=16
-width=416
-height=416
-channels=3
-momentum=0.9
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-learning_rate=0.001
-burn_in=1000
-max_batches = 50200
-policy=steps
-steps=40000,45000
-scales=.1,.1
-
-
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-######################
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=75
-activation=linear
-
-[yolo]
-mask = 6,7,8
-anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
-classes=20
-num=9
-jitter=.3
-ignore_thresh = .5
-truth_thresh = 1
-random=1
-
-[route]
-layers = -4
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[upsample]
-stride=2
-
-[route]
-layers = -1, 61
-
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=75
-activation=linear
-
-[yolo]
-mask = 3,4,5
-anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
-classes=20
-num=9
-jitter=.3
-ignore_thresh = .5
-truth_thresh = 1
-random=1
-
-[route]
-layers = -4
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[upsample]
-stride=2
-
-[route]
-layers = -1, 36
-
-
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=75
-activation=linear
-
-[yolo]
-mask = 0,1,2
-anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
-classes=20
-num=9
-jitter=.3
-ignore_thresh = .5
-truth_thresh = 1
-random=1
-
diff --git a/build/darknet/x64/cfg/yolov3-voc.yolov3-giou-40.cfg b/build/darknet/x64/cfg/yolov3-voc.yolov3-giou-40.cfg
deleted file mode 100644
index b56f8a5d698..00000000000
--- a/build/darknet/x64/cfg/yolov3-voc.yolov3-giou-40.cfg
+++ /dev/null
@@ -1,808 +0,0 @@
-[net]
-# Testing
-# batch=1
-# subdivisions=1
-# Training
-batch=64
-subdivisions=16
-width=416
-height=416
-channels=3
-momentum=0.9
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-## single gpu
-learning_rate=0.001
-burn_in=1000
-max_batches = 100400
-
-## 2x
-#learning_rate=0.0005
-#burn_in=2000
-#max_batches = 100400
-#max_batches = 200800
-
-## 4x
-#learning_rate=0.00025
-#burn_in=4000
-#max_batches = 50200
-##max_batches = 200800
-
-policy=steps
-steps=40000,45000
-scales=.1,.1
-
-
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-######################
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=75
-activation=linear
-
-[yolo]
-mask = 6,7,8
-anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
-classes=20
-num=9
-jitter=.3
-ignore_thresh = .5
-truth_thresh = 1
-random=1
-iou_normalizer=0.25
-cls_normalizer=1.0
-iou_loss=giou
-
-[route]
-layers = -4
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[upsample]
-stride=2
-
-[route]
-layers = -1, 61
-
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=75
-activation=linear
-
-[yolo]
-mask = 3,4,5
-anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
-classes=20
-num=9
-jitter=.3
-ignore_thresh = .5
-truth_thresh = 1
-random=1
-iou_normalizer=0.25
-cls_normalizer=1.0
-iou_loss=giou
-
-[route]
-layers = -4
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[upsample]
-stride=2
-
-[route]
-layers = -1, 36
-
-
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=75
-activation=linear
-
-[yolo]
-mask = 0,1,2
-anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
-classes=20
-num=9
-jitter=.3
-ignore_thresh = .5
-truth_thresh = 1
-random=1
-iou_normalizer=0.25
-cls_normalizer=1.0
-iou_loss=giou
-
diff --git a/build/darknet/x64/cfg/yolov3.cfg b/build/darknet/x64/cfg/yolov3.cfg
deleted file mode 100644
index 4a0ecc3320e..00000000000
--- a/build/darknet/x64/cfg/yolov3.cfg
+++ /dev/null
@@ -1,789 +0,0 @@
-[net]
-# Testing
-batch=1
-subdivisions=1
-# Training
-# batch=64
-# subdivisions=16
-width=416
-height=416
-channels=3
-momentum=0.9
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-learning_rate=0.001
-burn_in=1000
-max_batches = 500200
-policy=steps
-steps=400000,450000
-scales=.1,.1
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-######################
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=linear
-
-
-[yolo]
-mask = 6,7,8
-anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
-classes=80
-num=9
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-random=1
-
-
-[route]
-layers = -4
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[upsample]
-stride=2
-
-[route]
-layers = -1, 61
-
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=linear
-
-
-[yolo]
-mask = 3,4,5
-anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
-classes=80
-num=9
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-random=1
-
-
-
-[route]
-layers = -4
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[upsample]
-stride=2
-
-[route]
-layers = -1, 36
-
-
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=linear
-
-
-[yolo]
-mask = 0,1,2
-anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
-classes=80
-num=9
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-random=1
-
diff --git a/build/darknet/x64/cfg/yolov3.coco-giou-12.cfg b/build/darknet/x64/cfg/yolov3.coco-giou-12.cfg
deleted file mode 100644
index f3fd72db0b1..00000000000
--- a/build/darknet/x64/cfg/yolov3.coco-giou-12.cfg
+++ /dev/null
@@ -1,806 +0,0 @@
-[net]
-# Testing
-# batch=1
-# subdivisions=1
-# Training
-batch=64
-subdivisions=16
-width=608
-height=608
-channels=3
-momentum=0.9
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-## single gpu
-learning_rate=0.001
-burn_in=1000
-max_batches = 550400
-
-## 2 gpu
-#learning_rate=0.0005
-#burn_in=2000
-#max_batches = 500200
-
-## 4 gpu
-#learning_rate=0.00025
-#burn_in=4000
-#max_batches = 500200
-###max_batches = 2000800
-
-policy=steps
-steps=400000,450000
-scales=.1,.1
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-######################
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=linear
-
-
-[yolo]
-mask = 6,7,8
-anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
-classes=80
-num=9
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-random=1
-iou_normalizer=0.5
-iou_loss=giou
-
-[route]
-layers = -4
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[upsample]
-stride=2
-
-[route]
-layers = -1, 61
-
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=linear
-
-
-[yolo]
-mask = 3,4,5
-anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
-classes=80
-num=9
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-random=1
-iou_normalizer=0.5
-iou_loss=giou
-
-
-
-[route]
-layers = -4
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[upsample]
-stride=2
-
-[route]
-layers = -1, 36
-
-
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=linear
-
-
-[yolo]
-mask = 0,1,2
-anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
-classes=80
-num=9
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-random=1
-iou_normalizer=0.5
-iou_loss=giou
diff --git a/build/darknet/x64/cfg/yolov3_5l.cfg b/build/darknet/x64/cfg/yolov3_5l.cfg
deleted file mode 100644
index fec157e0a52..00000000000
--- a/build/darknet/x64/cfg/yolov3_5l.cfg
+++ /dev/null
@@ -1,968 +0,0 @@
-[net]
-# Testing
-#batch=1
-#subdivisions=1
-# Training
-batch=64
-subdivisions=16
-width=416
-height=416
-channels=3
-momentum=0.9
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-learning_rate=0.001
-burn_in=1000
-max_batches = 500200
-policy=steps
-steps=400000,450000
-scales=.1,.1
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-######################
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=linear
-
-
-[yolo]
-mask = 12,13,14
-anchors = 4,4,  5,5,  6,6, 7,7,  8,8,  9,9, 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
-classes=80
-num=15
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-random=1
-
-
-[route]
-layers = -4
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[upsample]
-stride=2
-
-[route]
-layers = -1, 61
-
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=linear
-
-
-[yolo]
-mask = 9,10,11
-anchors = 4,4,  5,5,  6,6, 7,7,  8,8,  9,9, 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
-classes=80
-num=15
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-random=1
-
-
-
-[route]
-layers = -4
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[upsample]
-stride=2
-
-[route]
-layers = -1, 36
-
-
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=linear
-
-
-[yolo]
-mask = 6,7,8
-anchors = 4,4,  5,5,  6,6, 7,7,  8,8,  9,9, 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
-classes=80
-num=15
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-random=1
-
-
-
-###############
-
-
-[route]
-layers = -4
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[upsample]
-stride=2
-
-[route]
-layers = -1, 11
-
-
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=128
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=128
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=128
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=linear
-
-
-[yolo]
-mask = 3,4,5
-anchors = 4,4,  5,5,  6,6, 7,7,  8,8,  9,9, 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
-classes=80
-num=15
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-random=1
-
-
-
-
-
-[route]
-layers = -4
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[upsample]
-stride=2
-
-[route]
-layers = -1, 4
-
-
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=64
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=64
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=64
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=linear
-
-
-[yolo]
-mask = 0,1,2
-anchors = 4,4,  5,5,  6,6, 7,7,  8,8,  9,9, 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
-classes=80
-num=15
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-random=1
-
diff --git a/build/darknet/x64/cfg/yolov4-csp-swish.cfg b/build/darknet/x64/cfg/yolov4-csp-swish.cfg
deleted file mode 100644
index 2aab444ecd5..00000000000
--- a/build/darknet/x64/cfg/yolov4-csp-swish.cfg
+++ /dev/null
@@ -1,1355 +0,0 @@
-[net]
-# Testing
-#batch=1
-#subdivisions=1
-# Training
-batch=64
-subdivisions=8
-width=640
-height=640
-channels=3
-momentum=0.949
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-learning_rate=0.001
-burn_in=1000
-max_batches = 500500
-policy=steps
-steps=400000,450000
-scales=.1,.1
-
-mosaic=1
-
-letter_box=1
-
-ema_alpha=0.9998
-
-#optimized_memory=1
-
-
-# ============ Backbone ============ #
-
-# Stem 
-
-# 0
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=swish
-
-# P1
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=2
-pad=1
-activation=swish
-
-# Residual Block
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=swish
-
-# 4 (previous+1+3k)
-[shortcut]
-from=-3
-activation=linear
-
-# P2
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=2
-pad=1
-activation=swish
-
-# Split
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=swish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=swish
-
-# Residual Block
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-# Transition first
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=swish
-
-# Merge [-1, -(3k+4)]
-
-[route]
-layers = -1,-10
-
-# Transition last
-
-# 17 (previous+7+3k)
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=swish
-
-# P3
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=2
-pad=1
-activation=swish
-
-# Split
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=swish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=swish
-
-# Residual Block
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-# Transition first
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=swish
-
-# Merge [-1 -(4+3k)]
-
-[route]
-layers = -1,-28
-
-# Transition last
-
-# 48 (previous+7+3k)
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=swish
-
-# P4
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=2
-pad=1
-activation=swish
-
-# Split
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=swish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=swish
-
-# Residual Block
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-# Transition first
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=swish
-
-# Merge [-1 -(3k+4)]
-
-[route]
-layers = -1,-28
-
-# Transition last
-
-# 79 (previous+7+3k)
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=swish
-
-# P5
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=2
-pad=1
-activation=swish
-
-# Split
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=swish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=swish
-
-# Residual Block
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-# Transition first
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=swish
-
-# Merge [-1 -(3k+4)]
-
-[route]
-layers = -1,-16
-
-# Transition last
-
-# 98 (previous+7+3k)
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=swish
-
-# ============ End of Backbone ============ #
-
-# ============ Neck ============ #
-
-# CSPSPP
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=swish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=swish
-
-### SPP ###
-[maxpool]
-stride=1
-size=5
-
-[route]
-layers=-2
-
-[maxpool]
-stride=1
-size=9
-
-[route]
-layers=-4
-
-[maxpool]
-stride=1
-size=13
-
-[route]
-layers=-1,-3,-5,-6
-### End SPP ###
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=swish
-
-[route]
-layers = -1, -13
-
-# 113 (previous+6+5+2k)
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=swish
-
-# End of CSPSPP
-
-
-# FPN-4
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=swish
-
-[upsample]
-stride=2
-
-[route]
-layers = 79
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=swish
-
-[route]
-layers = -1, -3
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=swish
-
-# Split
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=swish
-
-[route]
-layers = -2
-
-# Plain Block
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=swish
-
-# Merge [-1, -(2k+2)]
-
-[route]
-layers = -1, -6
-
-# Transition last
-
-# 127 (previous+6+4+2k)
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=swish
-
-
-# FPN-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=swish
-
-[upsample]
-stride=2
-
-[route]
-layers = 48
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=swish
-
-[route]
-layers = -1, -3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=swish
-
-# Split
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=swish
-
-[route]
-layers = -2
-
-# Plain Block
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=128
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=128
-activation=swish
-
-# Merge [-1, -(2k+2)]
-
-[route]
-layers = -1, -6
-
-# Transition last
-
-# 141 (previous+6+4+2k)
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=swish
-
-
-# PAN-4
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=2
-pad=1
-filters=256
-activation=swish
-
-[route]
-layers = -1, 127
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=swish
-
-# Split
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=swish
-
-[route]
-layers = -2
-
-# Plain Block
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=swish
-
-[route]
-layers = -1,-6
-
-# Transition last
-
-# 152 (previous+3+4+2k)
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=swish
-
-
-# PAN-5
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=2
-pad=1
-filters=512
-activation=swish
-
-[route]
-layers = -1, 113
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=swish
-
-# Split
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=swish
-
-[route]
-layers = -2
-
-# Plain Block
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=swish
-
-[route]
-layers = -1,-6
-
-# Transition last
-
-# 163 (previous+3+4+2k)
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=swish
-stopbackward=900
-
-# ============ End of Neck ============ #
-
-# ============ Head ============ #
-
-# YOLO-3
-
-[route]
-layers = 141
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=swish
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=logistic
-
-[yolo]
-mask = 0,1,2
-anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
-classes=80
-num=9
-jitter=.1
-scale_x_y = 2.0
-objectness_smooth=1
-ignore_thresh = .7
-truth_thresh = 1
-#random=1
-resize=1.5
-#iou_thresh=0.2
-iou_normalizer=0.05
-cls_normalizer=0.5
-obj_normalizer=0.4
-iou_loss=ciou
-nms_kind=diounms
-beta_nms=0.6
-new_coords=1
-max_delta=2
-
-
-# YOLO-4
-
-[route]
-layers = 152
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=swish
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=logistic
-
-[yolo]
-mask = 3,4,5
-anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
-classes=80
-num=9
-jitter=.1
-scale_x_y = 2.0
-objectness_smooth=1
-ignore_thresh = .7
-truth_thresh = 1
-#random=1
-resize=1.5
-#iou_thresh=0.2
-iou_normalizer=0.05
-cls_normalizer=0.5
-obj_normalizer=0.4
-iou_loss=ciou
-nms_kind=diounms
-beta_nms=0.6
-new_coords=1
-max_delta=2
-
-
-# YOLO-5
-
-[route]
-layers = 163
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=swish
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=logistic
-
-[yolo]
-mask = 6,7,8
-anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
-classes=80
-num=9
-jitter=.1
-scale_x_y = 2.0
-objectness_smooth=1
-ignore_thresh = .7
-truth_thresh = 1
-#random=1
-resize=1.5
-#iou_thresh=0.2
-iou_normalizer=0.05
-cls_normalizer=0.5
-obj_normalizer=0.4
-iou_loss=ciou
-nms_kind=diounms
-beta_nms=0.6
-new_coords=1
-max_delta=2
diff --git a/build/darknet/x64/cfg/yolov4-csp-x-swish-frozen.cfg b/build/darknet/x64/cfg/yolov4-csp-x-swish-frozen.cfg
deleted file mode 100644
index 838e0e1ac72..00000000000
--- a/build/darknet/x64/cfg/yolov4-csp-x-swish-frozen.cfg
+++ /dev/null
@@ -1,1556 +0,0 @@
-  
-[net]
-# Testing
-#batch=1
-#subdivisions=1
-# Training
-batch=64
-subdivisions=8
-width=640
-height=640
-channels=3
-momentum=0.949
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-learning_rate=0.001
-burn_in=1000
-max_batches = 500500
-policy=steps
-steps=400000,450000
-scales=.1,.1
-
-mosaic=1
-
-letter_box=1
-
-ema_alpha=0.9998
-
-#optimized_memory=1
-
-
-# ============ Backbone ============ #
-
-# Stem 
-
-# 0
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=swish
-
-# P1
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=3
-stride=2
-pad=1
-activation=swish
-
-# Residual Block
-
-[convolutional]
-batch_normalize=1
-filters=40
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=3
-stride=1
-pad=1
-activation=swish
-
-# 4 (previous+1+3k)
-[shortcut]
-from=-3
-activation=linear
-
-# P2
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=2
-pad=1
-activation=swish
-
-# Split
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=1
-stride=1
-pad=1
-activation=swish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=1
-stride=1
-pad=1
-activation=swish
-
-# Residual Block
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-# Transition first
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=1
-stride=1
-pad=1
-activation=swish
-
-# Merge [-1, -(3k+4)]
-
-[route]
-layers = -1,-13
-
-# Transition last
-
-# 20 (previous+7+3k)
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=swish
-
-# P3
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=2
-pad=1
-activation=swish
-
-# Split
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=swish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=swish
-
-# Residual Block
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-# Transition first
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=swish
-
-# Merge [-1 -(4+3k)]
-
-[route]
-layers = -1,-34
-
-# Transition last
-
-# 57 (previous+7+3k)
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=swish
-
-# P4
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=2
-pad=1
-activation=swish
-
-# Split
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=swish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=swish
-
-# Residual Block
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-# Transition first
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=swish
-
-# Merge [-1 -(3k+4)]
-
-[route]
-layers = -1,-34
-
-# Transition last
-
-# 94 (previous+7+3k)
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=swish
-
-# P5
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=1280
-size=3
-stride=2
-pad=1
-activation=swish
-
-# Split
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=swish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=swish
-
-# Residual Block
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-# Transition first
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=swish
-
-# Merge [-1 -(3k+4)]
-
-[route]
-layers = -1,-19
-
-# Transition last
-
-# 116 (previous+7+3k)
-[convolutional]
-batch_normalize=1
-filters=1280
-size=1
-stride=1
-pad=1
-activation=swish
-
-# ============ End of Backbone ============ #
-
-# ============ Neck ============ #
-
-# CSPSPP
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=swish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=640
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=swish
-
-### SPP ###
-[maxpool]
-stride=1
-size=5
-
-[route]
-layers=-2
-
-[maxpool]
-stride=1
-size=9
-
-[route]
-layers=-4
-
-[maxpool]
-stride=1
-size=13
-
-[route]
-layers=-1,-3,-5,-6
-### End SPP ###
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=640
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=640
-activation=swish
-
-[route]
-layers = -1, -15
-
-# 133 (previous+6+5+2k)
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=swish
-
-# End of CSPSPP
-
-
-# FPN-4
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=swish
-
-[upsample]
-stride=2
-
-[route]
-layers = 94
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=swish
-
-[route]
-layers = -1, -3
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=swish
-
-# Split
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=swish
-
-[route]
-layers = -2
-
-# Plain Block
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=320
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=320
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=320
-activation=swish
-
-# Merge [-1, -(2k+2)]
-
-[route]
-layers = -1, -8
-
-# Transition last
-
-# 149 (previous+6+4+2k)
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=swish
-
-
-# FPN-3
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=swish
-
-[upsample]
-stride=2
-
-[route]
-layers = 57
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=swish
-
-[route]
-layers = -1, -3
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=swish
-
-# Split
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=swish
-
-[route]
-layers = -2
-
-# Plain Block
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=160
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=160
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=160
-activation=swish
-
-# Merge [-1, -(2k+2)]
-
-[route]
-layers = -1, -8
-
-# Transition last
-
-# 165 (previous+6+4+2k)
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=swish
-
-
-# PAN-4
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=2
-pad=1
-filters=320
-activation=swish
-
-[route]
-layers = -1, 149
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=swish
-
-# Split
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=swish
-
-[route]
-layers = -2
-
-# Plain Block
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=320
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=320
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=320
-activation=swish
-
-[route]
-layers = -1,-8
-
-# Transition last
-
-# 178 (previous+3+4+2k)
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=swish
-
-
-# PAN-5
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=2
-pad=1
-filters=640
-activation=swish
-
-[route]
-layers = -1, 133
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=swish
-
-# Split
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=swish
-
-[route]
-layers = -2
-
-# Plain Block
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=640
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=640
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=640
-activation=swish
-
-[route]
-layers = -1,-8
-
-# Transition last
-
-# 191 (previous+3+4+2k)
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=swish
-stopbackward=1
-
-# ============ End of Neck ============ #
-
-# ============ Head ============ #
-
-# YOLO-3
-
-[route]
-layers = 165
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=320
-activation=swish
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=logistic
-
-[yolo]
-mask = 0,1,2
-anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
-classes=80
-num=9
-jitter=.1
-scale_x_y = 2.0
-objectness_smooth=1
-ignore_thresh = .7
-truth_thresh = 1
-#random=1
-resize=1.5
-#iou_thresh=0.2
-iou_normalizer=0.05
-cls_normalizer=0.5
-obj_normalizer=0.4
-iou_loss=ciou
-nms_kind=diounms
-beta_nms=0.6
-new_coords=1
-max_delta=2
-
-
-# YOLO-4
-
-[route]
-layers = 178
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=640
-activation=swish
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=logistic
-
-[yolo]
-mask = 3,4,5
-anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
-classes=80
-num=9
-jitter=.1
-scale_x_y = 2.0
-objectness_smooth=1
-ignore_thresh = .7
-truth_thresh = 1
-#random=1
-resize=1.5
-#iou_thresh=0.2
-iou_normalizer=0.05
-cls_normalizer=0.5
-obj_normalizer=0.4
-iou_loss=ciou
-nms_kind=diounms
-beta_nms=0.6
-new_coords=1
-max_delta=2
-
-
-# YOLO-5
-
-[route]
-layers = 191
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1280
-activation=swish
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=logistic
-
-[yolo]
-mask = 6,7,8
-anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
-classes=80
-num=9
-jitter=.1
-scale_x_y = 2.0
-objectness_smooth=1
-ignore_thresh = .7
-truth_thresh = 1
-#random=1
-resize=1.5
-#iou_thresh=0.2
-iou_normalizer=0.05
-cls_normalizer=0.5
-obj_normalizer=0.4
-iou_loss=ciou
-nms_kind=diounms
-beta_nms=0.6
-new_coords=1
-max_delta=2
diff --git a/build/darknet/x64/cfg/yolov4-csp-x-swish.cfg b/build/darknet/x64/cfg/yolov4-csp-x-swish.cfg
deleted file mode 100644
index 015db22c8b5..00000000000
--- a/build/darknet/x64/cfg/yolov4-csp-x-swish.cfg
+++ /dev/null
@@ -1,1556 +0,0 @@
-  
-[net]
-# Testing
-#batch=1
-#subdivisions=1
-# Training
-batch=64
-subdivisions=8
-width=640
-height=640
-channels=3
-momentum=0.949
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-learning_rate=0.001
-burn_in=1000
-max_batches = 500500
-policy=steps
-steps=400000,450000
-scales=.1,.1
-
-mosaic=1
-
-letter_box=1
-
-ema_alpha=0.9998
-
-#optimized_memory=1
-
-
-# ============ Backbone ============ #
-
-# Stem 
-
-# 0
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=swish
-
-# P1
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=3
-stride=2
-pad=1
-activation=swish
-
-# Residual Block
-
-[convolutional]
-batch_normalize=1
-filters=40
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=3
-stride=1
-pad=1
-activation=swish
-
-# 4 (previous+1+3k)
-[shortcut]
-from=-3
-activation=linear
-
-# P2
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=2
-pad=1
-activation=swish
-
-# Split
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=1
-stride=1
-pad=1
-activation=swish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=1
-stride=1
-pad=1
-activation=swish
-
-# Residual Block
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-# Transition first
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=1
-stride=1
-pad=1
-activation=swish
-
-# Merge [-1, -(3k+4)]
-
-[route]
-layers = -1,-13
-
-# Transition last
-
-# 20 (previous+7+3k)
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=swish
-
-# P3
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=2
-pad=1
-activation=swish
-
-# Split
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=swish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=swish
-
-# Residual Block
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-# Transition first
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=swish
-
-# Merge [-1 -(4+3k)]
-
-[route]
-layers = -1,-34
-
-# Transition last
-
-# 57 (previous+7+3k)
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=swish
-
-# P4
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=2
-pad=1
-activation=swish
-
-# Split
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=swish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=swish
-
-# Residual Block
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-# Transition first
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=swish
-
-# Merge [-1 -(3k+4)]
-
-[route]
-layers = -1,-34
-
-# Transition last
-
-# 94 (previous+7+3k)
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=swish
-
-# P5
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=1280
-size=3
-stride=2
-pad=1
-activation=swish
-
-# Split
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=swish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=swish
-
-# Residual Block
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=swish
-
-[shortcut]
-from=-3
-activation=linear
-
-# Transition first
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=swish
-
-# Merge [-1 -(3k+4)]
-
-[route]
-layers = -1,-19
-
-# Transition last
-
-# 116 (previous+7+3k)
-[convolutional]
-batch_normalize=1
-filters=1280
-size=1
-stride=1
-pad=1
-activation=swish
-
-# ============ End of Backbone ============ #
-
-# ============ Neck ============ #
-
-# CSPSPP
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=swish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=640
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=swish
-
-### SPP ###
-[maxpool]
-stride=1
-size=5
-
-[route]
-layers=-2
-
-[maxpool]
-stride=1
-size=9
-
-[route]
-layers=-4
-
-[maxpool]
-stride=1
-size=13
-
-[route]
-layers=-1,-3,-5,-6
-### End SPP ###
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=640
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=640
-activation=swish
-
-[route]
-layers = -1, -15
-
-# 133 (previous+6+5+2k)
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=swish
-
-# End of CSPSPP
-
-
-# FPN-4
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=swish
-
-[upsample]
-stride=2
-
-[route]
-layers = 94
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=swish
-
-[route]
-layers = -1, -3
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=swish
-
-# Split
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=swish
-
-[route]
-layers = -2
-
-# Plain Block
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=320
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=320
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=320
-activation=swish
-
-# Merge [-1, -(2k+2)]
-
-[route]
-layers = -1, -8
-
-# Transition last
-
-# 149 (previous+6+4+2k)
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=swish
-
-
-# FPN-3
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=swish
-
-[upsample]
-stride=2
-
-[route]
-layers = 57
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=swish
-
-[route]
-layers = -1, -3
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=swish
-
-# Split
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=swish
-
-[route]
-layers = -2
-
-# Plain Block
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=160
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=160
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=160
-activation=swish
-
-# Merge [-1, -(2k+2)]
-
-[route]
-layers = -1, -8
-
-# Transition last
-
-# 165 (previous+6+4+2k)
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=swish
-
-
-# PAN-4
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=2
-pad=1
-filters=320
-activation=swish
-
-[route]
-layers = -1, 149
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=swish
-
-# Split
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=swish
-
-[route]
-layers = -2
-
-# Plain Block
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=320
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=320
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=320
-activation=swish
-
-[route]
-layers = -1,-8
-
-# Transition last
-
-# 178 (previous+3+4+2k)
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=swish
-
-
-# PAN-5
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=2
-pad=1
-filters=640
-activation=swish
-
-[route]
-layers = -1, 133
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=swish
-
-# Split
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=swish
-
-[route]
-layers = -2
-
-# Plain Block
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=640
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=640
-activation=swish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=swish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=640
-activation=swish
-
-[route]
-layers = -1,-8
-
-# Transition last
-
-# 191 (previous+3+4+2k)
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=swish
-stopbackward=900
-
-# ============ End of Neck ============ #
-
-# ============ Head ============ #
-
-# YOLO-3
-
-[route]
-layers = 165
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=320
-activation=swish
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=logistic
-
-[yolo]
-mask = 0,1,2
-anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
-classes=80
-num=9
-jitter=.1
-scale_x_y = 2.0
-objectness_smooth=1
-ignore_thresh = .7
-truth_thresh = 1
-#random=1
-resize=1.5
-#iou_thresh=0.2
-iou_normalizer=0.05
-cls_normalizer=0.5
-obj_normalizer=0.4
-iou_loss=ciou
-nms_kind=diounms
-beta_nms=0.6
-new_coords=1
-max_delta=2
-
-
-# YOLO-4
-
-[route]
-layers = 178
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=640
-activation=swish
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=logistic
-
-[yolo]
-mask = 3,4,5
-anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
-classes=80
-num=9
-jitter=.1
-scale_x_y = 2.0
-objectness_smooth=1
-ignore_thresh = .7
-truth_thresh = 1
-#random=1
-resize=1.5
-#iou_thresh=0.2
-iou_normalizer=0.05
-cls_normalizer=0.5
-obj_normalizer=0.4
-iou_loss=ciou
-nms_kind=diounms
-beta_nms=0.6
-new_coords=1
-max_delta=2
-
-
-# YOLO-5
-
-[route]
-layers = 191
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1280
-activation=swish
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=logistic
-
-[yolo]
-mask = 6,7,8
-anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
-classes=80
-num=9
-jitter=.1
-scale_x_y = 2.0
-objectness_smooth=1
-ignore_thresh = .7
-truth_thresh = 1
-#random=1
-resize=1.5
-#iou_thresh=0.2
-iou_normalizer=0.05
-cls_normalizer=0.5
-obj_normalizer=0.4
-iou_loss=ciou
-nms_kind=diounms
-beta_nms=0.6
-new_coords=1
-max_delta=2
diff --git a/build/darknet/x64/cfg/yolov4-csp.cfg b/build/darknet/x64/cfg/yolov4-csp.cfg
deleted file mode 100644
index 691ec03b04a..00000000000
--- a/build/darknet/x64/cfg/yolov4-csp.cfg
+++ /dev/null
@@ -1,1279 +0,0 @@
-[net]
-# Testing
-#batch=1
-#subdivisions=1
-# Training
-batch=64
-subdivisions=8
-width=512
-height=512
-channels=3
-momentum=0.949
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-learning_rate=0.001
-burn_in=1000
-max_batches = 500500
-policy=steps
-steps=400000,450000
-scales=.1,.1
-
-mosaic=1
-
-letter_box=1
-
-ema_alpha=0.9998
-
-#optimized_memory=1
-
-#23:104x104 54:52x52 85:26x26 104:13x13 for 416
-
-
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=mish
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=2
-pad=1
-activation=mish
-
-#[convolutional]
-#batch_normalize=1
-#filters=64
-#size=1
-#stride=1
-#pad=1
-#activation=mish
-
-#[route]
-#layers = -2
-
-#[convolutional]
-#batch_normalize=1
-#filters=64
-#size=1
-#stride=1
-#pad=1
-#activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-#[convolutional]
-#batch_normalize=1
-#filters=64
-#size=1
-#stride=1
-#pad=1
-#activation=mish
-
-#[route]
-#layers = -1,-7
-
-#[convolutional]
-#batch_normalize=1
-#filters=64
-#size=1
-#stride=1
-#pad=1
-#activation=mish
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=2
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1,-10
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=2
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1,-28
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=2
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1,-28
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=2
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1,-16
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=mish
-
-##########################
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-### SPP ###
-[maxpool]
-stride=1
-size=5
-
-[route]
-layers=-2
-
-[maxpool]
-stride=1
-size=9
-
-[route]
-layers=-4
-
-[maxpool]
-stride=1
-size=13
-
-[route]
-layers=-1,-3,-5,-6
-### End SPP ###
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=mish
-
-[route]
-layers = -1, -13
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[upsample]
-stride=2
-
-[route]
-layers = 79
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1, -3
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=mish
-
-[route]
-layers = -1, -6
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[upsample]
-stride=2
-
-[route]
-layers = 48
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1, -3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=128
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=128
-activation=mish
-
-[route]
-layers = -1, -6
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-##########################
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=mish
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=logistic
-
-
-[yolo]
-mask = 0,1,2
-anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
-classes=80
-num=9
-jitter=.1
-scale_x_y = 2.0
-objectness_smooth=0
-ignore_thresh = .7
-truth_thresh = 1
-#random=1
-resize=1.5
-iou_thresh=0.2
-iou_normalizer=0.05
-cls_normalizer=0.5
-obj_normalizer=4.0
-iou_loss=ciou
-nms_kind=diounms
-beta_nms=0.6
-new_coords=1
-max_delta=5
-
-[route]
-layers = -4
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=2
-pad=1
-filters=256
-activation=mish
-
-[route]
-layers = -1, -20
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=mish
-
-[route]
-layers = -1,-6
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=mish
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=logistic
-
-
-[yolo]
-mask = 3,4,5
-anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
-classes=80
-num=9
-jitter=.1
-scale_x_y = 2.0
-objectness_smooth=1
-ignore_thresh = .7
-truth_thresh = 1
-#random=1
-resize=1.5
-iou_thresh=0.2
-iou_normalizer=0.05
-cls_normalizer=0.5
-obj_normalizer=1.0
-iou_loss=ciou
-nms_kind=diounms
-beta_nms=0.6
-new_coords=1
-max_delta=5
-
-[route]
-layers = -4
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=2
-pad=1
-filters=512
-activation=mish
-
-[route]
-layers = -1, -49
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=mish
-
-[route]
-layers = -1,-6
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=mish
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=logistic
-
-
-[yolo]
-mask = 6,7,8
-anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
-classes=80
-num=9
-jitter=.1
-scale_x_y = 2.0
-objectness_smooth=1
-ignore_thresh = .7
-truth_thresh = 1
-#random=1
-resize=1.5
-iou_thresh=0.2
-iou_normalizer=0.05
-cls_normalizer=0.5
-obj_normalizer=0.4
-iou_loss=ciou
-nms_kind=diounms
-beta_nms=0.6
-new_coords=1
-max_delta=2
diff --git a/build/darknet/x64/cfg/yolov4-custom.cfg b/build/darknet/x64/cfg/yolov4-custom.cfg
deleted file mode 100644
index 32516bdc120..00000000000
--- a/build/darknet/x64/cfg/yolov4-custom.cfg
+++ /dev/null
@@ -1,1160 +0,0 @@
-[net]
-# Testing
-#batch=1
-#subdivisions=1
-# Training
-batch=64
-subdivisions=16
-width=608
-height=608
-channels=3
-momentum=0.949
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-learning_rate=0.001
-burn_in=1000
-max_batches = 500500
-policy=steps
-steps=400000,450000
-scales=.1,.1
-
-#cutmix=1
-mosaic=1
-
-#:104x104 54:52x52 85:26x26 104:13x13 for 416
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=mish
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=2
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1,-7
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=2
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1,-10
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=2
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1,-28
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=2
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1,-28
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=2
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1,-16
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=mish
-stopbackward=800
-
-##########################
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-### SPP ###
-[maxpool]
-stride=1
-size=5
-
-[route]
-layers=-2
-
-[maxpool]
-stride=1
-size=9
-
-[route]
-layers=-4
-
-[maxpool]
-stride=1
-size=13
-
-[route]
-layers=-1,-3,-5,-6
-### End SPP ###
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[upsample]
-stride=2
-
-[route]
-layers = 85
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers = -1, -3
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[upsample]
-stride=2
-
-[route]
-layers = 54
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers = -1, -3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-##########################
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=linear
-
-
-[yolo]
-mask = 0,1,2
-anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
-classes=80
-num=9
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-scale_x_y = 1.2
-iou_thresh=0.213
-cls_normalizer=1.0
-iou_normalizer=0.07
-iou_loss=ciou
-nms_kind=greedynms
-beta_nms=0.6
-max_delta=5
-
-
-[route]
-layers = -4
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=2
-pad=1
-filters=256
-activation=leaky
-
-[route]
-layers = -1, -16
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=linear
-
-
-[yolo]
-mask = 3,4,5
-anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
-classes=80
-num=9
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-scale_x_y = 1.1
-iou_thresh=0.213
-cls_normalizer=1.0
-iou_normalizer=0.07
-iou_loss=ciou
-nms_kind=greedynms
-beta_nms=0.6
-max_delta=5
-
-
-[route]
-layers = -4
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=2
-pad=1
-filters=512
-activation=leaky
-
-[route]
-layers = -1, -37
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=linear
-
-
-[yolo]
-mask = 6,7,8
-anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
-classes=80
-num=9
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-random=1
-scale_x_y = 1.05
-iou_thresh=0.213
-cls_normalizer=1.0
-iou_normalizer=0.07
-iou_loss=ciou
-nms_kind=greedynms
-beta_nms=0.6
-max_delta=5
-
diff --git a/build/darknet/x64/cfg/yolov4-p5-frozen.cfg b/build/darknet/x64/cfg/yolov4-p5-frozen.cfg
deleted file mode 100644
index 38bebe32a21..00000000000
--- a/build/darknet/x64/cfg/yolov4-p5-frozen.cfg
+++ /dev/null
@@ -1,1838 +0,0 @@
-[net]
-# Testing
-#batch=1
-#subdivisions=1
-# Training
-batch=64
-subdivisions=8
-width=896
-height=896
-channels=3
-momentum=0.949
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-learning_rate=0.001
-burn_in=1000
-max_batches = 500500
-policy=steps
-steps=400000,450000
-scales=.1,.1
-
-mosaic=1
-
-letter_box=1
-
-ema_alpha=0.9998
-
-#use_cuda_graph = 1
-
-
-# ============ Backbone ============ #
-
-# Stem 
-
-# 0
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=mish
-
-
-# P1
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=2
-pad=1
-activation=mish
-
-# Split
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Residual Block
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-# Transition first
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Merge [-1, -(3k+4)]
-
-[route]
-layers = -1,-7
-
-# Transition last
-
-# 10 (previous+7+3k)
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-
-# P2
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=2
-pad=1
-activation=mish
-
-# Split
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Residual Block
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-# Transition first
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Merge [-1, -(3k+4)]
-
-[route]
-layers = -1,-13
-
-# Transition last
-
-# 26 (previous+7+3k)
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-
-# P3
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=2
-pad=1
-activation=mish
-
-# Split
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Residual Block
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-# Transition first
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Merge [-1, -(3k+4)]
-
-[route]
-layers = -1,-49
-
-# Transition last
-
-# 78 (previous+7+3k)
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-
-# P4
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=2
-pad=1
-activation=mish
-
-# Split
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Residual Block
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-# Transition first
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Merge [-1, -(3k+4)]
-
-[route]
-layers = -1,-49
-
-# Transition last
-
-# 130 (previous+7+3k)
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-
-# P5
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=2
-pad=1
-activation=mish
-
-# Split
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Residual Block
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-# Transition first
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Merge [-1, -(3k+4)]
-
-[route]
-layers = -1,-25
-
-# Transition last
-
-# 158 (previous+7+3k)
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=mish
-
-# ============ End of Backbone ============ #
-
-# ============ Neck ============ #
-
-# CSPSPP
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-### SPP ###
-[maxpool]
-stride=1
-size=5
-
-[route]
-layers=-2
-
-[maxpool]
-stride=1
-size=9
-
-[route]
-layers=-4
-
-[maxpool]
-stride=1
-size=13
-
-[route]
-layers=-1,-3,-5,-6
-### End SPP ###
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=mish
-
-[route]
-layers = -1, -13
-
-# 173 (previous+6+5+2k)
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-# End of CSPSPP
-
-
-# FPN-4
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[upsample]
-stride=2
-
-[route]
-layers = 130
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1, -3
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Split
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-# Plain Block
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=mish
-
-# Merge [-1, -(2k+2)]
-
-[route]
-layers = -1, -8
-
-# Transition last
-
-# 189 (previous+6+4+2k)
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-
-# FPN-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[upsample]
-stride=2
-
-[route]
-layers = 78
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1, -3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Split
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-# Plain Block
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=128
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=128
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=128
-activation=mish
-
-# Merge [-1, -(2k+2)]
-
-[route]
-layers = -1, -8
-
-# Transition last
-
-# 205 (previous+6+4+2k)
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-
-# PAN-4
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=2
-pad=1
-filters=256
-activation=mish
-
-[route]
-layers = -1, 189
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Split
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-# Plain Block
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=mish
-
-[route]
-layers = -1,-8
-
-# Transition last
-
-# 218 (previous+3+4+2k)
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-
-# PAN-5
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=2
-pad=1
-filters=512
-activation=mish
-
-[route]
-layers = -1, 173
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Split
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-# Plain Block
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=mish
-
-[route]
-layers = -1,-8
-
-# Transition last
-
-# 231 (previous+3+4+2k)
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-stopbackward=1
-
-# ============ End of Neck ============ #
-
-# ============ Head ============ #
-
-# YOLO-3
-
-[route]
-layers = 205
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=mish
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=340
-activation=logistic
-#activation=linear
-# use linear for Pytorch-Scaled-YOLOv4, and logistic for Darknet
-
-[yolo]
-mask = 0,1,2,3
-anchors = 13,17,  31,25,  24,51,  61,45,  48,102,  119,96, 97,189, 217,184,  171,384, 324,451, 616,618, 800,800
-classes=80
-num=12
-jitter=.1
-scale_x_y = 2.0
-objectness_smooth=1
-ignore_thresh = .7
-truth_thresh = 1
-#random=1
-resize=1.5
-iou_thresh=0.2
-iou_normalizer=0.05
-cls_normalizer=0.5
-obj_normalizer=1.0
-iou_loss=ciou
-nms_kind=diounms
-beta_nms=0.6
-new_coords=1
-max_delta=2
-
-
-# YOLO-4
-
-[route]
-layers = 218
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=mish
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=340
-activation=logistic
-#activation=linear
-# use linear for Pytorch-Scaled-YOLOv4, and logistic for Darknet
-
-[yolo]
-mask = 4,5,6,7
-anchors = 13,17,  31,25,  24,51,  61,45,  48,102,  119,96, 97,189, 217,184,  171,384, 324,451, 616,618, 800,800
-classes=80
-num=12
-jitter=.1
-scale_x_y = 2.0
-objectness_smooth=1
-ignore_thresh = .7
-truth_thresh = 1
-#random=1
-resize=1.5
-iou_thresh=0.2
-iou_normalizer=0.05
-cls_normalizer=0.5
-obj_normalizer=1.0
-iou_loss=ciou
-nms_kind=diounms
-beta_nms=0.6
-new_coords=1
-max_delta=2
-
-
-# YOLO-5
-
-[route]
-layers = 231
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=mish
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=340
-activation=logistic
-#activation=linear
-# use linear for Pytorch-Scaled-YOLOv4, and logistic for Darknet
-
-[yolo]
-mask = 8,9,10,11
-anchors = 13,17,  31,25,  24,51,  61,45,  48,102,  119,96, 97,189, 217,184,  171,384, 324,451, 616,618, 800,800
-classes=80
-num=12
-jitter=.1
-scale_x_y = 2.0
-objectness_smooth=1
-ignore_thresh = .7
-truth_thresh = 1
-#random=1
-resize=1.5
-iou_thresh=0.2
-iou_normalizer=0.05
-cls_normalizer=0.5
-obj_normalizer=1.0
-iou_loss=ciou
-nms_kind=diounms
-beta_nms=0.6
-new_coords=1
-max_delta=2
-
-# ============ End of Head ============ #
\ No newline at end of file
diff --git a/build/darknet/x64/cfg/yolov4-p5.cfg b/build/darknet/x64/cfg/yolov4-p5.cfg
deleted file mode 100644
index 14bce30ebd2..00000000000
--- a/build/darknet/x64/cfg/yolov4-p5.cfg
+++ /dev/null
@@ -1,1837 +0,0 @@
-[net]
-# Testing
-#batch=1
-#subdivisions=1
-# Training
-batch=64
-subdivisions=8
-width=896
-height=896
-channels=3
-momentum=0.949
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-learning_rate=0.001
-burn_in=1000
-max_batches = 500500
-policy=steps
-steps=400000,450000
-scales=.1,.1
-
-mosaic=1
-
-letter_box=1
-
-ema_alpha=0.9998
-
-#use_cuda_graph = 1
-
-
-# ============ Backbone ============ #
-
-# Stem 
-
-# 0
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=mish
-
-
-# P1
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=2
-pad=1
-activation=mish
-
-# Split
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Residual Block
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-# Transition first
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Merge [-1, -(3k+4)]
-
-[route]
-layers = -1,-7
-
-# Transition last
-
-# 10 (previous+7+3k)
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-
-# P2
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=2
-pad=1
-activation=mish
-
-# Split
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Residual Block
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-# Transition first
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Merge [-1, -(3k+4)]
-
-[route]
-layers = -1,-13
-
-# Transition last
-
-# 26 (previous+7+3k)
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-
-# P3
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=2
-pad=1
-activation=mish
-
-# Split
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Residual Block
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-# Transition first
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Merge [-1, -(3k+4)]
-
-[route]
-layers = -1,-49
-
-# Transition last
-
-# 78 (previous+7+3k)
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-
-# P4
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=2
-pad=1
-activation=mish
-
-# Split
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Residual Block
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-# Transition first
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Merge [-1, -(3k+4)]
-
-[route]
-layers = -1,-49
-
-# Transition last
-
-# 130 (previous+7+3k)
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-
-# P5
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=2
-pad=1
-activation=mish
-
-# Split
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Residual Block
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-# Transition first
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Merge [-1, -(3k+4)]
-
-[route]
-layers = -1,-25
-
-# Transition last
-
-# 158 (previous+7+3k)
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=mish
-
-# ============ End of Backbone ============ #
-
-# ============ Neck ============ #
-
-# CSPSPP
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-### SPP ###
-[maxpool]
-stride=1
-size=5
-
-[route]
-layers=-2
-
-[maxpool]
-stride=1
-size=9
-
-[route]
-layers=-4
-
-[maxpool]
-stride=1
-size=13
-
-[route]
-layers=-1,-3,-5,-6
-### End SPP ###
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=mish
-
-[route]
-layers = -1, -13
-
-# 173 (previous+6+5+2k)
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-# End of CSPSPP
-
-
-# FPN-4
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[upsample]
-stride=2
-
-[route]
-layers = 130
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1, -3
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Split
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-# Plain Block
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=mish
-
-# Merge [-1, -(2k+2)]
-
-[route]
-layers = -1, -8
-
-# Transition last
-
-# 189 (previous+6+4+2k)
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-
-# FPN-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[upsample]
-stride=2
-
-[route]
-layers = 78
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1, -3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Split
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-# Plain Block
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=128
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=128
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=128
-activation=mish
-
-# Merge [-1, -(2k+2)]
-
-[route]
-layers = -1, -8
-
-# Transition last
-
-# 205 (previous+6+4+2k)
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-
-# PAN-4
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=2
-pad=1
-filters=256
-activation=mish
-
-[route]
-layers = -1, 189
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Split
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-# Plain Block
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=mish
-
-[route]
-layers = -1,-8
-
-# Transition last
-
-# 218 (previous+3+4+2k)
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-
-# PAN-5
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=2
-pad=1
-filters=512
-activation=mish
-
-[route]
-layers = -1, 173
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Split
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-# Plain Block
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=mish
-
-[route]
-layers = -1,-8
-
-# Transition last
-
-# 231 (previous+3+4+2k)
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-# ============ End of Neck ============ #
-
-# ============ Head ============ #
-
-# YOLO-3
-
-[route]
-layers = 205
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=mish
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=340
-activation=logistic
-#activation=linear
-# use linear for Pytorch-Scaled-YOLOv4, and logistic for Darknet
-
-[yolo]
-mask = 0,1,2,3
-anchors = 13,17,  31,25,  24,51,  61,45,  48,102,  119,96, 97,189, 217,184,  171,384, 324,451, 616,618, 800,800
-classes=80
-num=12
-jitter=.1
-scale_x_y = 2.0
-objectness_smooth=1
-ignore_thresh = .7
-truth_thresh = 1
-#random=1
-resize=1.5
-iou_thresh=0.2
-iou_normalizer=0.05
-cls_normalizer=0.5
-obj_normalizer=1.0
-iou_loss=ciou
-nms_kind=diounms
-beta_nms=0.6
-new_coords=1
-max_delta=2
-
-
-# YOLO-4
-
-[route]
-layers = 218
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=mish
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=340
-activation=logistic
-#activation=linear
-# use linear for Pytorch-Scaled-YOLOv4, and logistic for Darknet
-
-[yolo]
-mask = 4,5,6,7
-anchors = 13,17,  31,25,  24,51,  61,45,  48,102,  119,96, 97,189, 217,184,  171,384, 324,451, 616,618, 800,800
-classes=80
-num=12
-jitter=.1
-scale_x_y = 2.0
-objectness_smooth=1
-ignore_thresh = .7
-truth_thresh = 1
-#random=1
-resize=1.5
-iou_thresh=0.2
-iou_normalizer=0.05
-cls_normalizer=0.5
-obj_normalizer=1.0
-iou_loss=ciou
-nms_kind=diounms
-beta_nms=0.6
-new_coords=1
-max_delta=2
-
-
-# YOLO-5
-
-[route]
-layers = 231
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=mish
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=340
-activation=logistic
-#activation=linear
-# use linear for Pytorch-Scaled-YOLOv4, and logistic for Darknet
-
-[yolo]
-mask = 8,9,10,11
-anchors = 13,17,  31,25,  24,51,  61,45,  48,102,  119,96, 97,189, 217,184,  171,384, 324,451, 616,618, 800,800
-classes=80
-num=12
-jitter=.1
-scale_x_y = 2.0
-objectness_smooth=1
-ignore_thresh = .7
-truth_thresh = 1
-#random=1
-resize=1.5
-iou_thresh=0.2
-iou_normalizer=0.05
-cls_normalizer=0.5
-obj_normalizer=1.0
-iou_loss=ciou
-nms_kind=diounms
-beta_nms=0.6
-new_coords=1
-max_delta=2
-
-# ============ End of Head ============ #
\ No newline at end of file
diff --git a/build/darknet/x64/cfg/yolov4-p6.cfg b/build/darknet/x64/cfg/yolov4-p6.cfg
deleted file mode 100644
index 8defa150b6f..00000000000
--- a/build/darknet/x64/cfg/yolov4-p6.cfg
+++ /dev/null
@@ -1,2298 +0,0 @@
-[net]
-# Testing
-#batch=1
-#subdivisions=1
-# Training
-batch=64
-subdivisions=8
-width=1280
-height=1280
-channels=3
-momentum=0.949
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-learning_rate=0.001
-burn_in=1000
-max_batches = 500500
-policy=steps
-steps=400000,450000
-scales=.1,.1
-
-mosaic=1
-
-letter_box=1
-
-ema_alpha=0.9998
-
-#use_cuda_graph = 1
-
-
-# ============ Backbone ============ #
-
-# Stem 
-
-# 0
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=mish
-
-
-# P1
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=2
-pad=1
-activation=mish
-
-# Split
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Residual Block
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-# Transition first
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Merge [-1, -(3k+4)]
-
-[route]
-layers = -1,-7
-
-# Transition last
-
-# 10 (previous+7+3k)
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-
-# P2
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=2
-pad=1
-activation=mish
-
-# Split
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Residual Block
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-# Transition first
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Merge [-1, -(3k+4)]
-
-[route]
-layers = -1,-13
-
-# Transition last
-
-# 26 (previous+7+3k)
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-
-# P3
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=2
-pad=1
-activation=mish
-
-# Split
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Residual Block
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-# Transition first
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Merge [-1, -(3k+4)]
-
-[route]
-layers = -1,-49
-
-# Transition last
-
-# 78 (previous+7+3k)
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-
-# P4
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=2
-pad=1
-activation=mish
-
-# Split
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Residual Block
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-# Transition first
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Merge [-1, -(3k+4)]
-
-[route]
-layers = -1,-49
-
-# Transition last
-
-# 130 (previous+7+3k)
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-
-# P5
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=2
-pad=1
-activation=mish
-
-# Split
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Residual Block
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-# Transition first
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Merge [-1, -(3k+4)]
-
-[route]
-layers = -1,-25
-
-# Transition last
-
-# 158 (previous+7+3k)
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=mish
-
-
-# P6
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=2
-pad=1
-activation=mish
-
-# Split
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Residual Block
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-# Transition first
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Merge [-1, -(3k+4)]
-
-[route]
-layers = -1,-25
-
-# Transition last
-
-# 186 (previous+7+3k)
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=mish
-
-# ============ End of Backbone ============ #
-
-# ============ Neck ============ #
-
-# CSPSPP
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-### SPP ###
-[maxpool]
-stride=1
-size=5
-
-[route]
-layers=-2
-
-[maxpool]
-stride=1
-size=9
-
-[route]
-layers=-4
-
-[maxpool]
-stride=1
-size=13
-
-[route]
-layers=-1,-3,-5,-6
-### End SPP ###
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=mish
-
-[route]
-layers = -1, -13
-
-# 201 (previous+6+5+2k)
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-# End of CSPSPP
-
-
-# FPN-5
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[upsample]
-stride=2
-
-[route]
-layers = 158
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1, -3
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Split
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-# Plain Block
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=mish
-
-# Merge [-1, -(2k+2)]
-
-[route]
-layers = -1, -8
-
-# Transition last
-
-# 217 (previous+6+4+2k)
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-
-# FPN-4
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[upsample]
-stride=2
-
-[route]
-layers = 130
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1, -3
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Split
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-# Plain Block
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=mish
-
-# Merge [-1, -(2k+2)]
-
-[route]
-layers = -1, -8
-
-# Transition last
-
-# 233 (previous+6+4+2k)
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-
-# FPN-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[upsample]
-stride=2
-
-[route]
-layers = 78
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1, -3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Split
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-# Plain Block
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=128
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=128
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=128
-activation=mish
-
-# Merge [-1, -(2k+2)]
-
-[route]
-layers = -1, -8
-
-# Transition last
-
-# 249 (previous+6+4+2k)
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-
-# PAN-4
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=2
-pad=1
-filters=256
-activation=mish
-
-[route]
-layers = -1, 233
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Split
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-# Plain Block
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=mish
-
-[route]
-layers = -1,-8
-
-# Transition last
-
-# 262 (previous+3+4+2k)
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-
-# PAN-5
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=2
-pad=1
-filters=512
-activation=mish
-
-[route]
-layers = -1, 217
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Split
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-# Plain Block
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=mish
-
-[route]
-layers = -1,-8
-
-# Transition last
-
-# 275 (previous+3+4+2k)
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-
-# PAN-6
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=2
-pad=1
-filters=512
-activation=mish
-
-[route]
-layers = -1, 201
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Split
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-# Plain Block
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=mish
-
-[route]
-layers = -1,-8
-
-# Transition last
-
-# 288 (previous+3+4+2k)
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-# ============ End of Neck ============ #
-
-# ============ Head ============ #
-
-# YOLO-3
-
-[route]
-layers = 249
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=mish
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=340
-activation=logistic
-#activation=linear
-# use linear for Pytorch-Scaled-YOLOv4, and logistic for Darknet
-
-[yolo]
-mask = 0,1,2,3
-anchors = 13,17,  31,25,  24,51, 61,45,  61,45,  48,102,  119,96,  97,189,  97,189,  217,184,  171,384,  324,451,  324,451, 545,357, 616,618, 1024,1024
-classes=80
-num=16
-jitter=.1
-scale_x_y = 2.0
-objectness_smooth=1
-ignore_thresh = .7
-truth_thresh = 1
-#random=1
-resize=1.5
-iou_thresh=0.2
-iou_normalizer=0.05
-cls_normalizer=0.5
-obj_normalizer=1.0
-iou_loss=ciou
-nms_kind=diounms
-beta_nms=0.6
-new_coords=1
-max_delta=2
-
-
-# YOLO-4
-
-[route]
-layers = 262
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=mish
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=340
-activation=logistic
-#activation=linear
-# use linear for Pytorch-Scaled-YOLOv4, and logistic for Darknet
-
-[yolo]
-mask = 4,5,6,7
-anchors = 13,17,  31,25,  24,51, 61,45,  61,45,  48,102,  119,96,  97,189,  97,189,  217,184,  171,384,  324,451,  324,451, 545,357, 616,618, 1024,1024
-classes=80
-num=16
-jitter=.1
-scale_x_y = 2.0
-objectness_smooth=1
-ignore_thresh = .7
-truth_thresh = 1
-#random=1
-resize=1.5
-iou_thresh=0.2
-iou_normalizer=0.05
-cls_normalizer=0.5
-obj_normalizer=1.0
-iou_loss=ciou
-nms_kind=diounms
-beta_nms=0.6
-new_coords=1
-max_delta=2
-
-
-# YOLO-5
-
-[route]
-layers = 275
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=mish
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=340
-activation=logistic
-#activation=linear
-# use linear for Pytorch-Scaled-YOLOv4, and logistic for Darknet
-
-[yolo]
-mask = 8,9,10,11
-anchors = 13,17,  31,25,  24,51, 61,45,  61,45,  48,102,  119,96,  97,189,  97,189,  217,184,  171,384,  324,451,  324,451, 545,357, 616,618, 1024,1024
-classes=80
-num=16
-jitter=.1
-scale_x_y = 2.0
-objectness_smooth=1
-ignore_thresh = .7
-truth_thresh = 1
-#random=1
-resize=1.5
-iou_thresh=0.2
-iou_normalizer=0.05
-cls_normalizer=0.5
-obj_normalizer=1.0
-iou_loss=ciou
-nms_kind=diounms
-beta_nms=0.6
-new_coords=1
-max_delta=2
-
-
-# YOLO-6
-
-[route]
-layers = 288
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=mish
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=340
-activation=logistic
-#activation=linear
-# use linear for Pytorch-Scaled-YOLOv4, and logistic for Darknet
-
-[yolo]
-mask = 12,13,14,15
-anchors = 13,17,  31,25,  24,51, 61,45,  61,45,  48,102,  119,96,  97,189,  97,189,  217,184,  171,384,  324,451,  324,451, 545,357, 616,618, 1024,1024
-classes=80
-num=16
-jitter=.1
-scale_x_y = 2.0
-objectness_smooth=1
-ignore_thresh = .7
-truth_thresh = 1
-#random=1
-resize=1.5
-iou_thresh=0.2
-iou_normalizer=0.05
-cls_normalizer=0.5
-obj_normalizer=1.0
-iou_loss=ciou
-nms_kind=diounms
-beta_nms=0.6
-new_coords=1
-max_delta=2
-
-# ============ End of Head ============ #
\ No newline at end of file
diff --git a/build/darknet/x64/cfg/yolov4-sam-mish-csp-reorg-bfm.cfg b/build/darknet/x64/cfg/yolov4-sam-mish-csp-reorg-bfm.cfg
deleted file mode 100644
index 1461d88838e..00000000000
--- a/build/darknet/x64/cfg/yolov4-sam-mish-csp-reorg-bfm.cfg
+++ /dev/null
@@ -1,1429 +0,0 @@
-[net]
-# Testing
-#batch=1
-#subdivisions=1
-# Training
-batch=64
-subdivisions=8
-width=512
-height=512
-channels=3
-momentum=0.949
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-learning_rate=0.001
-burn_in=1000
-max_batches = 500500
-policy=steps
-steps=400000,450000
-scales=.1,.1
-
-mosaic=1
-
-letter_box=1
-
-#:104x104 54:52x52 85:26x26 104:13x13 for 416
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=mish
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=2
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1,-7
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=2
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1,-10
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=2
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1,-28
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=2
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1,-28
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=2
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1,-16
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=mish
-stopbackward=800
-
-
-##########################
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-### SPP ###
-[maxpool]
-stride=1
-size=5
-
-[route]
-layers=-2
-
-[maxpool]
-stride=1
-size=9
-
-[route]
-layers=-4
-
-[maxpool]
-stride=1
-size=13
-
-[route]
-layers=-1,-3,-5,-6
-### End SPP ###
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=mish
-
-[route]
-layers = -1, -13
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=2
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[upsample]
-stride=2
-
-[route]
-layers = 79
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[reorg3d]
-stride=2
-
-[route]
-layers = -1, -4, -7
-
-[upsample]
-stride=2
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[upsample]
-stride=2
-
-[route]
-layers = 79
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = 48
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[reorg3d]
-stride=2
-
-[route]
-layers = -1, -4, -6
-
-[shortcut]
-from= -10
-activation=linear
-
-[upsample]
-stride=2
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=mish
-
-[route]
-layers = -1, -6
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[upsample]
-stride=2
-
-[route]
-layers = 48
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = 17
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-[reorg3d]
-stride=2
-
-[route]
-layers = -1, -4, -6
-
-[shortcut]
-from= -19
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-#### BFM-1
-
-[route]
-layers = 17
-
-[reorg3d]
-stride=2
-
-[route]
-layers = -1, -3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=128
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=128
-activation=mish
-
-[route]
-layers = -1, -6
-
-[convolutional]
-batch_normalize=1
-size=1
-stride=1
-pad=1
-filters=256
-activation=logistic
-
-[sam]
-from=-2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-
-##########################
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=mish
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=linear
-
-
-[yolo]
-mask = 0,1,2
-anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
-classes=80
-num=9
-jitter=.2
-ignore_thresh = .7
-truth_thresh = 1
-random=0
-resize=1.5
-scale_x_y = 1.2
-iou_thresh=0.213
-cls_normalizer=1.0
-iou_normalizer=0.07
-uc_normalizer=0.07
-iou_loss=ciou
-nms_kind=greedynms
-beta_nms=0.6
-beta1=0.6
-max_delta=5
-
-[route]
-layers = -4
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=2
-pad=1
-filters=256
-activation=mish
-
-[route]
-layers = -1, 137
-#layers = -1, -20
-
-[route]
-layers = -17
-
-[reorg3d]
-stride=2
-
-[route]
-layers = -1, -3
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=mish
-
-[route]
-layers = -1,-6
-
-[convolutional]
-batch_normalize=1
-size=1
-stride=1
-pad=1
-filters=512
-activation=logistic
-
-[sam]
-from=-2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=mish
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=linear
-
-
-[yolo]
-mask = 3,4,5
-anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
-classes=80
-num=9
-jitter=.2
-ignore_thresh = .7
-truth_thresh = 1
-random=0
-resize=1.5
-scale_x_y = 1.1
-iou_thresh=0.213
-cls_normalizer=1.0
-iou_normalizer=0.07
-uc_normalizer=0.07
-iou_loss=ciou
-nms_kind=greedynms
-beta_nms=0.6
-beta1=0.6
-max_delta=5
-
-[route]
-layers = -4
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=2
-pad=1
-filters=512
-activation=mish
-
-[route]
-layers = -1, 126
-# layers = -1, -49 
-
-[route]
-layers = -17
-
-[reorg3d]
-stride=2
-
-[route]
-layers = -1, -3
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=mish
-
-[route]
-layers = -1,-6
-
-[convolutional]
-batch_normalize=1
-size=1
-stride=1
-pad=1
-filters=1024
-activation=logistic
-
-[sam]
-from=-2
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=mish
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=linear
-
-
-[yolo]
-mask = 6,7,8
-anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
-classes=80
-num=9
-jitter=.1
-ignore_thresh = .7
-truth_thresh = 1
-random=0
-resize=1.5
-scale_x_y = 1.05
-iou_thresh=0.213
-cls_normalizer=1.0
-iou_normalizer=0.07
-uc_normalizer=0.07
-iou_loss=ciou
-nms_kind=greedynms
-beta_nms=0.6
-beta1=0.6
-max_delta=5
diff --git a/build/darknet/x64/cfg/yolov4-tiny-3l.cfg b/build/darknet/x64/cfg/yolov4-tiny-3l.cfg
deleted file mode 100644
index 116407066a3..00000000000
--- a/build/darknet/x64/cfg/yolov4-tiny-3l.cfg
+++ /dev/null
@@ -1,332 +0,0 @@
-[net]
-# Testing
-#batch=1
-#subdivisions=1
-# Training
-batch=64
-subdivisions=1
-width=608
-height=608
-channels=3
-momentum=0.9
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-learning_rate=0.00261
-burn_in=1000
-max_batches = 500200
-policy=steps
-steps=400000,450000
-scales=.1,.1
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1
-groups=2
-group_id=1
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers = -1,-2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers = -6,-1
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1
-groups=2
-group_id=1
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers = -1,-2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers = -6,-1
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1
-groups=2
-group_id=1
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers = -1,-2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers = -6,-1
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-##################################
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=linear
-
-
-
-[yolo]
-mask = 6,7,8
-anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
-classes=80
-num=9
-jitter=.3
-scale_x_y = 1.05
-cls_normalizer=1.0
-iou_normalizer=0.07
-iou_loss=ciou
-ignore_thresh = .7
-truth_thresh = 1
-random=0
-resize=1.5
-nms_kind=greedynms
-beta_nms=0.6
-
-[route]
-layers = -4
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[upsample]
-stride=2
-
-[route]
-layers = -1, 23
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=linear
-
-[yolo]
-mask = 3,4,5
-anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
-classes=80
-num=9
-jitter=.3
-scale_x_y = 1.05
-cls_normalizer=1.0
-iou_normalizer=0.07
-iou_loss=ciou
-ignore_thresh = .7
-truth_thresh = 1
-random=0
-resize=1.5
-nms_kind=greedynms
-beta_nms=0.6
-
-
-[route]
-layers = -3
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[upsample]
-stride=2
-
-[route]
-layers = -1, 15
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=linear
-
-[yolo]
-mask = 0,1,2
-anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
-classes=80
-num=9
-jitter=.3
-scale_x_y = 1.05
-cls_normalizer=1.0
-iou_normalizer=0.07
-iou_loss=ciou
-ignore_thresh = .7
-truth_thresh = 1
-random=0
-resize=1.5
-nms_kind=greedynms
-beta_nms=0.6
-
diff --git a/build/darknet/x64/cfg/yolov4-tiny-custom.cfg b/build/darknet/x64/cfg/yolov4-tiny-custom.cfg
deleted file mode 100644
index 5c83be0e19c..00000000000
--- a/build/darknet/x64/cfg/yolov4-tiny-custom.cfg
+++ /dev/null
@@ -1,281 +0,0 @@
-[net]
-# Testing
-#batch=1
-#subdivisions=1
-# Training
-batch=64
-subdivisions=1
-width=416
-height=416
-channels=3
-momentum=0.9
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-learning_rate=0.00261
-burn_in=1000
-max_batches = 500200
-policy=steps
-steps=400000,450000
-scales=.1,.1
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1
-groups=2
-group_id=1
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers = -1,-2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers = -6,-1
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1
-groups=2
-group_id=1
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers = -1,-2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers = -6,-1
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1
-groups=2
-group_id=1
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers = -1,-2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers = -6,-1
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-##################################
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=linear
-
-
-
-[yolo]
-mask = 3,4,5
-anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319
-classes=80
-num=6
-jitter=.3
-scale_x_y = 1.05
-cls_normalizer=1.0
-iou_normalizer=0.07
-iou_loss=ciou
-ignore_thresh = .7
-truth_thresh = 1
-random=0
-resize=1.5
-nms_kind=greedynms
-beta_nms=0.6
-
-[route]
-layers = -4
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[upsample]
-stride=2
-
-[route]
-layers = -1, 23
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=linear
-
-[yolo]
-mask = 0,1,2
-anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319
-classes=80
-num=6
-jitter=.3
-scale_x_y = 1.05
-cls_normalizer=1.0
-iou_normalizer=0.07
-iou_loss=ciou
-ignore_thresh = .7
-truth_thresh = 1
-random=0
-resize=1.5
-nms_kind=greedynms
-beta_nms=0.6
diff --git a/build/darknet/x64/cfg/yolov4-tiny.cfg b/build/darknet/x64/cfg/yolov4-tiny.cfg
deleted file mode 100644
index d990b5134d8..00000000000
--- a/build/darknet/x64/cfg/yolov4-tiny.cfg
+++ /dev/null
@@ -1,294 +0,0 @@
-[net]
-# Testing
-#batch=1
-#subdivisions=1
-# Training
-batch=64
-subdivisions=1
-width=416
-height=416
-channels=3
-momentum=0.9
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-learning_rate=0.00261
-burn_in=1000
-
-max_batches = 2000200
-policy=steps
-steps=1600000,1800000
-scales=.1,.1
-
-
-#weights_reject_freq=1001
-#ema_alpha=0.9998
-#equidistant_point=1000
-#num_sigmas_reject_badlabels=3
-#badlabels_rejection_percentage=0.2
-
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1
-groups=2
-group_id=1
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers = -1,-2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers = -6,-1
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1
-groups=2
-group_id=1
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers = -1,-2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers = -6,-1
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1
-groups=2
-group_id=1
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers = -1,-2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers = -6,-1
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-##################################
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=linear
-
-
-
-[yolo]
-mask = 3,4,5
-anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319
-classes=80
-num=6
-jitter=.3
-scale_x_y = 1.05
-cls_normalizer=1.0
-iou_normalizer=0.07
-iou_loss=ciou
-ignore_thresh = .7
-truth_thresh = 1
-random=0
-resize=1.5
-nms_kind=greedynms
-beta_nms=0.6
-#new_coords=1
-#scale_x_y = 2.0
-
-[route]
-layers = -4
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[upsample]
-stride=2
-
-[route]
-layers = -1, 23
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=linear
-
-[yolo]
-mask = 1,2,3
-anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319
-classes=80
-num=6
-jitter=.3
-scale_x_y = 1.05
-cls_normalizer=1.0
-iou_normalizer=0.07
-iou_loss=ciou
-ignore_thresh = .7
-truth_thresh = 1
-random=0
-resize=1.5
-nms_kind=greedynms
-beta_nms=0.6
-#new_coords=1
-#scale_x_y = 2.0
diff --git a/build/darknet/x64/cfg/yolov4-tiny_contrastive.cfg b/build/darknet/x64/cfg/yolov4-tiny_contrastive.cfg
deleted file mode 100644
index 1fed0433cf0..00000000000
--- a/build/darknet/x64/cfg/yolov4-tiny_contrastive.cfg
+++ /dev/null
@@ -1,452 +0,0 @@
-[net]
-# Testing
-#batch=1
-#subdivisions=1
-# Training
-batch=64
-subdivisions=2
-width=416
-height=416
-channels=3
-momentum=0.9
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-contrastive=1
-contrastive_jit_flip=1
-contrastive_color=0
-
-learning_rate=0.00261
-burn_in=1000
-max_batches = 500200
-policy=steps
-steps=400000,450000
-scales=.1,.1
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1
-groups=2
-group_id=1
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers = -1,-2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers = -6,-1
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1
-groups=2
-group_id=1
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers = -1,-2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers = -6,-1
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1
-groups=2
-group_id=1
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers = -1,-2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers = -6,-1
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-##################################
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-#[conv_lstm]
-#batch_normalize=1
-#size=3
-#pad=1
-#output=128
-#groups=1
-#peephole=0
-#bottleneck=1
-##time_normalizer=0.5
-#lstm_activation=tanh
-#activation=leaky
-
-
-###########
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-
-########### to [yolo-3]
-
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[upsample]
-stride=2
-
-[route]
-layers = -1, 23
-
-###########
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-#[conv_lstm]
-#batch_normalize=1
-#size=3
-#pad=1
-#output=128
-#groups=1
-#peephole=0
-#bottleneck=1
-##time_normalizer=0.5
-#lstm_activation=tanh
-#activation=leaky
-
-###########
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-########### to [yolo-2]
-
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[upsample]
-stride=2
-
-[route]
-layers = -1, 15
-
-###########
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-#[conv_lstm]
-#batch_normalize=1
-#size=3
-#pad=1
-#output=64
-#groups=1
-#peephole=0
-#bottleneck=1
-##time_normalizer=0.5
-#lstm_activation=tanh
-#activation=leaky
-
-###########
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers= 0
-onlyforward=1
-
-[local_avgpool]
-size=4
-stride=4
-
-[route]
-layers= -1,-3
-onlyforward=1
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-# Contrastive embeddings
-[convolutional]
-batch_normalize=1
-filters=1152
-size=1
-stride=1
-pad=1
-#activation=tanh
-#activation=leaky
-activation=linear
-
-[route]
-layers= -9
-#onlyforward=1
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=765
-activation=linear
-
-
-
-[yolo]
-mask = 0,1,2,3,4,5,6,7,8
-anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
-classes=80
-num=9
-jitter=.3
-scale_x_y = 1.05
-cls_normalizer=1.0
-iou_normalizer=0.07
-iou_loss=ciou
-ignore_thresh = .7
-truth_thresh = 1
-random=0
-resize=1.5
-nms_kind=greedynms
-beta_nms=0.6
-max_delta=5
-embedding_layer = -4
-
-track_history_size = 20
-sim_thresh = 0.7
-dets_for_show = 2
-dets_for_track = 8
-track_ciou_norm = 0.2
-
-[route]
-layers= -5
-
-
-[contrastive]
-classes=1
-temperature=1.0
-yolo_layer= -2
-cls_normalizer=1.0
-max_delta=10
diff --git a/build/darknet/x64/cfg/yolov4.cfg b/build/darknet/x64/cfg/yolov4.cfg
deleted file mode 100644
index a7be12b3088..00000000000
--- a/build/darknet/x64/cfg/yolov4.cfg
+++ /dev/null
@@ -1,1158 +0,0 @@
-[net]
-batch=64
-subdivisions=8
-# Training
-#width=512
-#height=512
-width=608
-height=608
-channels=3
-momentum=0.949
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-learning_rate=0.0013
-burn_in=1000
-max_batches = 500500
-policy=steps
-steps=400000,450000
-scales=.1,.1
-
-#cutmix=1
-mosaic=1
-
-#:104x104 54:52x52 85:26x26 104:13x13 for 416
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=mish
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=2
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1,-7
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=2
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1,-10
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=2
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1,-28
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=2
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1,-28
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=2
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1,-16
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=mish
-
-##########################
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-### SPP ###
-[maxpool]
-stride=1
-size=5
-
-[route]
-layers=-2
-
-[maxpool]
-stride=1
-size=9
-
-[route]
-layers=-4
-
-[maxpool]
-stride=1
-size=13
-
-[route]
-layers=-1,-3,-5,-6
-### End SPP ###
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[upsample]
-stride=2
-
-[route]
-layers = 85
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers = -1, -3
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[upsample]
-stride=2
-
-[route]
-layers = 54
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers = -1, -3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-##########################
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=linear
-
-
-[yolo]
-mask = 0,1,2
-anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
-classes=80
-num=9
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-scale_x_y = 1.2
-iou_thresh=0.213
-cls_normalizer=1.0
-iou_normalizer=0.07
-iou_loss=ciou
-nms_kind=greedynms
-beta_nms=0.6
-max_delta=5
-
-
-[route]
-layers = -4
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=2
-pad=1
-filters=256
-activation=leaky
-
-[route]
-layers = -1, -16
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=linear
-
-
-[yolo]
-mask = 3,4,5
-anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
-classes=80
-num=9
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-scale_x_y = 1.1
-iou_thresh=0.213
-cls_normalizer=1.0
-iou_normalizer=0.07
-iou_loss=ciou
-nms_kind=greedynms
-beta_nms=0.6
-max_delta=5
-
-
-[route]
-layers = -4
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=2
-pad=1
-filters=512
-activation=leaky
-
-[route]
-layers = -1, -37
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=linear
-
-
-[yolo]
-mask = 6,7,8
-anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
-classes=80
-num=9
-jitter=.3
-ignore_thresh = .7
-truth_thresh = 1
-random=1
-scale_x_y = 1.05
-iou_thresh=0.213
-cls_normalizer=1.0
-iou_normalizer=0.07
-iou_loss=ciou
-nms_kind=greedynms
-beta_nms=0.6
-max_delta=5
-
diff --git a/build/darknet/x64/cfg/yolov4x-mish.cfg b/build/darknet/x64/cfg/yolov4x-mish.cfg
deleted file mode 100644
index 2ff854f6dcc..00000000000
--- a/build/darknet/x64/cfg/yolov4x-mish.cfg
+++ /dev/null
@@ -1,1436 +0,0 @@
-[net]
-# Testing
-#batch=1
-#subdivisions=1
-# Training
-batch=64
-subdivisions=8
-width=640
-height=640
-channels=3
-momentum=0.949
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-learning_rate=0.001
-burn_in=1000
-max_batches = 500500
-policy=steps
-steps=400000,450000
-scales=.1,.1
-
-mosaic=1
-
-letter_box=1
-
-#optimized_memory=1
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=mish
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=3
-stride=2
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=40
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=2
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=80
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1,-13
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=2
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1,-34
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=2
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1,-34
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=1280
-size=3
-stride=2
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=3
-stride=1
-pad=1
-activation=mish
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1,-19
-
-[convolutional]
-batch_normalize=1
-filters=1280
-size=1
-stride=1
-pad=1
-activation=mish
-
-########################## 6 0 6 6 3
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=640
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-### SPP ###
-[maxpool]
-stride=1
-size=5
-
-[route]
-layers=-2
-
-[maxpool]
-stride=1
-size=9
-
-[route]
-layers=-4
-
-[maxpool]
-stride=1
-size=13
-
-[route]
-layers=-1,-3,-5,-6
-### End SPP ###
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=640
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=640
-activation=mish
-
-[route]
-layers = -1, -15
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[upsample]
-stride=2
-
-[route]
-layers = 94
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1, -3
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=320
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=320
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=320
-activation=mish
-
-[route]
-layers = -1, -8
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[upsample]
-stride=2
-
-[route]
-layers = 57
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -1, -3
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=160
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=160
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=160
-activation=mish
-
-[route]
-layers = -1, -8
-
-[convolutional]
-batch_normalize=1
-filters=160
-size=1
-stride=1
-pad=1
-activation=mish
-stopbackward=800
-
-##########################
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=320
-activation=mish
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=logistic
-
-
-[yolo]
-mask = 0,1,2
-anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
-classes=80
-num=9
-jitter=.1
-scale_x_y = 2.0
-objectness_smooth=0
-ignore_thresh = .7
-truth_thresh = 1
-#random=1
-resize=1.5
-iou_thresh=0.2
-iou_normalizer=0.05
-cls_normalizer=0.5
-obj_normalizer=4.0
-iou_loss=ciou
-nms_kind=diounms
-beta_nms=0.6
-new_coords=1
-max_delta=5
-
-[route]
-layers = -4
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=2
-pad=1
-filters=320
-activation=mish
-
-[route]
-layers = -1, -22
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=320
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=320
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=320
-activation=mish
-
-[route]
-layers = -1,-8
-
-[convolutional]
-batch_normalize=1
-filters=320
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=640
-activation=mish
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=logistic
-
-
-[yolo]
-mask = 3,4,5
-anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
-classes=80
-num=9
-jitter=.1
-scale_x_y = 2.0
-objectness_smooth=1
-ignore_thresh = .7
-truth_thresh = 1
-#random=1
-resize=1.5
-iou_thresh=0.2
-iou_normalizer=0.05
-cls_normalizer=0.5
-obj_normalizer=1.0
-iou_loss=ciou
-nms_kind=diounms
-beta_nms=0.6
-new_coords=1
-max_delta=5
-
-[route]
-layers = -4
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=2
-pad=1
-filters=640
-activation=mish
-
-[route]
-layers = -1, -55
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[route]
-layers = -2
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=640
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=640
-activation=mish
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=640
-activation=mish
-
-[route]
-layers = -1,-8
-
-[convolutional]
-batch_normalize=1
-filters=640
-size=1
-stride=1
-pad=1
-activation=mish
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1280
-activation=mish
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=255
-activation=logistic
-
-
-[yolo]
-mask = 6,7,8
-anchors = 12, 16, 19, 36, 40, 28, 36, 75, 76, 55, 72, 146, 142, 110, 192, 243, 459, 401
-classes=80
-num=9
-jitter=.1
-scale_x_y = 2.0
-objectness_smooth=1
-ignore_thresh = .7
-truth_thresh = 1
-#random=1
-resize=1.5
-iou_thresh=0.2
-iou_normalizer=0.05
-cls_normalizer=0.5
-obj_normalizer=0.4
-iou_loss=ciou
-nms_kind=diounms
-beta_nms=0.6
-new_coords=1
-max_delta=2
diff --git a/build/darknet/x64/darknet.py b/build/darknet/x64/darknet.py
deleted file mode 100644
index 23539e0b64d..00000000000
--- a/build/darknet/x64/darknet.py
+++ /dev/null
@@ -1,313 +0,0 @@
-#!python3
-"""
-Python 3 wrapper for identifying objects in images
-
-Requires DLL compilation
-
-Both the GPU and no-GPU version should be compiled; the no-GPU version should be renamed "yolo_cpp_dll_nogpu.dll".
-
-On a GPU system, you can force CPU evaluation by any of:
-
-- Set global variable DARKNET_FORCE_CPU to True
-- Set environment variable CUDA_VISIBLE_DEVICES to -1
-- Set environment variable "FORCE_CPU" to "true"
-
-Directly viewing or returning bounding-boxed images requires scikit-image to be installed (`pip install scikit-image`)
-
-Original *nix 2.7: https://github.com/pjreddie/darknet/blob/0f110834f4e18b30d5f101bf8f1724c34b7b83db/python/darknet.py
-Windows Python 2.7 version: https://github.com/AlexeyAB/darknet/blob/fc496d52bf22a0bb257300d3c79be9cd80e722cb/build/darknet/x64/darknet.py
-
-@author: Philip Kahn
-@date: 20180503
-"""
-from ctypes import *
-import math
-import random
-import os
-
-print("Run: darknet_images.py or:\n")
-print("python.exe darknet_video.py --data_file cfg/coco.data --config_file cfg/yolov4.cfg --weights yolov4.weights --input test.mp4 \n")
-
-class BOX(Structure):
-    _fields_ = [("x", c_float),
-                ("y", c_float),
-                ("w", c_float),
-                ("h", c_float)]
-
-
-class DETECTION(Structure):
-    _fields_ = [("bbox", BOX),
-                ("classes", c_int),
-                ("prob", POINTER(c_float)),
-                ("mask", POINTER(c_float)),
-                ("objectness", c_float),
-                ("sort_class", c_int),
-                ("uc", POINTER(c_float)),
-                ("points", c_int),
-                ("embeddings", POINTER(c_float)),
-                ("embedding_size", c_int),
-                ("sim", c_float),
-                ("track_id", c_int)]
-
-class DETNUMPAIR(Structure):
-    _fields_ = [("num", c_int),
-                ("dets", POINTER(DETECTION))]
-
-
-class IMAGE(Structure):
-    _fields_ = [("w", c_int),
-                ("h", c_int),
-                ("c", c_int),
-                ("data", POINTER(c_float))]
-
-
-class METADATA(Structure):
-    _fields_ = [("classes", c_int),
-                ("names", POINTER(c_char_p))]
-
-
-def network_width(net):
-    return lib.network_width(net)
-
-
-def network_height(net):
-    return lib.network_height(net)
-
-
-def bbox2points(bbox):
-    """
-    From bounding box yolo format
-    to corner points cv2 rectangle
-    """
-    x, y, w, h = bbox
-    xmin = int(round(x - (w / 2)))
-    xmax = int(round(x + (w / 2)))
-    ymin = int(round(y - (h / 2)))
-    ymax = int(round(y + (h / 2)))
-    return xmin, ymin, xmax, ymax
-
-
-def class_colors(names):
-    """
-    Create a dict with one random BGR color for each
-    class name
-    """
-    return {name: (
-        random.randint(0, 255),
-        random.randint(0, 255),
-        random.randint(0, 255)) for name in names}
-
-
-def load_network(config_file, data_file, weights, batch_size=1):
-    """
-    load model description and weights from config files
-    args:
-        config_file (str): path to .cfg model file
-        data_file (str): path to .data model file
-        weights (str): path to weights
-    returns:
-        network: trained model
-        class_names
-        class_colors
-    """
-    network = load_net_custom(
-        config_file.encode("ascii"),
-        weights.encode("ascii"), 0, batch_size)
-    metadata = load_meta(data_file.encode("ascii"))
-    class_names = [metadata.names[i].decode("ascii") for i in range(metadata.classes)]
-    colors = class_colors(class_names)
-    return network, class_names, colors
-
-
-def print_detections(detections, coordinates=False):
-    print("\nObjects:")
-    for label, confidence, bbox in detections:
-        x, y, w, h = bbox
-        if coordinates:
-            print("{}: {}%    (left_x: {:.0f}   top_y:  {:.0f}   width:   {:.0f}   height:  {:.0f})".format(label, confidence, x, y, w, h))
-        else:
-            print("{}: {}%".format(label, confidence))
-
-
-def draw_boxes(detections, image, colors):
-    import cv2
-    for label, confidence, bbox in detections:
-        left, top, right, bottom = bbox2points(bbox)
-        cv2.rectangle(image, (left, top), (right, bottom), colors[label], 1)
-        cv2.putText(image, "{} [{:.2f}]".format(label, float(confidence)),
-                    (left, top - 5), cv2.FONT_HERSHEY_SIMPLEX, 0.5,
-                    colors[label], 2)
-    return image
-
-
-def decode_detection(detections):
-    decoded = []
-    for label, confidence, bbox in detections:
-        confidence = str(round(confidence * 100, 2))
-        decoded.append((str(label), confidence, bbox))
-    return decoded
-
-
-def remove_negatives(detections, class_names, num):
-    """
-    Remove all classes with 0% confidence within the detection
-    """
-    predictions = []
-    for j in range(num):
-        for idx, name in enumerate(class_names):
-            if detections[j].prob[idx] > 0:
-                bbox = detections[j].bbox
-                bbox = (bbox.x, bbox.y, bbox.w, bbox.h)
-                predictions.append((name, detections[j].prob[idx], (bbox)))
-    return predictions
-
-
-def detect_image(network, class_names, image, thresh=.5, hier_thresh=.5, nms=.45):
-    """
-        Returns a list with highest confidence class and their bbox
-    """
-    pnum = pointer(c_int(0))
-    predict_image(network, image)
-    detections = get_network_boxes(network, image.w, image.h,
-                                   thresh, hier_thresh, None, 0, pnum, 0)
-    num = pnum[0]
-    if nms:
-        do_nms_sort(detections, num, len(class_names), nms)
-    predictions = remove_negatives(detections, class_names, num)
-    predictions = decode_detection(predictions)
-    free_detections(detections, num)
-    return sorted(predictions, key=lambda x: x[1])
-
-
-#  lib = CDLL("/home/pjreddie/documents/darknet/libdarknet.so", RTLD_GLOBAL)
-#  lib = CDLL("libdarknet.so", RTLD_GLOBAL)
-hasGPU = True
-if os.name == "nt":
-    cwd = os.path.dirname(__file__)
-    os.environ['PATH'] = cwd + ';' + os.environ['PATH']
-    winGPUdll = os.path.join(cwd, "yolo_cpp_dll.dll")
-    winNoGPUdll = os.path.join(cwd, "yolo_cpp_dll_nogpu.dll")
-    envKeys = list()
-    for k, v in os.environ.items():
-        envKeys.append(k)
-    try:
-        try:
-            tmp = os.environ["FORCE_CPU"].lower()
-            if tmp in ["1", "true", "yes", "on"]:
-                raise ValueError("ForceCPU")
-            else:
-                print("Flag value {} not forcing CPU mode".format(tmp))
-        except KeyError:
-            # We never set the flag
-            if 'CUDA_VISIBLE_DEVICES' in envKeys:
-                if int(os.environ['CUDA_VISIBLE_DEVICES']) < 0:
-                    raise ValueError("ForceCPU")
-            try:
-                global DARKNET_FORCE_CPU
-                if DARKNET_FORCE_CPU:
-                    raise ValueError("ForceCPU")
-            except NameError as cpu_error:
-                print(cpu_error)
-        if not os.path.exists(winGPUdll):
-            raise ValueError("NoDLL")
-        lib = CDLL(winGPUdll, RTLD_GLOBAL)
-    except (KeyError, ValueError):
-        hasGPU = False
-        if os.path.exists(winNoGPUdll):
-            lib = CDLL(winNoGPUdll, RTLD_GLOBAL)
-            print("Notice: CPU-only mode")
-        else:
-            # Try the other way, in case no_gpu was compile but not renamed
-            lib = CDLL(winGPUdll, RTLD_GLOBAL)
-            print("Environment variables indicated a CPU run, but we didn't find {}. Trying a GPU run anyway.".format(winNoGPUdll))
-else:
-    lib = CDLL("./libdarknet.so", RTLD_GLOBAL)
-lib.network_width.argtypes = [c_void_p]
-lib.network_width.restype = c_int
-lib.network_height.argtypes = [c_void_p]
-lib.network_height.restype = c_int
-
-copy_image_from_bytes = lib.copy_image_from_bytes
-copy_image_from_bytes.argtypes = [IMAGE,c_char_p]
-
-predict = lib.network_predict_ptr
-predict.argtypes = [c_void_p, POINTER(c_float)]
-predict.restype = POINTER(c_float)
-
-if hasGPU:
-    set_gpu = lib.cuda_set_device
-    set_gpu.argtypes = [c_int]
-
-init_cpu = lib.init_cpu
-
-make_image = lib.make_image
-make_image.argtypes = [c_int, c_int, c_int]
-make_image.restype = IMAGE
-
-get_network_boxes = lib.get_network_boxes
-get_network_boxes.argtypes = [c_void_p, c_int, c_int, c_float, c_float, POINTER(c_int), c_int, POINTER(c_int), c_int]
-get_network_boxes.restype = POINTER(DETECTION)
-
-make_network_boxes = lib.make_network_boxes
-make_network_boxes.argtypes = [c_void_p]
-make_network_boxes.restype = POINTER(DETECTION)
-
-free_detections = lib.free_detections
-free_detections.argtypes = [POINTER(DETECTION), c_int]
-
-free_batch_detections = lib.free_batch_detections
-free_batch_detections.argtypes = [POINTER(DETNUMPAIR), c_int]
-
-free_ptrs = lib.free_ptrs
-free_ptrs.argtypes = [POINTER(c_void_p), c_int]
-
-network_predict = lib.network_predict_ptr
-network_predict.argtypes = [c_void_p, POINTER(c_float)]
-
-reset_rnn = lib.reset_rnn
-reset_rnn.argtypes = [c_void_p]
-
-load_net = lib.load_network
-load_net.argtypes = [c_char_p, c_char_p, c_int]
-load_net.restype = c_void_p
-
-load_net_custom = lib.load_network_custom
-load_net_custom.argtypes = [c_char_p, c_char_p, c_int, c_int]
-load_net_custom.restype = c_void_p
-
-do_nms_obj = lib.do_nms_obj
-do_nms_obj.argtypes = [POINTER(DETECTION), c_int, c_int, c_float]
-
-do_nms_sort = lib.do_nms_sort
-do_nms_sort.argtypes = [POINTER(DETECTION), c_int, c_int, c_float]
-
-free_image = lib.free_image
-free_image.argtypes = [IMAGE]
-
-letterbox_image = lib.letterbox_image
-letterbox_image.argtypes = [IMAGE, c_int, c_int]
-letterbox_image.restype = IMAGE
-
-load_meta = lib.get_metadata
-lib.get_metadata.argtypes = [c_char_p]
-lib.get_metadata.restype = METADATA
-
-load_image = lib.load_image_color
-load_image.argtypes = [c_char_p, c_int, c_int]
-load_image.restype = IMAGE
-
-rgbgr_image = lib.rgbgr_image
-rgbgr_image.argtypes = [IMAGE]
-
-predict_image = lib.network_predict_image
-predict_image.argtypes = [c_void_p, IMAGE]
-predict_image.restype = POINTER(c_float)
-
-predict_image_letterbox = lib.network_predict_image_letterbox
-predict_image_letterbox.argtypes = [c_void_p, IMAGE]
-predict_image_letterbox.restype = POINTER(c_float)
-
-network_predict_batch = lib.network_predict_batch
-network_predict_batch.argtypes = [c_void_p, IMAGE, c_int, c_int, c_int,
-                                   c_float, c_float, POINTER(c_int), c_int, c_int]
-network_predict_batch.restype = POINTER(DETNUMPAIR)
diff --git a/build/darknet/x64/darknet_video.py b/build/darknet/x64/darknet_video.py
deleted file mode 100644
index 5e7036203a7..00000000000
--- a/build/darknet/x64/darknet_video.py
+++ /dev/null
@@ -1,183 +0,0 @@
-from ctypes import *
-import random
-import os
-import cv2
-import time
-import darknet
-import argparse
-from threading import Thread, enumerate
-from queue import Queue
-
-
-def parser():
-    parser = argparse.ArgumentParser(description="YOLO Object Detection")
-    parser.add_argument("--input", type=str, default=0,
-                        help="video source. If empty, uses webcam 0 stream")
-    parser.add_argument("--out_filename", type=str, default="",
-                        help="inference video name. Not saved if empty")
-    parser.add_argument("--weights", default="yolov4.weights",
-                        help="yolo weights path")
-    parser.add_argument("--dont_show", action='store_true',
-                        help="windown inference display. For headless systems")
-    parser.add_argument("--ext_output", action='store_true',
-                        help="display bbox coordinates of detected objects")
-    parser.add_argument("--config_file", default="./cfg/yolov4.cfg",
-                        help="path to config file")
-    parser.add_argument("--data_file", default="./cfg/coco.data",
-                        help="path to data file")
-    parser.add_argument("--thresh", type=float, default=.25,
-                        help="remove detections with confidence below this value")
-    return parser.parse_args()
-
-
-def str2int(video_path):
-    """
-    argparse returns and string althout webcam uses int (0, 1 ...)
-    Cast to int if needed
-    """
-    try:
-        return int(video_path)
-    except ValueError:
-        return video_path
-
-
-def check_arguments_errors(args):
-    assert 0 < args.thresh < 1, "Threshold should be a float between zero and one (non-inclusive)"
-    if not os.path.exists(args.config_file):
-        raise(ValueError("Invalid config path {}".format(os.path.abspath(args.config_file))))
-    if not os.path.exists(args.weights):
-        raise(ValueError("Invalid weight path {}".format(os.path.abspath(args.weights))))
-    if not os.path.exists(args.data_file):
-        raise(ValueError("Invalid data file path {}".format(os.path.abspath(args.data_file))))
-    if str2int(args.input) == str and not os.path.exists(args.input):
-        raise(ValueError("Invalid video path {}".format(os.path.abspath(args.input))))
-
-
-def set_saved_video(input_video, output_video, size):
-    fourcc = cv2.VideoWriter_fourcc(*"MJPG")
-    fps = int(input_video.get(cv2.CAP_PROP_FPS))
-    video = cv2.VideoWriter(output_video, fourcc, fps, size)
-    return video
-
-
-def convert2relative(bbox):
-    """
-    YOLO format use relative coordinates for annotation
-    """
-    x, y, w, h  = bbox
-    _height     = darknet_height
-    _width      = darknet_width
-    return x/_width, y/_height, w/_width, h/_height
-
-
-def convert2original(image, bbox):
-    x, y, w, h = convert2relative(bbox)
-
-    image_h, image_w, __ = image.shape
-
-    orig_x       = int(x * image_w)
-    orig_y       = int(y * image_h)
-    orig_width   = int(w * image_w)
-    orig_height  = int(h * image_h)
-
-    bbox_converted = (orig_x, orig_y, orig_width, orig_height)
-
-    return bbox_converted
-
-
-def convert4cropping(image, bbox):
-    x, y, w, h = convert2relative(bbox)
-
-    image_h, image_w, __ = image.shape
-
-    orig_left    = int((x - w / 2.) * image_w)
-    orig_right   = int((x + w / 2.) * image_w)
-    orig_top     = int((y - h / 2.) * image_h)
-    orig_bottom  = int((y + h / 2.) * image_h)
-
-    if (orig_left < 0): orig_left = 0
-    if (orig_right > image_w - 1): orig_right = image_w - 1
-    if (orig_top < 0): orig_top = 0
-    if (orig_bottom > image_h - 1): orig_bottom = image_h - 1
-
-    bbox_cropping = (orig_left, orig_top, orig_right, orig_bottom)
-
-    return bbox_cropping
-
-
-def video_capture(frame_queue, darknet_image_queue):
-    while cap.isOpened():
-        ret, frame = cap.read()
-        if not ret:
-            break
-        frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
-        frame_resized = cv2.resize(frame_rgb, (darknet_width, darknet_height),
-                                   interpolation=cv2.INTER_LINEAR)
-        frame_queue.put(frame)
-        img_for_detect = darknet.make_image(darknet_width, darknet_height, 3)
-        darknet.copy_image_from_bytes(img_for_detect, frame_resized.tobytes())
-        darknet_image_queue.put(img_for_detect)
-    cap.release()
-
-
-def inference(darknet_image_queue, detections_queue, fps_queue):
-    while cap.isOpened():
-        darknet_image = darknet_image_queue.get()
-        prev_time = time.time()
-        detections = darknet.detect_image(network, class_names, darknet_image, thresh=args.thresh)
-        detections_queue.put(detections)
-        fps = int(1/(time.time() - prev_time))
-        fps_queue.put(fps)
-        print("FPS: {}".format(fps))
-        darknet.print_detections(detections, args.ext_output)
-        darknet.free_image(darknet_image)
-    cap.release()
-
-
-def drawing(frame_queue, detections_queue, fps_queue):
-    random.seed(3)  # deterministic bbox colors
-    video = set_saved_video(cap, args.out_filename, (video_width, video_height))
-    while cap.isOpened():
-        frame = frame_queue.get()
-        detections = detections_queue.get()
-        fps = fps_queue.get()
-        detections_adjusted = []
-        if frame is not None:
-            for label, confidence, bbox in detections:
-                bbox_adjusted = convert2original(frame, bbox)
-                detections_adjusted.append((str(label), confidence, bbox_adjusted))
-            image = darknet.draw_boxes(detections_adjusted, frame, class_colors)
-            if not args.dont_show:
-                cv2.imshow('Inference', image)
-            if args.out_filename is not None:
-                video.write(image)
-            if cv2.waitKey(fps) == 27:
-                break
-    cap.release()
-    video.release()
-    cv2.destroyAllWindows()
-
-
-if __name__ == '__main__':
-    frame_queue = Queue()
-    darknet_image_queue = Queue(maxsize=1)
-    detections_queue = Queue(maxsize=1)
-    fps_queue = Queue(maxsize=1)
-
-    args = parser()
-    check_arguments_errors(args)
-    network, class_names, class_colors = darknet.load_network(
-            args.config_file,
-            args.data_file,
-            args.weights,
-            batch_size=1
-        )
-    darknet_width = darknet.network_width(network)
-    darknet_height = darknet.network_height(network)
-    input_path = str2int(args.input)
-    cap = cv2.VideoCapture(input_path)
-    video_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
-    video_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
-    Thread(target=video_capture, args=(frame_queue, darknet_image_queue)).start()
-    Thread(target=inference, args=(darknet_image_queue, detections_queue, fps_queue)).start()
-    Thread(target=drawing, args=(frame_queue, detections_queue, fps_queue)).start()
diff --git a/build/darknet/x64/data/9k.labels b/build/darknet/x64/data/9k.labels
deleted file mode 100644
index e2bd3082485..00000000000
--- a/build/darknet/x64/data/9k.labels
+++ /dev/null
@@ -1,9418 +0,0 @@
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diff --git a/build/darknet/x64/data/9k.names b/build/darknet/x64/data/9k.names
deleted file mode 100644
index e81c80e79e8..00000000000
--- a/build/darknet/x64/data/9k.names
+++ /dev/null
@@ -1,9418 +0,0 @@
-thing
-matter
-object
-atmospheric phenomenon
-body part
-body of water
-head
-hair
-structure
-vein
-mouth
-heel
-watercourse
-ocean
-gas
-solid
-substance
-food
-tear gas
-sky
-ice
-food
-cheese
-yogurt
-produce
-baked goods
-cake mix
-Emmenthal
-Camembert
-Brie
-mozzarella
-Stilton
-double cream
-edible fruit
-vegetable
-currant
-custard apple
-citrus
-jackfruit
-pomegranate
-avocado
-prickly pear
-apple
-carambola
-fig
-mangosteen
-tangelo
-plum
-papaya
-apricot
-berry
-elderberry
-loquat
-pear
-litchi
-peach
-muscat
-grape
-banana
-pitahaya
-rambutan
-kiwi
-melon
-breadfruit
-pineapple
-mango
-date
-papaw
-durian
-passion fruit
-jujube
-guava
-dried fruit
-cherry
-quince
-nectarine
-cherimoya
-soursop
-lime
-mandarin
-kumquat
-orange
-lemon
-citron
-grapefruit
-pomelo
-clementine
-tangerine
-satsuma
-sweet orange
-bitter orange
-navel orange
-Valencia orange
-crab apple
-eating apple
-Granny Smith
-Delicious
-McIntosh
-Red Delicious
-Golden Delicious
-strawberry
-mulberry
-currant
-lingonberry
-blackberry
-red currant
-raspberry
-cranberry
-acerola
-persimmon
-blueberry
-bilberry
-muskmelon
-watermelon
-cantaloup
-sour cherry
-sweet cherry
-bing cherry
-mushroom
-asparagus
-plantain
-pumpkin
-cucumber
-root vegetable
-cruciferous vegetable
-raw vegetable
-solanaceous vegetable
-artichoke
-legume
-leek
-squash
-greens
-celery
-cardoon
-gumbo
-pieplant
-onion
-fennel
-taro
-beet
-yam
-carrot
-potato
-baked potato
-mashed potato
-french fries
-mustard
-cabbage
-kohlrabi
-cauliflower
-brussels sprouts
-broccoli rabe
-broccoli
-radish
-turnip
-collards
-bok choy
-savoy cabbage
-head cabbage
-kale
-pepper
-tomato
-eggplant
-tomatillo
-sweet pepper
-hot pepper
-bell pepper
-pimento
-green pepper
-chili
-tabasco
-cayenne
-jalapeno
-cherry tomato
-beefsteak tomato
-bean
-pea
-chickpea
-lentil
-soy
-common bean
-black bean
-kidney bean
-fresh bean
-green bean
-shell bean
-snap bean
-haricot vert
-string bean
-fava bean
-green soybean
-green pea
-snow pea
-sugar snap pea
-summer squash
-winter squash
-spaghetti squash
-zucchini
-yellow squash
-butternut squash
-acorn squash
-chard
-turnip greens
-salad green
-bean sprout
-spinach
-lamb's-quarter
-cress
-chicory
-chicory escarole
-radicchio
-lettuce
-leaf lettuce
-crisphead lettuce
-cos
-green onion
-shallot
-purple onion
-doughnut
-bread
-crouton
-breadstick
-soft pretzel
-rye bread
-dark bread
-raisin bread
-brown bread
-cinnamon bread
-quick bread
-matzo
-sour bread
-bun
-white bread
-challah
-loaf of bread
-pretzel
-English muffin
-toast
-nan
-chapatti
-garlic bread
-Yorkshire pudding
-banana bread
-scone
-Irish soda bread
-biscuit
-muffin
-drop scone
-cornbread
-nut bread
-buttermilk biscuit
-hardtack
-shortcake
-corn muffin
-popover
-bran muffin
-cornpone
-hush puppy
-johnnycake
-hamburger bun
-bagel
-frankfurter bun
-sweet roll
-hard roll
-brioche
-crescent roll
-honey bun
-cinnamon roll
-cross bun
-Italian bread
-baguet
-French bread
-meat loaf
-French loaf
-material
-stucco
-gravel
-rock
-leopard
-soil
-sand
-loofa
-paper
-litter
-toilet tissue
-queen
-comestible
-foodstuff
-fare
-beverage
-soul food
-feed
-nutriment
-yolk
-comfort food
-egg
-grain
-carrot juice
-soya milk
-whole wheat flour
-oatmeal
-ingredient
-dairy product
-cocoa
-concoction
-Spam
-juice
-canned food
-corn
-rice
-wild rice
-barley
-wheat
-sweet corn
-popcorn
-white rice
-paddy
-flavorer
-egg yolk
-saffron
-juniper berries
-cayenne
-sesame seed
-sassafras
-spice
-condiment
-sweetening
-herb
-paprika
-garlic
-nasturtium
-mocha
-cardamom
-nutmeg
-stick cinnamon
-Chinese anise
-clove
-cinnamon
-guacamole
-chili sauce
-olive
-chutney
-vinegar
-dip
-soy sauce
-salsa
-cranberry sauce
-catsup
-spread
-green olive
-sauce
-wine vinegar
-cider vinegar
-hummus
-miso
-spaghetti sauce
-chocolate sauce
-Tabasco
-hot sauce
-veloute
-pesto
-dressing
-bourguignon
-hollandaise
-carbonara
-tomato sauce
-green mayonnaise
-mayonnaise
-powdered sugar
-honey
-syrup
-sorghum
-grenadine
-maple syrup
-basil
-lemon balm
-sweet woodruff
-clary sage
-hyssop
-comfrey
-coriander
-mint
-chives
-marjoram
-borage
-sage
-tea
-rosemary
-parsley
-bay leaf
-thyme
-tea bag
-oolong
-souchong
-cream
-milk
-whipping cream
-clotted cream
-light cream
-heavy cream
-stuffing
-batter
-dough
-filling
-pastry
-bread dough
-puff paste
-phyllo
-chow
-menu
-dietary
-diet
-diet
-dietary supplement
-vegetarianism
-vitamin pill
-multivitamin
-alcohol
-fruit juice
-fizz
-near beer
-cocoa
-coffee
-cider
-tea
-soft drink
-fruit drink
-ginger beer
-drinking water
-potion
-smoothie
-mixed drink
-liquor
-sake
-wine
-hooch
-home brew
-liqueur
-hard cider
-brew
-neutral spirits
-aperitif
-highball
-cocktail
-spritzer
-punch
-pina colada
-mimosa
-julep
-gin and tonic
-Bloody Mary
-martini
-gimlet
-gin and it
-daiquiri
-sidecar
-Sazerac
-margarita
-cup
-May wine
-eggnog
-fruit punch
-vodka
-firewater
-aquavit
-grog
-schnapps
-arrack
-gin
-rum
-aqua vitae
-tequila
-bitters
-geneva
-brandy
-ouzo
-whiskey
-eau de vie
-Cognac
-grappa
-Armagnac
-Calvados
-Irish
-bourbon
-sour mash
-Scotch
-rye
-corn whiskey
-blended whiskey
-blush wine
-vintage
-champagne
-vin ordinaire
-dessert wine
-macon
-sparkling wine
-Cotes de Provence
-varietal
-Burgundy
-fortified wine
-Bordeaux
-table wine
-California wine
-vermouth
-red wine
-Rhone wine
-white wine
-Montrachet
-Beaujolais
-Chablis
-Madeira
-sherry
-malmsey
-port
-muscat
-Saint Emilion
-claret
-dry vermouth
-sweet vermouth
-Medoc
-Chianti
-Pinot noir
-Rioja
-Merlot
-Cabernet
-zinfandel
-Riesling
-Sauvignon blanc
-Muscadet
-Yquem
-Pinot blanc
-Sauterne
-Chenin blanc
-Chardonnay
-sack
-Verdicchio
-Canary wine
-Pernod
-Drambuie
-sambuca
-triple sec
-absinth
-maraschino
-anisette
-beer
-lager
-draft beer
-ale
-suds
-Munich beer
-Pilsner
-light beer
-malt
-bock
-Weissbier
-porter
-stout
-bitter
-pale ale
-Guinness
-Weizenbock
-orange juice
-cranberry juice
-nectar
-iced coffee
-caffe latte
-espresso
-cappuccino
-Irish coffee
-chicory
-cafe au lait
-mocha
-Turkish coffee
-ice tea
-cuppa
-tonic
-cola
-orange soda
-ginger ale
-pop
-root beer
-Coca Cola
-Pepsi
-mineral water
-bottled water
-soda water
-oil cake
-bird feed
-fodder
-eatage
-hay
-alfalfa
-broad bean
-dainty
-fast food
-puree
-finger food
-dish
-course
-mother's milk
-vitamin
-kosher
-meal
-jello
-gelatin
-sweet
-candied apple
-confiture
-candy
-chewing gum
-confectionery
-maraschino
-conserve
-strawberry jam
-apple butter
-lemon curd
-jam
-jelly
-peppermint
-peanut brittle
-chocolate kiss
-nougat bar
-candy bar
-jelly bean
-lollipop
-candy cane
-truffle
-chocolate fudge
-cough drop
-sugar candy
-Easter egg
-kiss
-gumdrop
-candy corn
-fondant
-cotton candy
-caramel
-fudge
-candy egg
-chocolate egg
-bubble gum
-gum ball
-poached egg
-piece de resistance
-side dish
-stew
-omelet
-soup
-sashimi
-taco
-French toast
-cheese souffle
-potpie
-lamb curry
-stuffed tomato
-chow mein
-croquette
-gefilte fish
-coq au vin
-special
-spaghetti and meatballs
-eggs Benedict
-schnitzel
-buffalo wing
-chicken casserole
-rissole
-paella
-frittata
-meatball
-chili
-porridge
-tamale
-stuffed tomato
-couscous
-deviled egg
-beef Wellington
-pasta
-egg roll
-enchilada
-falafel
-mushy peas
-turnover
-scrambled eggs
-Spanish rice
-teriyaki
-barbecued spareribs
-pilaf
-kabob
-tempura
-samosa
-fried egg
-sandwich plate
-chicken cacciatore
-saute
-fried rice
-custard
-sukiyaki
-fish and chips
-souffle
-steak au poivre
-pizza
-fondue
-biryani
-stuffed peppers
-mousse
-shirred egg
-Swedish meatball
-jambalaya
-Scotch egg
-burrito
-risotto
-salad
-boiled egg
-curry
-snack food
-bouillabaisse
-goulash
-pottage
-beef stew
-hot pot
-fish stew
-hotchpotch
-Irish stew
-ratatouille
-gazpacho
-won ton
-petite marmite
-split-pea soup
-consomme
-chowder
-potage
-marmite
-lentil soup
-bisque
-pea soup
-pepper pot
-chicken broth
-chicken soup
-broth
-broth
-gumbo
-borsch
-corn chowder
-clam chowder
-fish chowder
-gruel
-congee
-macaroni and cheese
-lasagna
-cannelloni
-spaghetti
-creme caramel
-creme brulee
-pepperoni pizza
-anchovy pizza
-cheese pizza
-Sicilian pizza
-sausage pizza
-chocolate fondue
-cheese fondue
-coleslaw
-macaroni salad
-tossed salad
-salad nicoise
-pasta salad
-fruit salad
-tabbouleh
-green salad
-chef's salad
-hard-boiled egg
-Easter egg
-sandwich
-corn chip
-chip
-bomber
-cheeseburger
-chicken sandwich
-ham sandwich
-Reuben
-bacon-lettuce-tomato sandwich
-chili dog
-open-face sandwich
-gyro
-wrap
-hamburger
-club sandwich
-hotdog
-tortilla chip
-nacho
-entree
-plate
-dessert
-appetizer
-mousse
-tiramisu
-frozen dessert
-pudding
-pudding
-trifle
-flan
-whip
-dumpling
-compote
-chocolate mousse
-pavlova
-parfait
-ice-cream cake
-ice lolly
-ice-cream sundae
-ice-cream cone
-ice cream
-ice
-banana split
-frozen yogurt
-frozen custard
-vanilla ice cream
-peach ice cream
-chocolate ice cream
-strawberry ice cream
-plum pudding
-chocolate pudding
-shrimp cocktail
-stuffed mushroom
-cocktail
-hors d'oeuvre
-carrot stick
-antipasto
-water-soluble vitamin
-fat-soluble vitamin
-vitamin P
-vitamin C
-B-complex vitamin
-vitamin B2
-inositol
-vitamin B6
-choline
-pantothenic acid
-vitamin B12
-vitamin Bc
-vitamin D
-vitamin A1
-picnic
-bite
-supper
-breakfast
-refection
-smorgasbord
-buffet
-brunch
-continental breakfast
-dinner
-lunch
-banquet
-cookout
-fish fry
-barbecue
-refreshment
-nosh
-land
-location
-land
-fomite
-part
-geological formation
-cobweb
-whole
-hail
-swamp
-cultivated land
-region
-region
-pass
-line
-point
-opening
-bedside
-soil horizon
-extremity
-boundary
-nib
-selvage
-shoreline
-benthos
-resort area
-geographical area
-district
-scrubland
-bush
-oilfield
-field
-tract
-heronry
-grassland
-site
-court
-basketball court
-fairground
-plot
-field
-amusement park
-veld
-pasture
-campsite
-garbage heap
-cemetery
-flowerbed
-garden
-topiary
-peach orchard
-yard
-grainfield
-playground
-garden
-city
-city district
-eparchy
-kasbah
-waterfront
-business district
-col
-defile
-hemline
-spoor
-crest
-topographic point
-workplace
-half-mast
-intersection
-bus stop
-mecca
-hole-in-the-wall
-patisserie
-bakery
-farm
-piggery
-ranch
-dairy
-knothole
-chasm
-oxbow
-floor
-pinetum
-plain
-steppe
-cigarette butt
-pipefitting
-handle
-panhandle
-stock
-haft
-ax handle
-broomstick
-pistol grip
-arete
-volcanic crater
-spring
-ice mass
-natural depression
-natural elevation
-oceanfront
-massif
-cliff
-shore
-talus
-ridge
-range
-lakefront
-slope
-cave
-foreshore
-beach
-hot spring
-geyser
-icecap
-iceberg
-Alpine glacier
-glacier
-valley
-lunar crater
-landfill
-sinkhole
-basin
-crater
-bed
-hole
-arroyo
-ravine
-canyon
-gorge
-tidal basin
-cirque
-ocean floor
-riverbed
-streambed
-burrow
-pothole
-tableland
-hill
-mountain
-highland
-ridge
-promontory
-anthill
-butte
-foothill
-knoll
-alp
-ben
-volcano
-sandbar
-dune
-reef
-bank
-coral reef
-atoll
-sandbank
-bluff
-point
-mull
-crag
-precipice
-seashore
-strand
-lakeside
-littoral
-seaside
-mountainside
-descent
-hillside
-ski slope
-escarpment
-bank
-downhill
-ascent
-brae
-uphill
-riverbank
-waterside
-cove
-cavern
-grotto
-artifact
-living thing
-natural object
-assembly
-block
-millstone
-paving
-creation
-opening
-plaything
-surface
-tramline
-structure
-instrumentality
-padding
-covering
-fabric
-bookmark
-float
-building material
-decoration
-way
-strip
-article
-facility
-excavation
-commodity
-sheet
-fixture
-blacktop
-line
-bullion
-tessera
-tile
-anvil
-representation
-art
-needlework
-product
-pieta
-map
-sketch
-sonogram
-photograph
-waxwork
-arthrogram
-radiogram
-photomicrograph
-photostat
-painting
-triptych
-nude
-finger-painting
-smocking
-stitch
-sewing stitch
-knitting stitch
-lockstitch
-hemstitch
-garter stitch
-purl
-book
-work
-jotter
-newspaper
-wicker
-masterpiece
-openwork
-woodwork
-lacquerware
-cabinetwork
-joinery
-decolletage
-gargoyle
-aperture
-hole
-mouthpiece
-outfall
-plughole
-manhole
-keyhole
-perforation
-thumbhole
-pogo stick
-ball
-pinata
-teddy
-jungle gym
-bubble
-hula-hoop
-pinwheel
-slide
-sport kite
-cockhorse
-doll
-foam
-air bubble
-spume
-shaving foam
-golliwog
-kachina
-horizontal surface
-board
-tabletop
-side
-platform
-tarmacadam
-floor
-turntable
-stage
-dais
-sumo ring
-hurricane deck
-flatbed
-parquet
-backgammon board
-pegboard
-facade
-ceiling
-body
-floor
-bridge
-corner
-conformation
-superstructure
-airdock
-cross
-house of cards
-hull
-gun enclosure
-lookout
-building complex
-shelter
-honeycomb
-column
-altar
-arch
-tower
-transept
-mound
-fountain
-obelisk
-fan vaulting
-arcade
-loggia
-coil
-billboard
-partition
-masonry
-skein
-colonnade
-obstruction
-building
-sail
-projection
-peristyle
-door
-stadium
-drinking fountain
-area
-balcony
-porch
-dock
-high altar
-housing
-supporting structure
-balance
-defensive structure
-entablature
-memorial
-establishment
-signboard
-bodywork
-fuselage
-ground floor
-mezzanine
-loft
-basement
-footbridge
-drawbridge
-cantilever bridge
-rope bridge
-overpass
-truss bridge
-viaduct
-steel arch bridge
-covered bridge
-gangplank
-suspension bridge
-trestle bridge
-plant
-college
-winery
-factory
-distillery
-oil refinery
-refinery
-rolling mill
-foundry
-steel mill
-lumbermill
-stamp mill
-quartz battery
-battery
-harbor
-Nissen hut
-hovel
-tent
-hut
-igloo
-dugout
-mountain tent
-pup tent
-pavilion
-backpacking tent
-fly tent
-canvas tent
-field tent
-wall tent
-circus tent
-Gothic arch
-round arch
-pointed arch
-triumphal arch
-broken arch
-Moorish arch
-Roman arch
-campanile
-turret
-clock tower
-shot tower
-church tower
-minaret
-pylon
-silo
-watchtower
-trestle
-pylon
-steeple
-fire tower
-high-rise
-supporting tower
-control tower
-bell tower
-beacon
-burial mound
-snowbank
-rampart
-fraise
-battlement
-altarpiece
-wall
-gable
-wainscoting
-attic
-pediment
-bell gable
-brickwork
-stonework
-barrier
-obstacle
-plug
-lever
-grate
-safety rail
-movable barrier
-bannister
-breakwater
-grille
-weir
-railing
-hurdle
-starting gate
-fence
-dam
-gate
-door
-lychgate
-portcullis
-turnstile
-French window
-car door
-screen door
-French door
-double door
-interior door
-sliding door
-revolving door
-barn door
-hatchback
-storm door
-wall
-retaining wall
-rail fence
-chainlink fence
-dry wall
-hedge
-worm fence
-picket fence
-stone wall
-water jump
-bunker
-earplug
-cork
-tap
-presbytery
-hotel
-tenement
-abattoir
-apartment building
-aviary
-hall
-house
-Roman building
-rest house
-outbuilding
-funeral home
-medical building
-hotel-casino
-library
-casino
-farm building
-place of worship
-restaurant
-ministry
-rotunda
-observatory
-office building
-temple
-signal box
-government building
-greenhouse
-rink
-planetarium
-public house
-bowling alley
-house
-ruin
-architecture
-skyscraper
-gazebo
-school
-chapterhouse
-theater
-hall of residence
-conservatory
-center
-resort hotel
-resort
-motel
-dude ranch
-Ritz
-ski lodge
-hostel
-motor hotel
-city hall
-guildhall
-lyceum
-field house
-oast house
-courthouse
-shed
-garage
-carport
-outhouse
-coach house
-boathouse
-woodshed
-apiary
-maternity hospital
-dispensary
-stable
-chicken coop
-cowbarn
-barn
-pantheon
-church
-temple
-shrine
-stupa
-masjid
-synagogue
-mosque
-chapel
-kirk
-abbey
-cathedral
-cathedral
-minster
-cafe
-rotisserie
-automat
-brasserie
-cafeteria
-diner
-capitol
-embassy
-town hall
-chancellery
-customhouse
-Statehouse
-courthouse
-ice rink
-ice hockey rink
-alehouse
-free house
-solar house
-bungalow
-row house
-cabin
-duplex house
-mansion
-lodging house
-gatehouse
-log cabin
-saltbox
-country house
-dollhouse
-ranch house
-boarding house
-detached house
-villa
-chalet
-residence
-farmhouse
-terraced house
-brownstone
-palace
-stately home
-manor
-summer house
-dacha
-villa
-chateau
-manse
-religious residence
-glebe house
-parsonage
-palace
-monastery
-abbey
-abbey
-day school
-conservatory
-music school
-opera
-music hall
-cinema
-little theater
-home theater
-control center
-settlement house
-call center
-cornice
-cog
-knob
-bill
-flange
-brim
-tine
-eaves
-tooth
-pinhead
-football stadium
-hippodrome
-dome
-ballpark
-bullring
-amphitheater
-patio
-corner
-baggage claim
-hideaway
-choir
-breakfast area
-quad
-chancel
-auditorium
-court
-dining area
-room
-assembly hall
-enclosure
-nave
-aisle
-storage space
-goalmouth
-food court
-atrium
-cloister
-forecourt
-toilet
-sun parlor
-engineering
-surgery
-rotunda
-classroom
-gallery
-manor hall
-cell
-lounge
-sauna
-dressing room
-billiard room
-belfry
-kitchen
-library
-storeroom
-workroom
-sewing room
-anechoic chamber
-dining room
-recreation room
-hospital room
-reading room
-booth
-conference room
-bedroom
-clean room
-living room
-door
-hall
-reception room
-boardroom
-study
-locker room
-cocktail lounge
-television room
-compartment
-court
-poolroom
-bathroom
-control room
-anteroom
-water closet
-men's room
-washroom
-public toilet
-home room
-lecture room
-study hall
-pantry
-stockroom
-vault
-refectory
-dining-hall
-canteen
-family room
-rumpus room
-emergency room
-recovery room
-operating room
-telephone booth
-voting booth
-confessional
-shower stall
-master bedroom
-motel room
-guestroom
-hotel room
-dormitory
-nursery
-day nursery
-great hall
-concert hall
-palace
-exhibition hall
-parlor
-drawing room
-press box
-command module
-cabin
-pilothouse
-cab
-luggage compartment
-cabinet
-cockpit
-stateroom
-car
-cable car
-stall
-drawing room
-terrarium
-cage
-playpen
-pen
-vivarium
-pound
-lock
-chicken yard
-chamber
-recess
-birdcage
-rabbit hutch
-hutch
-cow pen
-rodeo
-fold
-sounding board
-burial chamber
-firing chamber
-resonator
-furnace
-bomb shelter
-hyperbaric chamber
-repository
-mausoleum
-kiln
-blast furnace
-oast
-gas oven
-incinerator
-mihrab
-columbarium
-fire
-fireplace
-apse
-cellar
-cupboard
-stacks
-gallery
-amphitheater
-organ loft
-stoop
-sun deck
-front porch
-veranda
-deck
-back porch
-portico
-marina
-dry dock
-block
-dwelling
-tennis camp
-living quarters
-mobile home
-condominium
-apartment
-ward
-cellblock
-condominium
-yurt
-lodge
-vacation home
-hearth
-fixer-upper
-cliff dwelling
-homestead
-semi-detached house
-wigwam
-tepee
-accommodation
-first class
-cabin class
-bedsitting room
-flatlet
-chassis
-support
-framework
-pedestal
-buttress
-flying buttress
-abutment
-ribbing
-bustle
-window frame
-frame
-gantry
-honeycomb
-truss
-lattice
-cornice
-picture frame
-window
-climbing frame
-trellis
-airframe
-grate
-grape arbor
-walker
-casing
-tambour
-arbor
-rack
-mounting
-sash
-casement
-oriel
-bay window
-stained-glass window
-skylight
-display window
-rose window
-porthole
-transom
-clerestory
-dormer
-dormer window
-lancet window
-fanlight
-plate rack
-barbecue
-bicycle rack
-luggage rack
-dish rack
-towel rack
-passe-partout
-pave
-mount
-stronghold
-fortress
-fortification
-bastion
-keep
-kremlin
-acropolis
-alcazar
-martello tower
-fieldwork
-bastion
-escarpment
-palisade
-castle
-cenotaph
-megalith
-Seven Wonders of the Ancient World
-brass
-national monument
-pantheon
-dolmen
-menhir
-place of business
-institution
-university
-mercantile establishment
-office
-cabaret
-health spa
-plaza
-country store
-department store
-shop
-marketplace
-boutique
-salon
-shoe shop
-bookshop
-package store
-thriftshop
-junk shop
-toyshop
-cleaners
-bazaar
-gift shop
-florist
-drugstore
-garage
-delicatessen
-small stores
-barbershop
-stall
-tobacco shop
-newsstand
-butcher shop
-pizzeria
-confectionery
-convenience store
-bazaar
-agora
-grocery store
-open-air market
-supermarket
-hypermarket
-greengrocery
-souk
-farmer's market
-flea market
-newsroom
-box office
-headquarters
-correctional institution
-orphanage
-jail
-penitentiary
-prison
-toiletry
-weaponry
-equipment
-connection
-implement
-furnishing
-device
-ceramic
-system
-container
-conveyance
-medium
-deodorant
-bath oil
-cream
-lotion
-shaving cream
-hair spray
-mousse
-perfume
-hairdressing
-antiperspirant
-powder
-cosmetic
-bath salts
-hand cream
-cold cream
-sunscreen
-lanolin
-body lotion
-toner
-hand lotion
-after-shave
-potpourri
-patchouli
-perfumery
-cologne
-toilet water
-pomade
-brilliantine
-toilet powder
-talcum
-depilatory
-highlighter
-makeup
-face powder
-lip-gloss
-eyeshadow
-mascara
-lipstick
-rouge
-eyeliner
-eyebrow pencil
-armament
-defense system
-bomb
-ammunition
-naval weaponry
-bazooka
-artillery
-launcher
-cannon
-field artillery
-mortar
-basilisk
-hydrogen bomb
-atom bomb
-round
-shotgun shell
-recorder
-sports equipment
-photographic equipment
-naval equipment
-parasail
-gear
-satellite
-game equipment
-parachute
-electronic equipment
-apparatus
-automation
-material
-baggage
-tape recorder
-cassette recorder
-Dictaphone
-videocassette recorder
-baseball equipment
-croquet mallet
-clay pigeon
-skate
-wrestling mat
-cricket equipment
-basketball equipment
-javelin
-shuttlecock
-golf equipment
-spike
-stick
-boxing equipment
-boxing glove
-gymnastic apparatus
-weight
-baseball glove
-batting cage
-batting glove
-base
-batting helmet
-baseball bat
-home plate
-first base
-third base
-second base
-in-line skate
-Rollerblade
-roller skate
-ice skate
-hockey skate
-speed skate
-figure skate
-cricket bat
-wicket
-golfcart
-golf glove
-tee
-golf club
-wood
-iron
-driver
-spoon
-wedge
-midiron
-putter
-niblick
-pitching wedge
-sand wedge
-hockey stick
-polo mallet
-horizontal bar
-horse
-uneven parallel bars
-parallel bars
-trampoline
-balance beam
-vaulting horse
-pommel horse
-dumbbell
-barbell
-enlarger
-camera
-clapperboard
-film
-light meter
-box camera
-flash camera
-Polaroid camera
-point-and-shoot camera
-webcam
-motion-picture camera
-digital camera
-portrait camera
-reflex camera
-X-ray film
-reel
-negative
-regalia
-kit
-rig
-fishing gear
-stable gear
-rigging
-crown
-crown jewels
-sewing kit
-first-aid kit
-carpenter's kit
-layette
-drill rig
-drilling platform
-harness
-snaffle
-headgear
-saddle blanket
-halter
-bridle
-sputnik
-space station
-backboard
-ball
-puzzle
-pool table
-bowling pin
-man
-chip
-roulette wheel
-goal
-volleyball net
-pinball machine
-soccer ball
-pool ball
-bowling ball
-softball
-field hockey ball
-punching bag
-billiard ball
-croquet ball
-cricket ball
-tennis ball
-golf ball
-rugby ball
-cue ball
-medicine ball
-basketball
-eight ball
-ping-pong ball
-handball
-baseball
-racquetball
-bocce ball
-volleyball
-jigsaw puzzle
-crossword puzzle
-chessman
-white
-pawn
-basket
-net
-electronic fetal monitor
-monitor
-monitor
-television monitor
-telephone
-oscilloscope
-peripheral
-booster
-cassette player
-CD player
-receiver
-audio system
-lens
-playback
-television equipment
-circuitry
-cassette deck
-central processing unit
-mixer
-scanner
-tape player
-detector
-modem
-equalizer
-tape deck
-amplifier
-cellular telephone
-speakerphone
-desk phone
-pay-phone
-handset
-dial telephone
-radiotelephone
-television receiver
-radio receiver
-satellite receiver
-heterodyne receiver
-clock radio
-reproducer
-hi-fi
-stereo
-iPod
-Walkman
-video iPod
-ghetto blaster
-camcorder
-television camera
-pendulum
-purifier
-sequencer
-reformer
-duplicator
-heat pump
-semaphore
-tomograph
-ultracentrifuge
-generator
-incubator
-burner
-Foucault pendulum
-clock pendulum
-metronome
-Photostat
-photocopier
-Xerox
-facsimile
-mimeograph
-positron emission tomography scanner
-computerized axial tomography scanner
-gas burner
-blowtorch
-bunsen burner
-gas ring
-packaging
-blister pack
-roofing
-temporary hookup
-slip ring
-telephone line
-ligament
-junction
-hot line
-digital subscriber line
-land line
-binder
-wire
-chain
-concertina
-barbed wire
-paper chain
-anchor chain
-fob
-bicycle chain
-tire chain
-chatelaine
-joint
-contact
-dovetail
-welt
-hinge
-scarf joint
-weld
-seam
-mortise joint
-butt hinge
-strap hinge
-distributor point
-tread
-wiper
-bar
-tool
-utensil
-rubber eraser
-needle
-eraser
-stick
-brush
-hook
-sharpener
-sports implement
-leather strip
-swatter
-fire iron
-oar
-stick
-cleaning implement
-rod
-writing implement
-shovel
-split rail
-fret
-bolt
-rotor
-towel rail
-lever
-track
-handlebar
-crowbar
-stick
-key
-tappet
-pedal
-rocker arm
-gun trigger
-space bar
-backspace key
-shift key
-telegraph key
-accelerator
-sustaining pedal
-hand tool
-jack
-pestle
-garden tool
-plow
-comb
-drill
-cutting implement
-tamp
-garden rake
-rake
-stamp
-locking pliers
-pestle
-plunger
-pincer
-pliers
-soldering iron
-spade
-hammer
-pipe cutter
-wrench
-screwdriver
-trowel
-saw
-opener
-scraper
-shovel
-brick trowel
-spatula
-carpenter's hammer
-gavel
-mallet
-maul
-torque wrench
-pipe wrench
-adjustable wrench
-open-end wrench
-Allen wrench
-box wrench
-hacksaw
-folding saw
-handsaw
-pruner
-pruning saw
-corkscrew
-bottle opener
-can opener
-hedge trimmer
-lawn mower
-power mower
-riding mower
-power drill
-electric drill
-cutter
-twist bit
-bit
-blade
-knife blade
-bolt cutter
-cigar cutter
-edge tool
-scissors
-knife
-ax
-razor
-wire cutter
-chisel
-plane
-shears
-snips
-pruning shears
-secateurs
-carving knife
-Bowie knife
-pocketknife
-cleaver
-hunting knife
-case knife
-parer
-letter opener
-switchblade
-penknife
-battle-ax
-hatchet
-shaver
-straight razor
-safety razor
-cold chisel
-wood chisel
-jointer
-smooth plane
-spokeshave
-kitchen utensil
-ceramic ware
-funnel
-rolling pin
-reamer
-masher
-kitchenware
-squeezer
-mixer
-cookie cutter
-cooking utensil
-grater
-mincer
-eggbeater
-whisk
-blender
-pan
-Crock Pot
-chafing dish
-spatula
-griddle
-enamelware
-steamer
-cookie sheet
-cooker
-turner
-omelet pan
-stewing pan
-frying pan
-roaster
-wok
-saucepan
-graniteware
-cloisonne
-porcelain
-earthenware
-stoneware
-pottery
-Spode
-china
-bone china
-majolica
-faience
-knitting needle
-crochet needle
-walking stick
-matchstick
-club
-fiddlestick
-spindle
-stob
-staff
-drumstick
-mallet
-cane
-sword cane
-bat
-table-tennis racquet
-truncheon
-alpenstock
-flagpole
-crutch
-electric toothbrush
-toothbrush
-sable
-scrub brush
-hairbrush
-bristle brush
-shaving brush
-pencil sharpener
-steel
-cue
-racket
-squash racket
-tennis racket
-badminton racket
-thong
-strap
-cheekpiece
-rein
-noseband
-leading rein
-scull
-paddle
-besom
-scouring pad
-dustmop
-squeegee
-broom
-swab
-rotating shaft
-wand
-shaft
-piston rod
-kickstand
-axle
-pole
-fishing rod
-connecting rod
-tie rod
-driveshaft
-crankshaft
-transmission shaft
-spindle
-camshaft
-boom
-stilt
-ski pole
-clothes tree
-caber
-spar
-mast
-mast
-bowsprit
-yard
-mizzenmast
-royal mast
-mainmast
-foremast
-fly rod
-spinning rod
-pencil
-pen
-highlighter
-chalk
-crayon
-lead pencil
-ballpoint
-Sharpie
-quill
-fountain pen
-felt-tip pen
-furniture
-office furniture
-dining-room furniture
-wardrobe
-bedroom furniture
-table
-table
-wall unit
-lamp
-dining-room table
-washstand
-buffet
-cabinet
-baby bed
-bedstead
-lawn furniture
-credenza
-bookcase
-entertainment center
-etagere
-seat
-sectional
-chest of drawers
-file
-Rolodex
-card index
-vertical file
-clothes closet
-armoire
-bed
-berth
-platform bed
-hospital bed
-bunk
-trundle bed
-four-poster
-couch
-bunk bed
-twin bed
-sleigh bed
-single bed
-hammock
-Murphy bed
-double bed
-gaming table
-gueridon
-table-tennis table
-counter
-altar
-breakfast table
-stand
-conference table
-pedestal table
-kitchen table
-operating table
-tea table
-lectern
-worktable
-gateleg table
-dressing table
-desk
-drop-leaf table
-coffee table
-trestle table
-console table
-checkout
-bar
-meat counter
-reception desk
-salad bar
-snack bar
-drafting table
-lab bench
-writing desk
-secretary
-davenport
-dining table
-dinner table
-refectory table
-floor lamp
-table lamp
-reading lamp
-dresser
-china cabinet
-medicine chest
-bassinet
-crib
-carrycot
-cradle
-chair
-toilet seat
-stool
-sofa
-ottoman
-bench
-lawn chair
-chaise longue
-rocking chair
-swivel chair
-throne
-straight chair
-ladder-back
-highchair
-armchair
-Windsor chair
-folding chair
-wheelchair
-motorized wheelchair
-barber chair
-easy chair
-recliner
-Morris chair
-wing chair
-deck chair
-camp chair
-music stool
-taboret
-footstool
-settee
-daybed
-convertible
-love seat
-chesterfield
-studio couch
-park bench
-flat bench
-pew
-settle
-window seat
-chiffonier
-highboy
-bird feeder
-heater
-lighter
-signal
-converter
-crusher
-drive
-knocker
-peeler
-musical instrument
-shoehorn
-shock absorber
-machine
-conductor
-bait
-stabilizer
-filter
-mechanism
-acoustic device
-trap
-charger
-airfoil
-router
-pick
-energizer
-fan
-hydrofoil
-dental appliance
-adapter
-toy
-support
-optical device
-straightener
-tongs
-phonograph needle
-instrument
-comb
-remote control
-exercise device
-comforter
-washboard
-shredder
-water ski
-blower
-ventilator
-breathing device
-applicator
-skeleton key
-guitar pick
-restraint
-keyboard
-electrical device
-appliance
-fire extinguisher
-corrective
-reflector
-alarm
-electronic device
-snowshoe
-holding device
-memory device
-key
-noisemaker
-source of illumination
-indicator
-detector
-breathalyzer
-imprint
-afterburner
-horn
-elastic device
-ski
-lifting device
-solar heater
-electric heater
-radiator
-gas heater
-convector
-space heater
-stove
-cigar lighter
-match
-cairn
-sign
-street sign
-traffic light
-electrical converter
-catalytic converter
-inverter
-synchronous converter
-external drive
-CD-ROM drive
-internal drive
-stringed instrument
-electronic instrument
-keyboard instrument
-wind instrument
-bass
-percussion instrument
-dulcimer
-chordophone
-banjo
-zither
-samisen
-guitar
-bowed stringed instrument
-sitar
-lute
-mandola
-mandolin
-harp
-acoustic guitar
-Hawaiian guitar
-uke
-electric guitar
-viol
-violin
-cello
-viola
-viola da gamba
-Stradavarius
-theremin
-electric organ
-synthesizer
-piano
-clavier
-organ
-accordion
-grand piano
-upright
-spinet
-mechanical piano
-baby grand
-concert grand
-harpsichord
-spinet
-ocarina
-woodwind
-brass
-organ pipe
-free-reed instrument
-whistle
-pipe
-kazoo
-flute
-beating-reed instrument
-double-reed instrument
-single-reed instrument
-bassoon
-oboe
-clarinet
-sax
-baritone
-bugle
-flugelhorn
-French horn
-trombone
-cornet
-harmonium
-harmonica
-concertina
-chanter
-panpipe
-bagpipe
-fipple flute
-pennywhistle
-drone
-bass fiddle
-bass horn
-bass guitar
-euphonium
-handbell
-bones
-gong
-vibraphone
-steel drum
-marimba
-glockenspiel
-chime
-kettle
-maraca
-drum
-cymbal
-bongo
-bass drum
-tambourine
-snare drum
-tenor drum
-slot machine
-power shovel
-press
-backhoe
-printer
-machine tool
-motor
-snow thrower
-cash machine
-farm machine
-computer
-Zamboni
-mill
-staple gun
-power tool
-concrete mixer
-stapler
-slicer
-textile machine
-record player
-calculator
-vending machine
-slot
-automat
-garlic press
-bench press
-hydraulic press
-punch press
-character printer
-impact printer
-printer
-drum printer
-line printer
-laser printer
-Linotype
-thermal printer
-portable
-typewriter
-bar printer
-wire matrix printer
-dot matrix printer
-bubble jet printer
-ink-jet printer
-shaper
-drill press
-grinder
-lathe
-miller
-engine
-electric motor
-heat engine
-jet engine
-automobile engine
-aircraft engine
-generator
-steam engine
-internal-combustion engine
-wind turbine
-gasoline engine
-diesel
-outboard motor
-radial engine
-rocket
-fanjet
-booster
-space rocket
-alternator
-windmill
-starter
-kick starter
-cultivator
-haymaker
-combine
-thresher
-harvester
-disk harrow
-harrow
-slide rule
-web site
-home computer
-server
-digital computer
-supercomputer
-workstation
-personal computer
-portable computer
-desktop computer
-notebook
-planner
-laptop
-hand-held computer
-pepper mill
-water mill
-meat grinder
-coffee mill
-treadmill
-windmill
-electric hammer
-power saw
-buffer
-circular saw
-chain saw
-table saw
-saber saw
-bandsaw
-spinning wheel
-loom
-jukebox
-gramophone
-abacus
-adding machine
-hand calculator
-semiconductor device
-wire
-cord
-heat sink
-cable
-microprocessor
-transistor
-light-emitting diode
-chip
-filament
-jumper cable
-telephone wire
-patchcord
-telephone cord
-power cord
-extension cord
-ethernet cable
-electrical cable
-printer cable
-power line
-fisherman's lure
-fly
-dry fly
-wet fly
-streamer fly
-outrigger
-vane
-strainer
-air filter
-oil filter
-sieve
-tea-strainer
-colander
-fusee drive
-android
-radiator
-mechanical device
-rotating mechanism
-rotor head
-carriage
-control
-power steering
-automaton
-action
-cooling system
-gear
-tape drive
-film advance
-sprinkler
-propeller
-anchor
-golf-club head
-weathervane
-machine
-seeder
-pump
-gearshift
-ride
-bumper
-hook
-ski binding
-coupling
-record changer
-swing
-windshield wiper
-winder
-winder
-diaphragm
-shutter
-escapement
-broadcaster
-curler
-splint
-compressor
-air compressor
-carburetor
-dildo
-cartridge holder
-trapeze
-gearing
-stator
-airplane propeller
-screw
-pulley
-wheel
-idle pulley
-lever
-inclined plane
-millwheel
-waterwheel
-roller
-bicycle wheel
-caster
-grinding wheel
-rowel
-fifth wheel
-wagon wheel
-waterwheel
-car wheel
-sprocket
-pinwheel
-potter's wheel
-gear
-driving wheel
-paddlewheel
-roulette
-spur gear
-bevel gear
-pinion
-ramp
-ax head
-screw
-grease-gun
-gas pump
-bicycle pump
-sump pump
-hand pump
-centrifugal pump
-Ferris wheel
-roller coaster
-carousel
-universal joint
-clutch
-freewheel
-disk clutch
-bobbin
-reel
-shuttle
-blade
-gyroscope
-circle
-rotor
-paddle
-impeller
-fan blade
-disk
-puck
-brake disk
-token
-Frisbee
-planchet
-tail rotor
-main rotor
-valve
-steering wheel
-governor
-joystick
-regulator
-switch
-ball valve
-butterfly valve
-timer
-flywheel
-faucet
-thermostat
-aperture
-mixing faucet
-stopcock
-toggle switch
-push button
-dial
-horn button
-mouse button
-doorbell
-bell push
-flintlock
-movement
-gunlock
-cooling tower
-evaporative cooler
-air conditioner
-gearset
-four-wheel drive
-whistle
-silencer
-megaphone
-hearing aid
-bell
-cowbell
-church bell
-dinner bell
-spider web
-mousetrap
-lobster pot
-web
-net
-landing net
-fishnet
-vertical stabilizer
-spoiler
-spoiler
-rotor blade
-flap
-rudder
-horizontal stabilizer
-wing
-exhaust fan
-electric fan
-brace
-denture
-backboard
-stirrup
-pier
-pier
-back
-shelf
-landing gear
-baluster
-spoke
-base
-step
-brace
-pillow block
-bearing
-rocker
-coat hanger
-harp
-rest
-bracket
-tailstock
-bookend
-structural member
-headstock
-seat
-thrust bearing
-hanger
-rack
-harness
-cantle
-ladder-back
-bookshelf
-mantel
-neck brace
-knee brace
-ankle brace
-back brace
-arm
-headrest
-chin rest
-armrest
-sconce
-corbel
-shelf bracket
-sill
-riser
-upright
-brace
-tread
-beam
-windowsill
-doorsill
-stile
-jamb
-column
-post
-support column
-caryatid
-goalpost
-newel post
-bollard
-lamppost
-telephone pole
-maypole
-timber
-rundle
-tie
-rafter
-girder
-timber
-floor joist
-joist
-car seat
-pillion
-plane seat
-saddle
-chair
-bicycle seat
-bucket seat
-backseat
-stock saddle
-English saddle
-tripod
-spice rack
-magazine rack
-music stand
-camera tripod
-easel
-autofocus
-projector
-finder
-laser
-lens
-objective
-condenser
-camera lens
-anastigmat
-contact
-sunglass
-eyepiece
-field lens
-Fresnel lens
-portrait lens
-closeup lens
-telephoto lens
-wide-angle lens
-plotter
-scientific instrument
-measuring instrument
-weapon
-guillotine
-drafting instrument
-analyzer
-navigational instrument
-optical instrument
-medical instrument
-instrument of punishment
-catapult
-extractor
-theodolite
-riding crop
-tachymeter
-collider
-microtome
-accelerator
-stroboscope
-magnifier
-console
-telescope
-microscope
-astronomical telescope
-equatorial
-optical telescope
-radio telescope
-refracting telescope
-field glass
-reflecting telescope
-Cassegrainian telescope
-Newtonian telescope
-Schmidt telescope
-Maksutov telescope
-electron microscope
-field-emission microscope
-light microscope
-binocular microscope
-hand glass
-operating microscope
-compound microscope
-loupe
-oximeter
-dropper
-refractometer
-rangefinder
-barometer
-pedometer
-thermometer
-astrolabe
-measuring stick
-gauge
-timepiece
-aneroid barometer
-caliper
-potentiometer
-tachometer
-scale
-tape
-meter
-hygrometer
-sextant
-rule
-altazimuth
-pyrometer
-meat thermometer
-water gauge
-vacuum gauge
-anemometer
-gasoline gauge
-pressure gauge
-manometer
-sphygmomanometer
-atomic clock
-clock
-watch
-sundial
-timer
-hourglass
-grandfather clock
-digital clock
-alarm clock
-wall clock
-analog clock
-pendulum clock
-cuckoo clock
-digital watch
-analog watch
-pocket watch
-wristwatch
-stopwatch
-parking meter
-chronograph
-vernier caliper
-micrometer
-balance
-analytical balance
-electronic balance
-electric meter
-odometer
-ammeter
-speedometer
-ohmmeter
-water meter
-voltmeter
-magnetometer
-tomahawk
-gun
-bow
-bow and arrow
-brass knucks
-knife
-sword
-stun gun
-projectile
-antiaircraft
-firearm
-set gun
-air gun
-gas gun
-paintball gun
-cannon
-autoloader
-pistol
-twenty-two
-Mauser
-muzzle loader
-rifle
-repeating firearm
-semiautomatic firearm
-automatic firearm
-Garand rifle
-Luger
-semiautomatic pistol
-automatic rifle
-assault rifle
-automatic pistol
-machine gun
-submachine gun
-burp gun
-Uzi
-Kalashnikov
-Tommy gun
-Colt
-derringer
-revolver
-gat
-flintlock
-musket
-sniper rifle
-Winchester
-carbine
-crossbow
-longbow
-khukuri
-bayonet
-machete
-dagger
-rapier
-fencing sword
-broadsword
-cavalry sword
-saber
-epee
-foil
-bullet
-cannonball
-compass
-protractor
-artificial horizon
-depth finder
-magnetic compass
-compass
-binoculars
-spectacles
-projector
-telescopic sight
-goggles
-sunglasses
-slide projector
-front projector
-movie projector
-overhead projector
-hypodermic syringe
-cardiograph
-syringe
-stethoscope
-laryngoscope
-otoscope
-surgical instrument
-retractor
-hemostat
-pillory
-rattan
-exercise bike
-treadmill
-respirator
-snorkel
-oxygen mask
-aqualung
-paintbrush
-spray gun
-brake
-handcuff
-fastener
-seat belt
-leash
-safety belt
-brake system
-muzzle
-chain
-bolt
-buckle
-knot
-cleat
-clothespin
-catch
-pin
-dowel
-screw
-slide fastener
-button
-seal
-paper fastener
-lock
-thumbtack
-locker
-clasp
-clip
-carabiner
-nail
-toggle
-nut and bolt
-bowline
-bow
-latch
-hasp
-rivet
-hairpin
-skewer
-hatpin
-brochette
-bobby pin
-barrette
-safety pin
-shirt button
-coat button
-washer
-gasket
-head gasket
-O ring
-padlock
-sash fastener
-latch
-combination lock
-doorlock
-paper clip
-bulldog clip
-hair slide
-hydraulic brake
-disk brake
-drum brake
-typewriter keyboard
-QWERTY keyboard
-computer keyboard
-piano keyboard
-circuit
-Segway
-jack
-control panel
-telephone jack
-circuit breaker
-plug
-electrolytic
-dashboard
-transducer
-solar cell
-antenna
-capacitor
-spark plug
-relay
-surge suppressor
-solar array
-battery
-Tesla coil
-closed circuit
-wiring
-computer circuit
-integrated circuit
-module
-printed circuit
-interface
-CPU board
-circuit board
-mosaic
-electro-acoustic transducer
-earphone
-microphone
-loudspeaker
-telephone receiver
-headset
-condenser microphone
-cardioid microphone
-tweeter
-bullhorn
-tannoy
-woofer
-subwoofer
-omnidirectional antenna
-directional antenna
-radio antenna
-television antenna
-dish
-scanner
-yagi
-voltaic battery
-flashlight battery
-lead-acid battery
-pack
-prosthesis
-solar dish
-mirror
-hand glass
-car mirror
-rearview mirror
-outside mirror
-burglar alarm
-automobile horn
-shofar
-fire alarm
-readout
-scanner
-tube
-display
-personal digital assistant
-dongle
-trackball
-mouse
-answering machine
-hearing aid
-beeper
-triode
-pentode
-computer monitor
-monitor
-screen
-digital display
-liquid crystal display
-flat panel display
-window
-dialog box
-caller ID
-computer screen
-background
-C-clamp
-chuck
-collet
-holder
-vise
-candlestick
-cigarette holder
-candelabrum
-menorah
-Menorah
-cache
-optical disk
-magnetic disk
-memory
-magnetic tape
-recording
-auxiliary storage
-compact disk
-videodisk
-CD-ROM
-CD-R
-audio CD
-hard disc
-diskette
-flash memory
-random-access memory
-videotape
-cassette tape
-tape
-phonograph record
-LP
-seventy-eight
-lamp
-light
-flash
-lantern
-candle
-neon lamp
-vigil light
-taillight
-gas lamp
-oil lamp
-hurricane lamp
-fluorescent lamp
-streetlight
-spotlight
-electric lamp
-jack-o'-lantern
-Chinese lantern
-flashlight
-light bulb
-penlight
-headlight
-room light
-strip lighting
-fairy light
-sconce
-searchlight
-night-light
-blinker
-torch
-flood
-fuel gauge
-gnomon
-dial
-vernier scale
-pointer
-light pen
-hand
-sweep hand
-minute hand
-second hand
-hour hand
-spring
-rubber band
-coil spring
-box spring
-hoist
-winch
-elevator
-crane
-wheel and axle
-derrick
-maze
-communication system
-network
-Global Positioning System
-resonator
-exhaust
-mechanical system
-computer system
-scaffolding
-reticle
-walkie-talkie
-radio
-telecommunication system
-telephone system
-intercommunication system
-interphone
-television
-satellite television
-surveillance system
-color television
-local area network
-superhighway
-ethernet
-wireless local area network
-production line
-linkage
-suspension
-fuel injection
-planter
-trophy case
-wastepaper basket
-dish
-bread-bin
-dispenser
-pot
-bunker
-reliquary
-cup
-bag
-cassette
-Dumpster
-bag
-measuring cup
-glass
-paintball
-measure
-envelope
-shaker
-piggy bank
-basket
-sewing basket
-savings bank
-powder horn
-can
-wheeled vehicle
-workbasket
-bin
-canister
-mold
-cargo container
-videocassette
-case
-case
-vessel
-drawer
-receptacle
-package
-watering can
-box
-cocotte
-Petri dish
-gravy boat
-serving dish
-tureen
-sugar bowl
-bowl
-casserole
-ramekin
-butter dish
-salad bowl
-mixing bowl
-porringer
-cereal bowl
-soup bowl
-punch bowl
-roll-on
-aerosol
-soap dispenser
-atomizer
-inhaler
-demitasse
-beaker
-kylix
-coffee cup
-chalice
-teacup
-Dixie cup
-evening bag
-shoulder bag
-clutch bag
-reticule
-backpack
-sachet
-beanbag
-sandbag
-carryall
-pannier
-duffel bag
-book bag
-tool bag
-mailbag
-purse
-drawstring bag
-envelope
-saddlebag
-sack
-pouch
-shopping bag
-toilet bag
-gamebag
-kitbag
-plastic bag
-golf bag
-sleeping bag
-gunnysack
-grocery bag
-sporran
-pocket
-waist pack
-fanny pack
-hip pocket
-patch pocket
-flute
-tumbler
-water glass
-bumper
-liqueur glass
-snifter
-shot glass
-beer glass
-rummer
-goblet
-wineglass
-cocktail shaker
-saltshaker
-pepper shaker
-pannier
-clothes hamper
-hamper
-breadbasket
-shopping basket
-wicker basket
-milk can
-beer can
-soda can
-pedicab
-camper trailer
-rolling stock
-motor scooter
-self-propelled vehicle
-unicycle
-wagon
-bassinet
-handcart
-baby buggy
-bicycle
-horse-drawn vehicle
-trailer
-car
-tricycle
-armored vehicle
-recreational vehicle
-tracked vehicle
-snowmobile
-bulldozer
-locomotive
-streetcar
-motor vehicle
-tractor
-forklift
-armored personnel carrier
-armored car
-dune buggy
-camper
-van
-shunter
-diesel locomotive
-electric locomotive
-tank engine
-traction engine
-steam locomotive
-diesel-electric locomotive
-diesel-hydraulic locomotive
-hearse
-truck
-amphibian
-four-wheel drive
-motorcycle
-go-kart
-car
-snowplow
-fire engine
-van
-trailer truck
-transporter
-garbage truck
-ladder truck
-tow truck
-dump truck
-tractor
-pickup
-delivery truck
-moving van
-passenger van
-police van
-bookmobile
-trail bike
-moped
-beach wagon
-loaner
-Model T
-electric
-minivan
-convertible
-compact
-cab
-shooting brake
-racer
-hatchback
-roadster
-berlin
-sport utility
-sedan
-jeep
-limousine
-cruiser
-ambulance
-used-car
-stock car
-subcompact
-pace car
-hot rod
-sports car
-coupe
-covered wagon
-cart
-horse cart
-dumpcart
-jinrikisha
-pony cart
-oxcart
-tea cart
-laundry cart
-serving cart
-barrow
-shopping cart
-hand truck
-bicycle-built-for-two
-safety bicycle
-push-bike
-mountain bike
-carriage
-gharry
-buggy
-stagecoach
-four-wheeler
-baggage car
-freight car
-passenger car
-cabin car
-boxcar
-tank car
-nonsmoker
-Pullman
-dining car
-smoker
-recycling bin
-ashcan
-litterbin
-sandbox
-pig bed
-briefcase
-compact
-dispatch case
-kit
-wallet
-cardcase
-portfolio
-ditty bag
-cigarette case
-shoe
-gun case
-attache case
-locket
-writing desk
-watch case
-baggage
-glasses case
-hand luggage
-satchel
-bag
-trunk
-hatbox
-garment bag
-weekender
-carpetbag
-portmanteau
-overnighter
-valise
-boiler
-flagon
-bowl
-ladle
-bottle
-bottle
-pot
-pitcher
-bathtub
-mortar
-bucket
-drinking vessel
-cream pitcher
-wine bucket
-pressure cooker
-tub
-inkwell
-tin
-basin
-monstrance
-autoclave
-churn
-barrel
-tank
-jar
-censer
-toilet bowl
-fishbowl
-scoop
-soup ladle
-smelling bottle
-pop bottle
-water bottle
-jug
-catsup bottle
-gourd
-pill bottle
-carboy
-flask
-beer bottle
-ink bottle
-demijohn
-whiskey bottle
-cruet
-wine bottle
-carafe
-phial
-whiskey jug
-water jug
-hipflask
-Erlenmeyer flask
-thermos
-canteen
-vacuum flask
-magnum
-jeroboam
-saucepot
-teapot
-Dutch oven
-urn
-stockpot
-kettle
-caldron
-percolator
-teakettle
-coffeepot
-coffee urn
-samovar
-tea urn
-sitz bath
-hot tub
-footbath
-mug
-loving cup
-tankard
-coffee mug
-toby
-beer mug
-bidet
-birdbath
-washbasin
-baptismal font
-beer barrel
-wine cask
-keg
-gas tank
-water heater
-septic tank
-aquarium
-reservoir
-water tower
-rain barrel
-canopic jar
-amphora
-cookie jar
-beaker
-urn
-Mason jar
-vase
-crock
-jampot
-plate
-tray
-cat box
-dustpan
-chamberpot
-salver
-garbage
-in-basket
-hot-water bottle
-ossuary
-socket
-ashtray
-packet
-bundle
-deck
-bale
-hay bale
-pack
-ballot box
-carton
-coffin
-shoebox
-snuffbox
-pencil box
-crate
-bandbox
-window box
-chest
-strongbox
-cereal box
-mailbox
-casket
-bier
-packing box
-toolbox
-toy box
-coffer
-hope chest
-treasure chest
-cedar chest
-cash register
-safe-deposit
-cashbox
-safe
-tramway
-chairlift
-sidecar
-public transport
-semitrailer
-horsebox
-vehicle
-ski tow
-roll-on roll-off
-trailer
-shipping
-litter
-express
-shuttle bus
-train
-bus
-local
-freight liner
-passenger train
-subway train
-mail train
-freight train
-commuter
-bullet train
-trolleybus
-minibus
-school bus
-steamroller
-bumper car
-rocket
-military vehicle
-missile
-craft
-sled
-half track
-tank
-panzer
-personnel carrier
-Humvee
-aircraft
-vessel
-spacecraft
-hovercraft
-heavier-than-air craft
-stealth aircraft
-lighter-than-air craft
-hang glider
-glider
-helicopter
-warplane
-airplane
-autogiro
-bomber
-amphibian
-propeller plane
-airliner
-biplane
-floatplane
-jet
-fighter
-stealth bomber
-seaplane
-airbus
-widebody aircraft
-jumbojet
-jetliner
-stealth fighter
-interceptor
-airship
-blimp
-balloon
-hot-air balloon
-boat
-trawler
-yacht
-ship
-sailing vessel
-bareboat
-lifeboat
-police boat
-gondola
-sea boat
-barge
-river boat
-tugboat
-punt
-pilot boat
-small boat
-ferry
-tender
-canal boat
-fireboat
-motorboat
-dredger
-pontoon
-houseboat
-skiff
-canoe
-dinghy
-racing boat
-coracle
-yawl
-gig
-jolly boat
-rowing boat
-kayak
-outrigger canoe
-dugout canoe
-racing gig
-racing skiff
-speedboat
-outboard motorboat
-cabin cruiser
-hydrofoil
-shipwreck
-wreck
-passenger ship
-pirate
-lightship
-hospital ship
-steamer
-cargo ship
-sister ship
-warship
-liner
-luxury liner
-cargo liner
-cruise ship
-paddle steamer
-sternwheeler
-bottom
-container ship
-banana boat
-oil tanker
-submarine
-guided missile cruiser
-frigate
-battleship
-guided missile frigate
-aircraft carrier
-man-of-war
-destroyer
-attack submarine
-nautilus
-yawl
-clipper
-felucca
-sloop
-ketch
-dhow
-sailboat
-bark
-schooner
-windjammer
-trimaran
-catamaran
-catboat
-space shuttle
-space capsule
-dogsled
-bobsled
-bobsled
-stretcher
-covered couch
-telecommunication
-vehicle
-print media
-broadcasting
-telephone
-radiotelephone
-television
-reception
-radio
-cable television
-high-definition television
-three-way calling
-call
-voice mail
-press
-journalism
-magazine
-newspaper
-pulp
-slick
-comic book
-news magazine
-tabloid
-daily
-gazette
-Fleet Street
-yellow journalism
-pillow
-pad
-sanitary napkin
-beer mat
-futon
-carpet pad
-range hood
-screen
-top
-footwear
-protective covering
-cloak
-wrapping
-upholstery
-cloth covering
-mask
-finger
-floor cover
-coating
-canopy
-flap
-domino
-folder
-planking
-earmuff
-camouflage
-shoji
-cap
-manhole cover
-lid
-radiator cap
-bottlecap
-nipple
-clog
-shoe
-arctic
-boot
-flats
-slipper
-overshoe
-sabot
-slingback
-chukka
-saddle oxford
-spectator pump
-brogan
-wing tip
-walker
-blucher
-anklet
-cleats
-gaiter
-Loafer
-running shoe
-oxford
-bowling shoe
-plimsoll
-pump
-sandal
-chopine
-pusher
-talaria
-flip-flop
-espadrille
-jodhpur
-buskin
-ski boot
-hip boot
-riding boot
-rubber boot
-Hessian boot
-waders
-cowboy boot
-mule
-bootee
-cold frame
-cloche
-washboard
-toecap
-mulch
-shield
-bracer
-screen
-sheathing
-bell jar
-shade
-shelter
-splashboard
-testudo
-roof
-faceplate
-hood
-sheath
-cap
-mask
-facing
-crystal
-calash
-armor
-binder
-binding
-housing
-blind
-lining
-plate
-horseshoe
-armor plate
-breastplate
-helmet
-cannon
-knee piece
-pickelhaube
-sallet
-window screen
-fire screen
-windshield
-mosquito net
-lampshade
-parasol
-lean-to
-bell cote
-sentry box
-birdhouse
-canopy
-kennel
-awning
-umbrella
-gamp
-gable roof
-sunroof
-mansard
-dome
-hip roof
-tile roof
-housetop
-vault
-slate roof
-gambrel
-thatch
-cupola
-geodesic dome
-onion dome
-barrel vault
-ribbed vault
-holster
-scabbard
-shoulder holster
-hubcap
-thimble
-distributor cap
-lens cap
-gasmask
-face mask
-ski mask
-catcher's mask
-body armor
-shield
-chain mail
-bulletproof vest
-corselet
-cuirass
-cabinet
-radome
-boot
-window blind
-jalousie
-curtain
-shutter
-Venetian blind
-window shade
-roller blind
-theater curtain
-shower curtain
-bushing
-brake lining
-gift wrapping
-envelope
-cellophane
-book jacket
-jacket
-plastic wrap
-shoulder
-pant leg
-leg
-back
-cosy
-bandage
-bosom
-slipcover
-bedclothes
-sleeve
-blindfold
-eyepatch
-skirt
-seat
-Band Aid
-swathe
-cast
-elastic bandage
-quilt
-afghan
-blanket
-bedspread
-mattress cover
-patchwork
-eiderdown
-crazy quilt
-coverlet
-quilted bedspread
-raglan sleeve
-long sleeve
-rug
-doormat
-mat
-scatter rug
-shag rug
-prayer rug
-broadloom
-stair-carpet
-red carpet
-Brussels carpet
-fixative
-gold plate
-verdigris
-paint
-nail polish
-gilt
-couch
-enamel
-veneer
-finger paint
-enamel
-encaustic
-oil paint
-water-base paint
-latex paint
-whitewash
-earflap
-pocket flap
-lapel
-tongue
-revers
-tent-fly
-file folder
-matchbook
-plush
-muslin
-tarpaulin
-velvet
-batik
-khaki
-belting
-sacking
-diaper
-voile
-duffel
-chenille
-cotton flannel
-toweling
-crinoline
-panting
-chintz
-felt
-cotton
-velveteen
-satin
-knit
-sateen
-print
-flannel
-webbing
-gabardine
-camouflage
-worsted
-cashmere
-tartan
-mohair
-brocade
-velour
-shirttail
-boucle
-madras
-net
-paisley
-yoke
-percale
-piece of cloth
-moquette
-terry
-rayon
-acetate rayon
-cord
-permanent press
-chiffon
-burlap
-ticking
-basket weave
-lace
-sheeting
-georgette
-poplin
-denim
-flannelette
-shantung
-camel's hair
-nylon
-drapery
-gauze
-organza
-foulard
-gingham
-wool
-suede cloth
-taffeta
-leatherette
-tweed
-organdy
-canopy
-etamine
-damask
-oilcloth
-tapestry
-broadcloth
-pique
-homespun
-tricot
-double knit
-jersey
-gauze
-tulle
-chicken wire
-handkerchief
-groundsheet
-dustcloth
-dishrag
-towel
-bandanna
-gusset
-bib
-sail
-patch
-hand towel
-paper towel
-dishtowel
-fore-and-aft sail
-foresail
-spinnaker
-headsail
-topsail
-mainsail
-balloon sail
-jib
-mizzen
-gaff topsail
-lugsail
-staysail
-lateen
-flash
-shoulder patch
-narrow wale
-Bedford cord
-macrame
-pillow lace
-raft
-life preserver
-life buoy
-Mae West
-life jacket
-stone
-brick
-lumber
-bricks and mortar
-tile
-concrete
-quoin
-millstone
-stele
-hone
-grindstone
-curbstone
-gravestone
-firebrick
-mud brick
-clinker
-adobe
-strip
-chipboard
-slat
-fingerboard
-toothpick
-hip tile
-pantile
-cornice
-embellishment
-graffito
-epergne
-necklet
-marquetry
-brass
-garnish
-arabesque
-design
-adornment
-frieze
-lambrequin
-tattoo
-mihrab
-emblem
-swastika
-herringbone
-spot
-flag
-banner
-totem pole
-crucifix
-fleur-de-lis
-macule
-parhelion
-jewelry
-frill
-lavaliere
-peplum
-bangle
-cigar band
-aigrette
-bracelet
-bling
-pendant earring
-necklace
-ghat
-path
-road
-passage
-sidewalk
-towpath
-pedestrian crossing
-highway
-carriageway
-thoroughfare
-trail
-divided highway
-expressway
-arterial road
-autostrada
-autobahn
-street
-street
-piste
-horse-trail
-adit
-conduit
-passageway
-tube
-sluice
-snorkel
-waterspout
-catheter
-barrel
-pipe
-hookah
-tailpipe
-drain
-culvert
-soil pipe
-tunnel
-stairwell
-gangway
-catacomb
-railroad tunnel
-tape
-band
-inkle
-adhesive tape
-plaster
-cellulose tape
-headstall
-girdle
-tire
-armlet
-radial
-car tire
-tableware
-riband
-cutlery
-glass
-hollowware
-platter
-spoon
-table knife
-fork
-Spork
-soupspoon
-teaspoon
-sugar spoon
-wooden spoon
-iced-tea spoon
-tablespoon
-dessert spoon
-case knife
-butter knife
-steak knife
-tablefork
-carving fork
-airfield
-telpherage
-air terminal
-airport
-menagerie
-storehouse
-station
-warehouse
-granary
-crib
-mineshaft
-ditch
-irrigation ditch
-furrow
-consumer goods
-linen
-clothing
-appliance
-leisure wear
-grey
-blue
-nightwear
-protective garment
-outerwear
-neckpiece
-knitwear
-loungewear
-apparel
-collar
-military uniform
-headdress
-pajama
-garment
-array
-woman's clothing
-overall
-glove
-accessory
-black
-footwear
-attire
-ready-to-wear
-beachwear
-man's clothing
-street clothes
-slip-on
-shin guard
-overall
-pressure suit
-arm guard
-foul-weather gear
-diving suit
-apron
-shoulder pad
-coverall
-chest protector
-elbow pad
-spacesuit
-knee pad
-gown
-vestment
-chasuble
-academic gown
-battle dress
-fatigues
-dress uniform
-khakis
-helmet
-hood
-turban
-hat
-cap
-cowl
-tiara
-football helmet
-hard hat
-crash helmet
-sunhat
-fur hat
-cowboy hat
-bearskin
-boater
-snap-brim hat
-fedora
-cavalier hat
-sombrero
-tricorn
-beaver
-porkpie
-bonnet
-pith hat
-bowler hat
-millinery
-cloche
-pillbox
-baseball cap
-coonskin cap
-shower cap
-kepi
-balaclava
-fez
-tam
-beret
-skullcap
-cloth cap
-ski cap
-watch cap
-bathing cap
-mortarboard
-yarmulke
-beanie
-head covering
-scarf
-romper
-diaper
-wraparound
-robe
-wet suit
-legging
-skirt
-undergarment
-separate
-vest
-shirt
-overgarment
-hose
-burqa
-trouser
-trouser
-straitjacket
-fur
-neckwear
-sweat suit
-leotard
-swimsuit
-hand-me-down
-raglan
-suit
-sweater
-gown
-face veil
-niqab
-chador
-mantilla
-muffler
-headscarf
-tudung
-feather boa
-stole
-hijab
-khimar
-dressing gown
-kimono
-abaya
-bathrobe
-gaiter
-spat
-overskirt
-grass skirt
-miniskirt
-kilt
-maxi
-ballet skirt
-dirndl
-sarong
-hoopskirt
-petticoat
-brassiere
-foundation garment
-singlet
-garter belt
-crinoline
-underwear
-body stocking
-camisole
-uplift
-chemise
-underpants
-corset
-panty girdle
-roll-on
-lingerie
-long johns
-BVD
-undies
-nightgown
-bloomers
-thong
-bikini pants
-briefs
-pantie
-drawers
-work-shirt
-kurta
-jersey
-dashiki
-polo shirt
-coat
-cloak
-snowsuit
-surcoat
-duffel coat
-sheepskin coat
-frock coat
-lab coat
-greatcoat
-jacket
-raincoat
-capote
-sack coat
-fur coat
-mess jacket
-single-breasted jacket
-bomber jacket
-pea jacket
-swallow-tailed coat
-doublet
-bolero
-parka
-oilskin
-trench coat
-mink
-sable coat
-poncho
-toga virilis
-toga
-kameez
-serape
-tunic
-shawl
-caftan
-short pants
-pajama
-sweat pants
-salwar
-breeches
-chino
-slacks
-jodhpurs
-pedal pusher
-long trousers
-jean
-cords
-Levi's
-stretch pants
-bellbottom trousers
-buckskins
-hot pants
-Bermuda shorts
-lederhosen
-necktie
-cravat
-bolo tie
-Windsor tie
-bow tie
-black tie
-maillot
-swimming trunks
-bikini
-double-breasted suit
-pinstripe
-single-breasted suit
-pants suit
-business suit
-three-piece suit
-two-piece
-turtleneck
-cardigan
-sweatshirt
-pullover
-top
-G-string
-camisole
-dress
-bodice
-blouse
-halter
-cocktail dress
-sari
-caftan
-sundress
-chemise
-strapless
-gown
-jumper
-dirndl
-bridal gown
-tea gown
-ball gown
-gauntlet
-mitten
-kid glove
-belt
-furnishing
-money belt
-holster
-cartridge belt
-hosiery
-tights
-sock
-stocking
-pantyhose
-maillot
-athletic sock
-tabi
-knee-high
-argyle
-nylons
-Christmas stocking
-formalwear
-ensemble
-outfit
-ao dai
-costume
-fancy dress
-costume
-frock
-sportswear
-academic costume
-disguise
-hairpiece
-dinner jacket
-balldress
-dinner dress
-dress suit
-Afro-wig
-toupee
-wig
-dress hat
-brace
-athletic supporter
-home appliance
-dryer
-vacuum
-iron
-trouser press
-curling iron
-white goods
-sewing machine
-serger
-kitchen appliance
-Hoover
-travel iron
-steam iron
-dishwasher
-refrigerator
-washer
-cooler
-electric refrigerator
-ice machine
-deep-freeze
-toaster
-microwave
-toaster oven
-coffee maker
-hot plate
-waffle iron
-disposal
-espresso maker
-stove
-oven
-food processor
-ice maker
-cookstove
-electric range
-gas range
-Primus stove
-broiler
-rotisserie
-Dutch oven
-gas oven
-hand blower
-clothes dryer
-spin dryer
-tumble-dryer
-wringer
-bath towel
-doily
-Turkish towel
-bed linen
-pillow sham
-sheet
-tinfoil
-plywood
-doorplate
-board
-drumhead
-panel
-laminate
-blackboard
-snowboard
-Sheetrock
-surfboard
-skateboard
-sideboard
-scoreboard
-wainscot
-headboard
-chandelier
-plumbing fixture
-soap dish
-toilet
-shower
-water faucet
-flush toilet
-potty seat
-rope
-cord
-lasso
-bungee
-spun yarn
-cordage
-thread
-bootlace
-wick
-lanyard
-floss
-woof
-worsted
-organism
-cell
-mistletoe
-plant
-animal
-microorganism
-bryophyte
-person
-fungus
-benthos
-flowering maple
-vascular plant
-strangler
-aquatic
-annual
-houseplant
-poisonous plant
-agave
-pteridophyte
-spermatophyte
-aquatic plant
-herb
-vine
-woody plant
-weed
-cultivar
-bulbous plant
-succulent
-American agave
-maguey
-maguey
-sansevieria
-dracaena
-mother-in-law's tongue
-fern ally
-fern
-club moss
-scouring rush
-ground pine
-ground cedar
-ground fir
-flowering fern
-lady fern
-Boston fern
-flowering fern
-royal fern
-tree fern
-oak fern
-common polypody
-mountain fern
-shield fern
-deer fern
-wood fern
-American maidenhair fern
-hart's-tongue
-soft shield fern
-holly fern
-maidenhair
-holly fern
-water clover
-sensitive fern
-Christmas fern
-angiopteris
-soft tree fern
-male fern
-marginal wood fern
-angiosperm
-gymnosperm
-barbados cherry
-dicot
-flower
-wildflower
-commelina
-woodland star
-nigella
-black-eyed Susan
-mistflower
-calceolaria
-toadflax
-zinnia
-centaury
-Easter daisy
-African violet
-brompton stock
-verbena
-blue daisy
-pink calla
-Mexican sunflower
-bloomer
-achimenes
-lychnis
-painted daisy
-treasure flower
-globe amaranth
-common valerian
-rose moss
-tidytips
-common daisy
-composite
-ice plant
-gentian
-soapwort
-anemone
-veronica
-larkspur
-spring beauty
-gazania
-damask violet
-Barberton daisy
-bush violet
-baby's breath
-corydalis
-calendula
-sunflower
-scabious
-valerian
-rue anemone
-sandwort
-candytuft
-horn poppy
-sandwort
-poppy
-stokes' aster
-dahlia
-Virginia spring beauty
-petunia
-orchid
-hybrid petunia
-African daisy
-pink
-African daisy
-African daisy
-daisy
-common ageratum
-oxeye daisy
-columbine
-calla lily
-sweet alyssum
-spathiphyllum
-four o'clock
-common marigold
-cornflower
-strawflower
-silene
-tuberose
-common four-o'clock
-rocket larkspur
-bellwort
-begonia
-streptocarpus
-Swan River daisy
-wallflower
-peony
-love-in-a-mist
-wallflower
-cineraria
-chrysanthemum
-stock
-sandwort
-Malcolm stock
-mountain sandwort
-coneflower
-ageratum
-coneflower
-sowbread
-scorpionweed
-cyclamen
-delphinium
-marigold
-aster
-cosmos
-Mediterranean snapdragon
-mullein pink
-ragged robin
-mayweed
-tansy
-dusty miller
-corn chamomile
-shasta daisy
-everlasting
-wingstem
-rosinweed
-oxeye daisy
-strawflower
-strawflower
-cudweed
-pearly everlasting
-gentianella
-agueweed
-closed gentian
-closed gentian
-great yellow gentian
-fringed gentian
-marsh gentian
-snowdrop anemone
-wood anemone
-wood anemone
-germander speedwell
-common speedwell
-common sunflower
-prairie sunflower
-giant sunflower
-Jerusalem artichoke
-sweet scabious
-field scabious
-Iceland poppy
-wind poppy
-Iceland poppy
-celandine
-oriental poppy
-opium poppy
-celandine poppy
-prickly poppy
-California poppy
-corn poppy
-blue poppy
-aerides
-coelogyne
-lady's slipper
-Venus' slipper
-cymbid
-sobralia
-spider orchid
-spider orchid
-Psychopsis papilio
-liparis
-butterfly orchid
-butterfly orchid
-butterfly orchid
-oncidium
-twayblade
-twayblade
-grass pink
-brassavola
-fragrant orchid
-fly orchid
-frog orchid
-coral root
-cattleya
-lesser butterfly orchid
-vanilla
-short-spurred fragrant orchid
-common spotted orchid
-bog rose
-ladies' tresses
-odontoglossum
-orchis
-vanda
-pansy orchid
-Bletilla striata
-rattlesnake plantain
-marsh orchid
-stanhopea
-laelia
-phaius
-lizard orchid
-caladenia
-calypso
-moth orchid
-blue orchid
-bee orchid
-early spider orchid
-masdevallia
-bog rein orchid
-European ladies' tresses
-fen orchid
-pogonia
-fringed orchis
-dendrobium
-fly orchid
-helleborine
-helleborine
-stelis
-greater butterfly orchid
-yellow lady's slipper
-large yellow lady's slipper
-common lady's-slipper
-moccasin flower
-butterfly orchid
-male orchis
-ragged orchid
-purple-fringed orchid
-stream orchid
-Epipactis helleborine
-sweet William
-china pink
-Japanese pink
-carnation
-cottage pink
-maiden pink
-meeting house
-granny's bonnets
-blue columbine
-fire pink
-white campion
-bladder campion
-red campion
-wild pink
-moss campion
-wax begonia
-hybrid tuberous begonia
-rex begonia
-crown daisy
-corn marigold
-florist's chrysanthemum
-African marigold
-French marigold
-New England aster
-bushy aster
-Michaelmas daisy
-Indian paintbrush
-goldenrod
-sand verbena
-bitterroot
-Indian pipe
-heliopsis
-meadow goldenrod
-pasqueflower
-fleabane
-blazing star
-edelweiss
-coneflower
-balloon flower
-wild carrot
-prairie gentian
-desert sunflower
-Arnica montana
-butterweed
-gaillardia
-brittlebush
-orange daisy
-daisy fleabane
-Mexican hat
-long-head coneflower
-cycad
-welwitschia
-encephalartos
-dioon
-macrozamia
-false sago
-water shamrock
-water hyacinth
-pistia
-water lily
-marsh plant
-water nymph
-European white lily
-bog star
-marsh marigold
-wild calla
-sedge
-parnassia
-skunk cabbage
-skunk cabbage
-cotton grass
-nutgrass
-common cotton grass
-winter aconite
-buttercup
-phlox
-willowherb
-stapelia
-skullcap
-bedstraw
-gumweed
-kangaroo paw
-common chickweed
-hyssop
-arum
-common comfrey
-borage
-nasturtium
-canna
-loosestrife
-toad lily
-globe thistle
-wild thyme
-common fennel
-bear's breech
-ironweed
-feverfew
-monarda
-physostegia
-creeping bugle
-vegetable
-hedge nettle
-plum tomato
-ground cherry
-flax
-primrose
-oxalis
-kniphofia
-boneset
-chickweed
-periwinkle
-garden angelica
-bugloss
-Dutchman's breeches
-pie plant
-cow parsnip
-butterbur
-milk thistle
-mouse-ear chickweed
-yellow bells
-lobelia
-anise hyssop
-banana
-Joe-Pye weed
-Joe-Pye weed
-garden forget-me-not
-evening primrose
-spiderflower
-sweet false chamomile
-agrimonia
-hepatica
-medic
-peperomia
-geranium
-viola
-okra
-bergenia
-astrantia
-aspidistra
-thyme
-common teasel
-carnivorous plant
-harvest-lice
-nemophila
-hawkweed
-hawkweed
-fleabane
-plumbago
-spiderwort
-prickly poppy
-common foxglove
-stonecrop
-garden lettuce
-teasel
-herb Paris
-coltsfoot
-basil
-sainfoin
-sneezeweed
-cockscomb
-baby blue-eyes
-coleus
-spurge nettle
-arnica
-sour dock
-clover
-mint
-coreopsis
-pimpernel
-kidney vetch
-foxglove
-legume
-reseda
-forget-me-not
-Virginia bluebell
-pineapple
-blueweed
-anchusa
-moss pink
-common dandelion
-false lupine
-sage
-chamomile
-crucifer
-chicory
-broad-leaved plantain
-bugle
-milkweed
-fireweed
-spirea
-inula
-hemp nettle
-garden nasturtium
-pokeweed
-moneywort
-asparagus
-Italian parsley
-rhubarb
-jewelweed
-asparagus fern
-sedum
-yarrow
-bird's foot trefoil
-scarlet pimpernel
-campanula
-mayapple
-painted nettle
-pigweed
-bleeding heart
-achillea
-snow-in-summer
-gramineous plant
-balsamroot
-Abyssinian banana
-herbage
-astilbe
-ginger
-saxifrage
-cow parsley
-dill
-common mullein
-dead nettle
-creeping buttercup
-meadow buttercup
-yellow bedstraw
-sweet woodruff
-caladium
-cuckoopint
-jack-in-the-pulpit
-alocasia
-taro
-amorphophallus
-bee balm
-bee balm
-artichoke
-cardoon
-tomatillo
-tomatillo
-English primrose
-oxlip
-cowslip
-polyanthus
-creeping oxalis
-common wood sorrel
-Bermuda buttercup
-red-hot poker
-poker plant
-dwarf banana
-Japanese banana
-plantain
-sundrops
-common evening primrose
-ivy geranium
-cranesbill
-fish geranium
-rose geranium
-meadow cranesbill
-wild geranium
-dove's foot geranium
-herb robert
-horned violet
-field pansy
-violet
-dog violet
-pale violet
-bird's-foot violet
-hedge violet
-Venus's flytrap
-pitcher plant
-tropical pitcher plant
-sundew
-white clover
-red clover
-crimson clover
-pennyroyal
-water-mint
-beach pea
-chickpea
-vetch
-tufted vetch
-bean
-wild pea
-scarlet runner
-sieva bean
-clary
-common sage
-clary sage
-wild sage
-purple sage
-meadow clary
-bok choy
-mustard
-cabbage
-cauliflower
-collard
-broccoli
-brussels sprout
-garlic mustard
-head cabbage
-radish plant
-bittercress
-alyssum
-field mustard
-rape
-radish
-radish
-lady's smock
-crinkleroot
-butterfly weed
-swamp milkweed
-tussock bellflower
-Canterbury bell
-clustered bellflower
-peach bells
-giant bamboo
-grass
-fescue
-cordgrass
-feather reed grass
-reed grass
-orchard grass
-cereal
-broom beard grass
-tall oat grass
-tallgrass
-St. Augustine grass
-pampas grass
-grama
-dallisgrass
-zoysia
-rye grass
-brome
-fountain grass
-rye
-popcorn
-wheat
-millet
-sorghum
-panic grass
-goose grass
-switch grass
-common ginger
-shellflower
-meadow saxifrage
-purple saxifrage
-white dead nettle
-henbit
-ground ivy
-blue pea
-purple clematis
-black-eyed Susan
-bougainvillea
-butterfly pea
-butterfly pea
-bindweed
-kudzu
-Boston ivy
-squash
-yellow jasmine
-wax plant
-morning glory
-liana
-Japanese wistaria
-allamanda
-field bindweed
-common allamanda
-passionflower
-convolvulus
-gourd
-grape
-Chinese gooseberry
-summer squash
-winter squash
-pumpkin
-spaghetti squash
-yellow squash
-acorn squash
-winter crookneck
-cypress vine
-Japanese morning glory
-moonflower
-golden pothos
-ceriman
-jade vine
-pothos
-love-in-a-mist
-maypop
-granadilla
-sweet melon
-bottle gourd
-net melon
-winter melon
-cantaloupe
-Sauvignon grape
-fox grape
-wild indigo
-shrub
-tree
-raspberry
-lupine
-abelia
-banksia
-bird pepper
-sea holly
-guelder rose
-crape myrtle
-castor-oil plant
-spirea
-hydrangea
-fuchsia
-redberry
-saltbush
-false indigo
-bridal wreath
-protea
-Oregon grape
-grevillea
-gorse
-rockrose
-cowberry
-subshrub
-honeypot
-California fuchsia
-sumac
-jasmine
-impala lily
-currant
-axseed
-mimosa
-southern buckthorn
-flowering quince
-yucca
-purple heather
-waratah
-mallow
-strawberry tree
-mock orange
-honeysuckle
-spurge
-kalmia
-bush hibiscus
-weigela
-Christmasberry
-angel's trumpet
-angel's trumpet
-gooseberry
-dusty miller
-croton
-Pyracantha
-forsythia
-artemisia
-silversword
-waratah
-philadelphus
-common lilac
-saltwort
-calliandra
-wahoo
-bird of paradise
-cape jasmine
-camellia
-night jasmine
-rose
-mountain laurel
-cotoneaster
-rhododendron
-frangipani
-broom
-desert pea
-lavender
-butterfly bush
-deutzia
-hortensia
-burdock
-prairie smoke
-centaury
-sea lavender
-common mugwort
-bird's foot trefoil
-large periwinkle
-great burdock
-St John's wort
-eriogonum
-purple loosestrife
-loosestrife
-mountain avens
-matilija poppy
-bur marigold
-wild lupine
-marguerite
-dusty miller
-great knapweed
-knapweed
-creeping St John's wort
-klammath weed
-common St John's wort
-common jasmine
-winter jasmine
-Adam's needle
-bear grass
-Joshua tree
-Spanish dagger
-hollyhock
-rose mallow
-common mallow
-marsh mallow
-musk mallow
-hibiscus
-althea
-rose mallow
-cotton rose
-woodbine
-trumpet honeysuckle
-Japanese honeysuckle
-poinsettia
-crown of thorns
-damask rose
-musk rose
-azalea
-rosebay
-swamp azalea
-common broom
-woodwaxen
-English lavender
-spike lavender
-French lavender
-locust tree
-kowhai
-bottle-tree
-timber tree
-linden
-bonsai
-snag
-hackberry
-pepper tree
-Japanese oak
-European hackberry
-cork tree
-birch
-star anise
-red silk-cotton tree
-roble
-common alder
-fig tree
-Japanese pagoda tree
-albizzia
-European hornbeam
-cassia
-coral tree
-neem
-white mangrove
-Chinese parasol tree
-bayberry
-yellowwood
-elm
-alder
-prickly ash
-angiospermous tree
-chestnut
-cabbage bark
-ash
-beech
-fringe tree
-golden shower tree
-lead tree
-palm
-balata
-sapling
-black beech
-acacia
-coffee
-gymnospermous tree
-ceibo
-incense tree
-lacebark
-shade tree
-pollard
-gum tree
-wild medlar
-hornbeam
-willow
-textile screw pine
-mescal bean
-Brazilian rosewood
-pandanus
-white mangrove
-oak
-bean tree
-plane tree
-blackwood
-coralwood
-Kentucky coffee tree
-black locust
-honey locust
-flame tree
-flame tree
-kurrajong
-American basswood
-silver lime
-black birch
-silver birch
-swamp birch
-downy birch
-grey birch
-golden fig
-India-rubber tree
-fig
-banyan
-pipal
-rain tree
-silk tree
-smooth-leaved elm
-American elm
-English elm
-cedar elm
-myrtle
-mangrove
-magnolia
-Queen's crape myrtle
-looking-glass plant
-tulip tree
-maple
-nut tree
-redbud
-baobab
-poplar
-tree of heaven
-ailanthus
-dogwood
-holly
-cacao
-laurel
-mountain ebony
-kapok
-sorrel tree
-cacao bean
-Spanish elm
-rowan
-mountain ash
-royal poinciana
-iron tree
-fruit tree
-sweet bay
-southern magnolia
-star magnolia
-umbrella tree
-box elder
-red maple
-hedge maple
-Norway maple
-Japanese maple
-sycamore
-California box elder
-silver maple
-sugar maple
-Oregon maple
-cashew
-walnut
-hazelnut
-black walnut
-English walnut
-black poplar
-aspen
-cottonwood
-white poplar
-quaking aspen
-Eastern cottonwood
-black cottonwood
-cornelian cherry
-bunchberry
-common European dogwood
-common white dogwood
-bearberry
-inkberry
-true laurel
-cassia
-citrus
-mulberry
-jackfruit
-pomegranate
-pawpaw
-persimmon
-carambola
-plum
-almond tree
-durian
-papaya
-olive tree
-longan
-pear
-loquat
-medlar
-peach
-white mulberry
-olive
-litchi
-Japanese apricot
-rambutan
-apple tree
-Japanese persimmon
-mango
-breadfruit
-guava
-guava
-jaboticaba
-cherry
-Surinam cherry
-lime
-mandarin
-orange
-kumquat
-lemon
-pomelo
-grapefruit
-clementine
-tangerine
-sweet orange
-sour orange
-bergamot
-cherry plum
-Allegheny plum
-flowering almond
-almond
-flowering almond
-crab apple
-apple
-wild apple
-Southern crab apple
-Bechtel crab
-Iowa crab
-sour cherry
-flowering cherry
-wild cherry
-sweet cherry
-chokecherry
-Japanese flowering cherry
-oriental cherry
-fuji
-hagberry tree
-black cherry
-Ozark chinkapin
-American chestnut
-pumpkin ash
-mountain ash
-manna ash
-European ash
-red ash
-weeping beech
-American beech
-copper beech
-bamboo palm
-wine palm
-fan palm
-royal palm
-cabbage palm
-cabbage palm
-sago palm
-miniature fan palm
-lady palm
-feather palm
-coconut
-cabbage palm
-carnauba
-caranday
-palmyra
-cabbage palmetto
-key palm
-saw palmetto
-palmetto
-date palm
-oil palm
-silver wattle
-wattle
-huisache
-ginkgo
-conifer
-kauri
-green douglas fir
-miro
-cedar
-cedar
-douglas fir
-matai
-arborvitae
-spruce
-yew
-araucaria
-cypress
-metasequoia
-pine
-fir
-larch
-hemlock
-cedar of Lebanon
-Atlas cedar
-deodar
-southern white cedar
-incense cedar
-Oregon cedar
-Japanese cedar
-Oriental arborvitae
-American arborvitae
-western red cedar
-Colorado spruce
-white spruce
-Norway spruce
-red spruce
-black spruce
-Sitka spruce
-oriental spruce
-bunya bunya
-monkey puzzle
-Monterey cypress
-Arizona cypress
-Italian cypress
-Scotch pine
-pond pine
-pitch pine
-table-mountain pine
-ancient pine
-stone pine
-Jeffrey pine
-loblolly pine
-Swiss pine
-spruce pine
-white pine
-red pine
-Japanese black pine
-Swiss mountain pine
-black pine
-Monterey pine
-yellow pine
-Torrey pine
-shore pine
-bristlecone pine
-whitebark pine
-western white pine
-longleaf pine
-ponderosa
-silver fir
-Fraser fir
-Alpine fir
-amabilis fir
-lowland fir
-balsam fir
-European silver fir
-white fir
-western larch
-American larch
-eastern hemlock
-western hemlock
-mountain hemlock
-gumbo-limbo
-elephant tree
-sweet gum
-eucalyptus
-sour gum
-liquidambar
-snow gum
-mountain ash
-black mallee
-alpine ash
-red gum
-red gum
-blue gum
-osier
-pussy willow
-bay willow
-weeping willow
-swamp willow
-purple willow
-common osier
-European turkey oak
-red oak
-cork oak
-black oak
-live oak
-chestnut oak
-bluejack oak
-pin oak
-post oak
-shingle oak
-white oak
-laurel oak
-northern red oak
-southern red oak
-southern live oak
-canyon oak
-coast live oak
-chinquapin oak
-basket oak
-swamp chestnut oak
-bur oak
-Oregon white oak
-common oak
-tamarind
-catalpa
-carob
-California sycamore
-American sycamore
-London plane
-lightwood
-black mangrove
-wild raspberry
-black raspberry
-bluebonnet
-Texas bluebonnet
-thistle
-cat's-ear
-corn cockle
-yellow rocket
-fireweed
-stinging nettle
-horseweed
-stemless carline thistle
-musk thistle
-cotton thistle
-plume thistle
-field thistle
-bull thistle
-Canada thistle
-hippeastrum
-narcissus
-iridaceous plant
-fritillary
-liliaceous plant
-star-of-Bethlehem
-daffodil
-jonquil
-jonquil
-iris
-blue-eyed grass
-blackberry-lily
-dwarf iris
-dwarf iris
-beardless iris
-bearded iris
-Japanese iris
-German iris
-snake's head fritillary
-crown imperial
-dogtooth violet
-lily
-African lily
-grape hyacinth
-common camas
-false lily of the valley
-common hyacinth
-camas
-clintonia
-lemon lily
-squaw grass
-scilla
-tulip
-alliaceous plant
-fawn lily
-glacier lily
-yellow adder's tongue
-Turk's-cap
-Turk's-cap
-tiger lily
-tiger lily
-mountain lily
-Easter lily
-tassel hyacinth
-common grape hyacinth
-Tulipa gesneriana
-Darwin tulip
-garlic chive
-wild garlic
-Hottentot fig
-livingstone daisy
-cactus
-nopal
-nopal
-barrel cactus
-night-blooming cereus
-night-blooming cereus
-night-blooming cereus
-cholla
-echinocactus
-mammillaria
-feather ball
-prickly pear
-crab cactus
-saguaro
-Christmas cactus
-hedgehog cactus
-golden barrel cactus
-flamingo flower
-anthurium
-gloxinia
-baneberry
-red baneberry
-poison ivy
-gloriosa
-monkshood
-American holly
-oleander
-poison ash
-herbivore
-big game
-thoroughbred
-creepy-crawly
-young
-domestic animal
-pet
-critter
-larva
-feeder
-male
-pest
-omnivore
-predator
-chordate
-work animal
-invertebrate
-female
-marine animal
-scavenger
-hexapod
-mate
-prey
-carnivore
-young mammal
-orphan
-spat
-young bird
-hatchling
-foal
-kitten
-calf
-pup
-calf
-lamb
-baby
-puppy
-suckling
-cub
-piglet
-nestling
-fledgling
-head
-stray
-feeder
-tadpole
-caterpillar
-nymph
-doodlebug
-tobacco hornworm
-cabbageworm
-tomato hornworm
-silkworm
-cutworm
-woolly bear
-measuring worm
-armyworm
-silkworm
-tussock caterpillar
-tent caterpillar
-colt
-ridgeling
-sire
-sea squirt
-ascidian
-vertebrate
-aquatic vertebrate
-amphibian
-mammal
-baby
-fetus
-quadruped
-reptile
-bird
-fish
-lamprey
-teleost fish
-food fish
-elasmobranch
-ganoid
-trumpetfish
-pipefish
-seahorse
-spiny-finned fish
-soft-finned fish
-needlefish
-beluga
-gar
-bowfin
-paddlefish
-sturgeon
-percoid fish
-dragonet
-frogfish
-barracuda
-soldierfish
-goosefish
-scorpaenoid
-flatfish
-great barracuda
-plectognath
-snook
-perch
-perch
-dolphinfish
-freshwater bass
-scombroid
-bass
-parrotfish
-sea bream
-grunt
-flathead
-bluefish
-carangid fish
-damselfish
-butterfly fish
-mudskipper
-pike
-goby
-tautog
-sunfish
-snapper
-snapper
-sciaenid fish
-wolffish
-cichlid
-wrasse
-yellow perch
-European perch
-walleye
-mackerel
-skipjack
-black marlin
-sailfish
-marlin
-bonito
-tuna
-wahoo
-Spanish mackerel
-Spanish mackerel
-cero
-king mackerel
-bluefin
-yellowfin
-jack
-permit
-scad
-crevalle jack
-kingfish
-amberjack
-yellowtail
-horse mackerel
-horse mackerel
-clown anemone fish
-sergeant major
-anemone fish
-chaetodon
-rock beauty
-angelfish
-northern pike
-pickerel
-muskellunge
-black bass
-pumpkinseed
-freshwater bream
-bluegill
-crappie
-smallmouth
-largemouth
-sea trout
-croaker
-kingfish
-mulloway
-red drum
-white croaker
-white croaker
-scorpaenid
-flathead
-scorpionfish
-lionfish
-stonefish
-rockfish
-plaice
-flounder
-halibut
-cowfish
-boxfish
-ocean sunfish
-puffer
-spiny puffer
-triggerfish
-balloonfish
-porcupinefish
-tarpon
-bonefish
-pollack
-anchovy
-lizardfish
-catfish
-cypriniform fish
-eel
-clupeid fish
-European catfish
-flathead catfish
-channel catfish
-blue catfish
-characin
-electric eel
-cyprinodont
-loach
-cyprinid
-topminnow
-piranha
-cardinal tetra
-tetra
-killifish
-striped killifish
-guppy
-swordtail
-carp
-minnow
-tench
-crucian carp
-goldfish
-gudgeon
-platy
-mosquitofish
-conger
-tuna
-moray
-sardine
-pilchard
-sea bass
-trout
-salmon
-barracouta
-grouper
-striped bass
-jewfish
-hind
-sea trout
-brook trout
-rainbow trout
-brown trout
-lake trout
-chinook
-Atlantic salmon
-redfish
-coho
-landlocked salmon
-shark
-ray
-sand tiger
-angel shark
-nurse shark
-requiem shark
-smooth dogfish
-hammerhead
-mackerel shark
-whale shark
-bull shark
-blue shark
-sandbar shark
-blacktip shark
-whitetip shark
-tiger shark
-lemon shark
-whitetip shark
-smoothhound
-great white shark
-mako
-porbeagle
-stingray
-electric ray
-spotted eagle ray
-Atlantic manta
-manta
-skate
-eagle ray
-salamander
-frog
-spotted salamander
-newt
-European fire salamander
-slender salamander
-ambystomid
-eft
-common newt
-red eft
-spotted salamander
-axolotl
-tiger salamander
-true toad
-true frog
-tailed frog
-crapaud
-tree toad
-tree frog
-natterjack
-Eurasian green toad
-bufo
-American toad
-agua
-western toad
-European toad
-grass frog
-wood-frog
-bullfrog
-leopard frog
-pickerel frog
-green frog
-spring peeper
-chorus frog
-placental
-tusker
-monotreme
-marsupial
-female mammal
-aardvark
-livestock
-insectivore
-hyrax
-doe
-edentate
-stag
-bull
-primate
-carnivore
-bat
-aquatic mammal
-lagomorph
-rock hyrax
-yearling
-rodent
-cow
-pachyderm
-buck
-pangolin
-ungulate
-shrew
-hedgehog
-peba
-sloth
-armadillo
-anteater
-two-toed sloth
-two-toed sloth
-three-toed sloth
-ant bear
-tamandua
-simian
-tarsier
-homo
-ape
-lemur
-monkey
-Homo sapiens sapiens
-Homo sapiens
-Neandertal man
-anthropoid ape
-lesser ape
-great ape
-siamang
-gibbon
-chimpanzee
-orangutan
-gorilla
-pygmy chimpanzee
-central chimpanzee
-western lowland gorilla
-mountain gorilla
-silverback
-indri
-Madagascar cat
-potto
-galago
-slow loris
-Old World monkey
-New World monkey
-baboon
-vervet
-proboscis monkey
-colobus
-patas
-macaque
-guenon
-langur
-chacma
-mandrill
-Barbary ape
-rhesus
-spider monkey
-marmoset
-squirrel monkey
-titi
-capuchin
-howler monkey
-tamarin
-pygmy marmoset
-procyonid
-feline
-viverrine
-canine
-musteline mammal
-bear
-coati
-common raccoon
-lesser panda
-raccoon
-kinkajou
-giant panda
-big cat
-cat
-jaguar
-tiger
-leopard
-cheetah
-lion
-snow leopard
-tigress
-Bengal tiger
-tiger cub
-lioness
-lion cub
-domestic cat
-wildcat
-tabby
-tiger cat
-tabby
-tortoiseshell
-Manx
-Egyptian cat
-Abyssinian
-kitty
-Angora
-Persian cat
-Burmese cat
-Siamese cat
-alley cat
-tom
-mouser
-margay
-ocelot
-lynx
-cougar
-European wildcat
-serval
-manul
-sand cat
-common lynx
-bobcat
-caracal
-Canada lynx
-meerkat
-genet
-mongoose
-slender-tailed meerkat
-suricate
-dog
-wild dog
-wolf
-bitch
-jackal
-fox
-hyena
-pug
-corgi
-Great Pyrenees
-Brabancon griffon
-poodle
-cur
-Leonberg
-griffon
-dalmatian
-pooch
-spitz
-toy dog
-hunting dog
-working dog
-basenji
-Mexican hairless
-Newfoundland
-lapdog
-Cardigan
-Pembroke
-standard poodle
-toy poodle
-miniature poodle
-Pomeranian
-keeshond
-chow
-Samoyed
-toy spaniel
-Shih-Tzu
-toy terrier
-Maltese dog
-Japanese spaniel
-Chihuahua
-Pekinese
-King Charles spaniel
-Blenheim spaniel
-papillon
-terrier
-Rhodesian ridgeback
-sausage dog
-sporting dog
-hound
-dachshund
-Dandie Dinmont
-schnauzer
-wirehair
-Airedale
-West Highland white terrier
-Kerry blue terrier
-Norfolk terrier
-Border terrier
-Yorkshire terrier
-wire-haired fox terrier
-Bedlington terrier
-Tibetan terrier
-silky terrier
-Lhasa
-Scotch terrier
-cairn
-Boston bull
-fox terrier
-Australian terrier
-bullterrier
-Norwich terrier
-Irish terrier
-rat terrier
-soft-coated wheaten terrier
-standard schnauzer
-giant schnauzer
-miniature schnauzer
-Lakeland terrier
-Welsh terrier
-Sealyham terrier
-Staffordshire bullterrier
-American Staffordshire terrier
-Manchester terrier
-toy Manchester
-water dog
-pointer
-bird dog
-setter
-spaniel
-retriever
-vizsla
-German short-haired pointer
-Gordon setter
-English setter
-Irish setter
-cocker spaniel
-water spaniel
-springer spaniel
-Brittany spaniel
-clumber
-Sussex spaniel
-Irish water spaniel
-English springer
-Welsh springer spaniel
-flat-coated retriever
-golden retriever
-curly-coated retriever
-Chesapeake Bay retriever
-Labrador retriever
-otterhound
-bloodhound
-wolfhound
-basset
-Ibizan hound
-Norwegian elkhound
-coonhound
-Saluki
-Afghan hound
-black-and-tan coonhound
-bluetick
-Scottish deerhound
-redbone
-foxhound
-beagle
-Weimaraner
-greyhound
-borzoi
-Irish wolfhound
-English foxhound
-Walker hound
-whippet
-Italian greyhound
-Great Dane
-watchdog
-Eskimo dog
-Tibetan mastiff
-sled dog
-Saint Bernard
-French bulldog
-police dog
-bulldog
-Sennenhunde
-bull mastiff
-shepherd dog
-boxer
-mastiff
-kuvasz
-housedog
-pinscher
-schipperke
-Doberman
-miniature pinscher
-affenpinscher
-Siberian husky
-malamute
-Greater Swiss Mountain dog
-EntleBucher
-Bernese mountain dog
-Appenzeller
-Belgian sheepdog
-kelpie
-Shetland sheepdog
-komondor
-Border collie
-collie
-Rottweiler
-Old English sheepdog
-German shepherd
-briard
-Bouvier des Flandres
-groenendael
-malinois
-African hunting dog
-dingo
-dhole
-coyote
-wolf pup
-red wolf
-white wolf
-timber wolf
-red fox
-red fox
-kit fox
-Arctic fox
-grey fox
-kit fox
-spotted hyena
-striped hyena
-mink
-black-footed ferret
-striped skunk
-pine marten
-sea otter
-otter
-weasel
-polecat
-glutton
-skunk
-badger
-ferret
-river otter
-Eurasian otter
-ice bear
-American black bear
-bear cub
-Asiatic black bear
-brown bear
-sloth bear
-grizzly
-Alaskan brown bear
-carnivorous bat
-flying fox
-fruit bat
-brown bat
-vespertilian bat
-pallid bat
-pipistrelle
-cetacean
-sea cow
-pinniped mammal
-whale
-toothed whale
-baleen whale
-dolphin
-bottle-nosed whale
-porpoise
-common dolphin
-bottlenose dolphin
-pilot whale
-killer whale
-white whale
-Pacific bottlenose dolphin
-Atlantic bottlenose dolphin
-grey whale
-rorqual
-blue whale
-lesser rorqual
-finback
-manatee
-dugong
-walrus
-seal
-earless seal
-eared seal
-elephant seal
-harbor seal
-harp seal
-fur seal
-fur seal
-sea lion
-California sea lion
-Australian sea lion
-Steller sea lion
-pika
-leporid
-rabbit
-hare
-eastern cottontail
-wood rabbit
-bunny
-European rabbit
-lapin
-Angora
-rabbit ears
-snowshoe hare
-European hare
-jackrabbit
-chinchilla
-rat
-capybara
-golden hamster
-water vole
-porcupine
-coypu
-vole
-beaver
-hamster
-prairie dog
-squirrel
-marmot
-blacktail prairie dog
-cavy
-gerbil
-mouse
-muskrat
-gopher
-brown rat
-black rat
-chipmunk
-ground squirrel
-eastern chipmunk
-tree squirrel
-rock squirrel
-mantled ground squirrel
-eastern grey squirrel
-red squirrel
-black squirrel
-American red squirrel
-fox squirrel
-hoary marmot
-groundhog
-aperea
-guinea pig
-field mouse
-house mouse
-elephant
-African elephant
-Indian elephant
-even-toed ungulate
-odd-toed ungulate
-ruminant
-camel
-swine
-llama
-vicuna
-collared peccary
-hippopotamus
-peccary
-pronghorn
-deer
-bovid
-giraffe
-okapi
-woodland caribou
-caribou
-fallow deer
-elk
-hart
-mule deer
-fawn
-red deer
-muntjac
-Virginia deer
-wapiti
-Japanese deer
-roe deer
-black-tailed deer
-wild sheep
-bison
-musk ox
-Old World buffalo
-bovine
-antelope
-sheep
-goat antelope
-goat
-aoudad
-mountain sheep
-Dall sheep
-bighorn
-mouflon
-American bison
-wisent
-carabao
-water buffalo
-Cape buffalo
-Brahman
-ox
-zebu
-cattle
-yak
-gaur
-beef
-ox
-bull
-bullock
-heifer
-cow
-dairy cattle
-longhorn
-Charolais
-Hereford
-Durham
-Aberdeen Angus
-Galloway
-Friesian
-Brown Swiss
-kudu
-addax
-blackbuck
-waterbuck
-eland
-steenbok
-dik-dik
-gnu
-harnessed antelope
-gerenuk
-sassaby
-impala
-greater kudu
-sable antelope
-hartebeest
-bongo
-gemsbok
-oryx
-gazelle
-nyala
-bushbuck
-Thomson's gazelle
-springbok antelope
-domestic sheep
-black sheep
-ewe
-wether
-ram
-mountain goat
-chamois
-takin
-nanny
-kid
-ibex
-Angora
-domestic goat
-billy
-wild goat
-Bactrian camel
-Arabian camel
-wild boar
-warthog
-boar
-hog
-guanaco
-alpaca
-rhinoceros
-tapir
-equine
-Malayan tapir
-Indian rhinoceros
-black rhinoceros
-white rhinoceros
-horse
-zebra
-ass
-bay
-broodmare
-racehorse
-palomino
-wild horse
-pinto
-hack
-roan
-male horse
-post horse
-liver chestnut
-tarpan
-saddle horse
-chestnut
-harness horse
-polo pony
-workhorse
-mare
-pony
-pony
-sorrel
-yearling
-thoroughbred
-trotting horse
-stud
-stallion
-gelding
-Tennessee walker
-hack
-cavalry horse
-grey
-Morgan
-buckskin
-dun
-Arabian
-quarter horse
-cob
-hackney
-plow horse
-farm horse
-draft horse
-carthorse
-Percheron
-Clydesdale
-shire
-cayuse
-bronco
-mustang
-Welsh pony
-Shetland pony
-Exmoor
-common zebra
-mountain zebra
-grevy's zebra
-jennet
-burro
-domestic ass
-echidna
-platypus
-echidna
-kangaroo
-koala
-wombat
-common opossum
-opossum
-dasyurid marsupial
-phalanger
-giant kangaroo
-wallaby
-rock wallaby
-tree wallaby
-Tasmanian devil
-numbat
-chelonian
-diapsid
-turtle
-Western box turtle
-box turtle
-common snapping turtle
-terrapin
-soft-shelled turtle
-painted turtle
-sea turtle
-snapping turtle
-slider
-tortoise
-mud turtle
-cooter
-hawksbill turtle
-loggerhead
-green turtle
-leatherback turtle
-ridley
-Pacific ridley
-Atlantic ridley
-giant tortoise
-gopher tortoise
-European tortoise
-desert tortoise
-crocodilian reptile
-snake
-tuatara
-lizard
-dinosaur
-alligator
-crocodile
-American alligator
-caiman
-Asian crocodile
-African crocodile
-blind snake
-viper
-sea snake
-elapid
-constrictor
-colubrid snake
-horned viper
-asp
-adder
-puff adder
-pit viper
-water moccasin
-copperhead
-rattlesnake
-ground rattler
-massasauga
-diamondback
-Mojave rattlesnake
-timber rattlesnake
-prairie rattlesnake
-Western diamondback
-sidewinder
-rock rattlesnake
-speckled rattlesnake
-cobra
-green mamba
-taipan
-copperhead
-mamba
-coral snake
-coral snake
-Indian cobra
-hamadryad
-boa
-python
-rosy boa
-boa constrictor
-anaconda
-reticulated python
-carpet snake
-rock python
-blacksnake
-garter snake
-bull snake
-hognose snake
-rat snake
-whip-snake
-water snake
-green snake
-racer
-green snake
-thunder snake
-ringneck snake
-vine snake
-king snake
-night snake
-ribbon snake
-common garter snake
-pine snake
-gopher snake
-corn snake
-black rat snake
-grass snake
-common water snake
-water moccasin
-smooth green snake
-rough green snake
-milk snake
-common kingsnake
-banded gecko
-chameleon
-monitor
-skink
-Gila monster
-Komodo dragon
-whiptail
-iguanid
-agamid
-gecko
-African chameleon
-lacertid lizard
-anguid lizard
-horned lizard
-tree lizard
-chuckwalla
-American chameleon
-basilisk
-side-blotched lizard
-spiny lizard
-collared lizard
-common iguana
-marine iguana
-leopard lizard
-western fence lizard
-fence lizard
-agama
-mountain devil
-frilled lizard
-green lizard
-sand lizard
-blindworm
-alligator lizard
-ornithischian
-tyrannosaur
-stegosaur
-triceratops
-bird of passage
-aquatic bird
-passerine
-cock
-hummingbird
-piciform bird
-coraciiform bird
-quetzal
-bird of prey
-caprimulgiform bird
-cuculiform bird
-gamecock
-ratite
-gallinaceous bird
-trogon
-parrot
-carinate
-dickeybird
-hen
-wading bird
-swan
-gallinule
-seabird
-waterfowl
-heron
-crested cariama
-trumpeter
-bustard
-ibis
-stork
-whooping crane
-crane
-limpkin
-chunga
-flamingo
-rail
-spoonbill
-shoebill
-shorebird
-great blue heron
-night heron
-little blue heron
-boatbill
-great white heron
-egret
-bittern
-black-crowned night heron
-yellow-crowned night heron
-great white heron
-little egret
-snowy egret
-American egret
-cattle egret
-least bittern
-American bittern
-wood ibis
-sacred ibis
-marabou
-black stork
-white stork
-saddlebill
-jabiru
-policeman bird
-wood ibis
-notornis
-weka
-spotted crake
-crake
-coot
-Old World coot
-American coot
-common spoonbill
-roseate spoonbill
-plover
-godwit
-Hudsonian godwit
-stilt
-stone curlew
-oystercatcher
-stilt
-American woodcock
-snipe
-woodcock
-avocet
-sandpiper
-European curlew
-pratincole
-curlew
-phalarope
-golden plover
-ruddy turnstone
-killdeer
-lapwing
-turnstone
-piping plover
-black-necked stilt
-black-winged stilt
-whole snipe
-Wilson's snipe
-great snipe
-dowitcher
-tattler
-greenshank
-willet
-curlew sandpiper
-sanderling
-redshank
-spotted sandpiper
-knot
-red-backed sandpiper
-upland sandpiper
-least sandpiper
-pectoral sandpiper
-ruff
-European sandpiper
-yellowlegs
-greater yellowlegs
-lesser yellowlegs
-red phalarope
-Wilson's phalarope
-pen
-cygnet
-trumpeter
-coscoroba
-mute swan
-cob
-whooper
-black swan
-tundra swan
-whistling swan
-Bewick's swan
-purple gallinule
-European gallinule
-moorhen
-coastal diving bird
-pelagic bird
-grebe
-auk
-loon
-pelecaniform seabird
-sphenisciform seabird
-puffin
-larid
-jaeger
-skimmer
-sea swallow
-gull
-tern
-ivory gull
-mew
-laughing gull
-black-backed gull
-kittiwake
-herring gull
-skua
-parasitic jaeger
-petrel
-albatross
-wandering albatross
-shearwater
-storm petrel
-fulmar
-red-necked grebe
-great crested grebe
-pied-billed grebe
-black-necked grebe
-dabchick
-razorbill
-guillemot
-auklet
-murre
-black guillemot
-common murre
-pigeon guillemot
-frigate bird
-cormorant
-snakebird
-pelican
-gannet
-water turkey
-tropic bird
-white pelican
-Old world white pelican
-solan
-booby
-penguin
-emperor penguin
-jackass penguin
-king penguin
-rock hopper
-Adelie
-horned puffin
-tufted puffin
-Atlantic puffin
-anseriform bird
-goose
-duck
-blue goose
-barnacle goose
-snow goose
-Chinese goose
-common brant goose
-brant
-gosling
-greylag
-gander
-honker
-diving duck
-scaup
-shelduck
-wood drake
-bufflehead
-black duck
-mandarin duck
-American widgeon
-pintail
-mallard
-sheldrake
-teal
-Barrow's goldeneye
-quack-quack
-wild duck
-ruddy duck
-wood duck
-drake
-muscovy duck
-shoveler
-dabbling duck
-widgeon
-sea duck
-redhead
-pochard
-goldeneye
-canvasback
-duckling
-greater scaup
-lesser scaup
-garganey
-greenwing
-bluewing
-eider
-old squaw
-merganser
-scoter
-common scoter
-American merganser
-red-breasted merganser
-hooded merganser
-smew
-goosander
-wren
-broadbill
-tyrannid
-oscine
-scrubbird
-sparrow
-marsh wren
-rock wren
-winter wren
-cactus wren
-house wren
-Carolina wren
-ovenbird
-manakin
-pitta
-woodhewer
-New World flycatcher
-kingbird
-phoebe
-pewee
-vermillion flycatcher
-western wood pewee
-scissortail
-grey kingbird
-eastern kingbird
-Arkansas kingbird
-warbler
-brown creeper
-corvine bird
-starling
-pipit
-titmouse
-fairy bluebird
-thrush
-hedge sparrow
-wood swallow
-shrike
-lark
-golden oriole
-Old World flycatcher
-thrasher
-vireo
-tanager
-honeycreeper
-finch
-bowerbird
-water ouzel
-accentor
-mockingbird
-brown thrasher
-skylark
-catbird
-satin bowerbird
-waxwing
-red-eyed vireo
-New World oriole
-Old World oriole
-babbler
-swallow
-creeper
-songbird
-Australian magpie
-wagtail
-meadow pipit
-spotted flycatcher
-weaver
-nuthatch
-greater whitethroat
-New World warbler
-kinglet
-Old World warbler
-gnatcatcher
-lesser whitethroat
-yellowthroat
-common yellowthroat
-ovenbird
-parula warbler
-Blackburn
-yellow warbler
-American redstart
-yellow-breasted chat
-Audubon's warbler
-Wilson's warbler
-Cape May warbler
-myrtle warbler
-goldcrest
-ruby-crowned kinglet
-tailorbird
-sedge warbler
-wren warbler
-blackcap
-rook
-Clark's nutcracker
-jackdaw
-European magpie
-jay
-raven
-crow
-magpie
-American crow
-blue jay
-Canada jay
-common starling
-hill myna
-myna
-bushtit
-chickadee
-blue tit
-tufted titmouse
-Carolina chickadee
-black-capped chickadee
-robin
-robin
-hermit thrush
-redwing
-fieldfare
-song thrush
-nightingale
-blackbird
-missel thrush
-ring ouzel
-wheatear
-bluebird
-thrush nightingale
-bluethroat
-redstart
-bulbul
-Old World chat
-wood thrush
-stonechat
-whinchat
-butcherbird
-loggerhead shrike
-bush shrike
-northern shrike
-European shrike
-western tanager
-summer tanager
-scarlet tanager
-serin
-bullfinch
-grosbeak
-goldfinch
-New World sparrow
-crossbill
-bunting
-linnet
-cardinal
-siskin
-common canary
-towhee
-purple finch
-honeycreeper
-brambling
-New World goldfinch
-pine siskin
-redpoll
-dark-eyed junco
-house finch
-chaffinch
-canary
-redpoll
-junco
-pine grosbeak
-evening grosbeak
-hawfinch
-song sparrow
-white-throated sparrow
-tree sparrow
-field sparrow
-white-crowned sparrow
-swamp sparrow
-chipping sparrow
-indigo bunting
-reed bunting
-snow bunting
-ortolan
-yellowhammer
-cedar waxwing
-Bohemian waxwing
-bobolink
-meadowlark
-northern oriole
-orchard oriole
-New World blackbird
-eastern meadowlark
-western meadowlark
-Bullock's oriole
-Baltimore oriole
-purple grackle
-cowbird
-grackle
-red-winged blackbird
-white-bellied swallow
-tree swallow
-martin
-barn swallow
-cliff swallow
-house martin
-bank martin
-butcherbird
-currawong
-Java sparrow
-zebra finch
-red-breasted nuthatch
-European nuthatch
-white-breasted nuthatch
-English sparrow
-tree sparrow
-thornbill
-Archilochus colubris
-jacamar
-woodpecker
-barbet
-toucanet
-toucan
-flicker
-downy woodpecker
-green woodpecker
-sapsucker
-wryneck
-redheaded woodpecker
-yellow-shafted flicker
-red-breasted sapsucker
-yellow-bellied sapsucker
-kingfisher
-roller
-motmot
-Euopean hoopoe
-hornbill
-European roller
-hoopoe
-bee eater
-kookaburra
-Eurasian kingfisher
-belted kingfisher
-vulture
-hawk
-secretary bird
-eagle
-owl
-Old World vulture
-New World vulture
-Egyptian vulture
-bearded vulture
-black vulture
-griffon vulture
-black vulture
-buzzard
-king vulture
-condor
-Andean condor
-California condor
-harrier
-goshawk
-red-shouldered hawk
-honey buzzard
-falcon
-harrier eagle
-Cooper's hawk
-osprey
-kite
-rough-legged hawk
-buzzard
-sparrow hawk
-marsh harrier
-marsh hawk
-carancha
-gyrfalcon
-peregrine
-caracara
-hobby
-pigeon hawk
-kestrel
-sparrow hawk
-white-tailed kite
-swallow-tailed kite
-black kite
-eaglet
-golden eagle
-sea eagle
-bald eagle
-harpy
-tawny eagle
-fishing eagle
-ern
-tawny owl
-owlet
-spotted owl
-screech owl
-horned owl
-screech owl
-little owl
-barn owl
-scops owl
-Old World scops owl
-hawk owl
-great horned owl
-barred owl
-long-eared owl
-great grey owl
-frogmouth
-goatsucker
-touraco
-cuckoo
-coucal
-roadrunner
-rhea
-rhea
-ostrich
-emu
-cassowary
-domestic fowl
-columbiform bird
-brush turkey
-red jungle fowl
-jungle fowl
-game bird
-turkey cock
-bantam
-turkey
-guinea fowl
-chicken
-cockerel
-cock
-Rhode Island red
-chick
-Orpington
-hen
-pullet
-brood hen
-sandgrouse
-pigeon
-domestic pigeon
-dove
-wood pigeon
-rock dove
-homing pigeon
-roller
-Streptopelia turtur
-turtledove
-Australian turtledove
-mourning dove
-phasianid
-tinamou
-grouse
-pheasant
-quail
-partridge
-tragopan
-ring-necked pheasant
-golden pheasant
-peafowl
-peahen
-blue peafowl
-peacock
-green peafowl
-bobwhite
-California quail
-northern bobwhite
-red-legged partridge
-Hungarian partridge
-spruce grouse
-prairie chicken
-capercaillie
-ruffed grouse
-sage grouse
-moorhen
-black grouse
-ptarmigan
-cockateel
-parakeet
-cockatoo
-poll
-kea
-African grey
-macaw
-amazon
-lovebird
-lory
-popinjay
-budgerigar
-ring-necked parakeet
-sulphur-crested cockatoo
-pink cockatoo
-rainbow lorikeet
-lorikeet
-beast of burden
-draft animal
-ctenophore
-worm
-mollusk
-echinoderm
-coelenterate
-arthropod
-sponge
-nematode
-annelid
-flatworm
-medicinal leech
-earthworm
-chiton
-bivalve
-cephalopod
-gastropod
-oyster
-ark shell
-clam
-mussel
-cockle
-scallop
-pearl oyster
-soft-shell clam
-quahog
-giant clam
-freshwater mussel
-edible mussel
-zebra mussel
-octopod
-chambered nautilus
-cuttlefish
-octopus
-paper nautilus
-sea hare
-cowrie
-conch
-seasnail
-ormer
-tiger cowrie
-sea slug
-slug
-snail
-common limpet
-whelk
-nerita
-edible snail
-brown snail
-garden snail
-starfish
-feather star
-sand dollar
-sea urchin
-sea cucumber
-brittle star
-polyp
-anthozoan
-Portuguese man-of-war
-jellyfish
-sea pen
-sea anemone
-coral
-stony coral
-gorgonian
-sea fan
-mushroom coral
-brain coral
-centipede
-crustacean
-trilobite
-millipede
-arachnid
-horseshoe crab
-instar
-insect
-house centipede
-daphnia
-brachyuran
-mantis shrimp
-malacostracan crustacean
-decapod crustacean
-isopod
-amphipod
-pill bug
-woodlouse
-lobster
-shrimp
-hermit crab
-prawn
-crab
-crayfish
-Norway lobster
-spiny lobster
-American lobster
-king crab
-blue crab
-rock crab
-Dungeness crab
-fiddler crab
-European spider crab
-scorpion
-harvestman
-acarine
-spider
-tick
-mite
-wood tick
-orb-weaving spider
-European wolf spider
-tarantula
-wolf spider
-garden spider
-black widow
-black and gold garden spider
-barn spider
-orthopterous insect
-hemipterous insect
-neuropteron
-dictyopterous insect
-collembolan
-mayfly
-homopterous insect
-dipterous insect
-earwig
-common European earwig
-phasmid
-pollinator
-bug
-pupa
-walking stick
-scorpion fly
-beetle
-heteropterous insect
-stonefly
-hymenopterous insect
-lepidopterous insect
-chrysalis
-odonate
-silverfish
-worker bee
-grasshopper
-cricket
-katydid
-locust
-true bug
-bedbug
-dobson
-green lacewing
-lacewing
-mantis
-praying mantis
-cockroach
-American cockroach
-German cockroach
-oriental cockroach
-plant louse
-cicada
-meadow spittlebug
-seventeen-year locust
-mealybug
-leafhopper
-aphid
-mosquito
-crane fly
-midge
-fruit fly
-fly
-horse tick
-robber fly
-Asian tiger mosquito
-common mosquito
-bee fly
-horsefly
-flesh fly
-blowfly
-housefly
-greenbottle
-bluebottle
-Colorado potato beetle
-firefly
-ground beetle
-sawyer
-ladybug
-lamellicorn beetle
-rove beetle
-Asian longhorned beetle
-leaf beetle
-elaterid beetle
-click beetle
-tiger beetle
-weevil
-long-horned beetle
-Hippodamia convergens
-vedalia
-scarabaeid beetle
-stag beetle
-rose chafer
-June beetle
-Japanese beetle
-rhinoceros beetle
-dung beetle
-scarab
-cockchafer
-water strider
-wheel bug
-wasp
-ichneumon fly
-ant
-bee
-cicada killer
-digger wasp
-vespid
-hornet
-paper wasp
-common wasp
-giant hornet
-yellow jacket
-carpenter ant
-fire ant
-wood ant
-carpenter bee
-honeybee
-mason bee
-andrena
-leaf-cutting bee
-bumblebee
-Africanized bee
-black bee
-butterfly
-moth
-lycaenid
-nymphalid
-sulphur butterfly
-ringlet
-monarch
-cabbage butterfly
-blue
-hairstreak
-copper
-tortoiseshell
-fritillary
-admiral
-banded purple
-peacock
-red-spotted purple
-painted beauty
-mourning cloak
-viceroy
-red admiral
-white admiral
-comma
-small white
-large white
-cinnabar
-saturniid
-noctuid moth
-hawkmoth
-tea tortrix
-geometrid
-tineid
-atlas moth
-emperor
-polyphemus moth
-cecropia
-luna moth
-carpet moth
-clothes moth
-dragonfly
-damselfly
-hen
-filly
-dam
-herpes
-protoctist
-herpes simplex
-herpes zoster
-cytomegalovirus
-herpes varicella zoster
-alga
-protozoan
-seagrass
-pond scum
-green algae
-plasmodium
-ameba
-ciliate
-paramecium
-sphagnum
-hepatica
-liverwort
-peer
-birth
-adult
-juvenile
-countrywoman
-businessperson
-native
-celebrant
-native
-Filipino
-male
-Gemini
-onlooker
-queen
-referee
-commoner
-expert
-newcomer
-face
-demonstrator
-orphan
-Black woman
-contestant
-bullfighter
-lowerclassman
-candidate
-friend
-life
-anomaly
-actor
-thrower
-creature
-child
-sheep
-scuba diver
-dancer
-garbage man
-entertainer
-lover
-unfortunate
-anti
-defender
-sphinx
-Indian
-patient
-Slav
-White
-brick
-recipient
-religious person
-rescuer
-Latin
-money handler
-rich person
-domestic partner
-creator
-consumer
-worker
-groom
-boy scout
-inhabitant
-African
-fan
-eager beaver
-leader
-schoolmate
-man
-philatelist
-advocate
-eccentric
-bad person
-transvestite
-citizen
-communicator
-nonworker
-parrot
-intellectual
-nonsmoker
-student
-chameleon
-combatant
-platinum blond
-appointee
-unpleasant person
-politician
-ruler
-ancient
-spectator
-right-hander
-traveler
-scientist
-picker
-female
-acquaintance
-Black
-relative
-beard
-redhead
-sleeper
-computer user
-associate
-participant
-member
-raiser
-groom
-bride
-commissioner
-director
-tribesman
-board member
-important person
-professional
-oldster
-celebrity
-very important person
-serjeant-at-law
-educator
-health professional
-teacher
-reading teacher
-schoolmaster
-nurse
-medical practitioner
-pharmacist
-head nurse
-probationer
-doctor
-surgeon
-specialist
-house physician
-cardiologist
-radiologist
-schoolchild
-child
-bairn
-orphan
-entrepreneur
-baron
-agent
-merchant
-certified public accountant
-syndic
-insurance broker
-fishmonger
-vintner
-peddler
-seller
-male child
-mother's boy
-son
-man
-cub
-farm boy
-bat boy
-Herr
-hunk
-Peter Pan
-patriarch
-adonis
-young buck
-stud
-guy
-patriarch
-sleuth
-archer
-authority
-military attache
-therapist
-technician
-black belt
-high priest
-critic
-taster
-panelist
-physical therapist
-osteopath
-player
-athlete
-rival
-billiard player
-medalist
-seeded player
-chess master
-pool player
-football player
-tennis player
-ball hawk
-vaulter
-runner
-skater
-acrobat
-climber
-diver
-alpinist
-soccer player
-winger
-tennis pro
-forward
-sport
-basketball player
-miler
-ballplayer
-gymnast
-back
-lineman
-halfback
-quarterback
-tailback
-skateboarder
-speedskater
-circus acrobat
-aerialist
-fielder
-designated hitter
-base runner
-minor leaguer
-first baseman
-outfielder
-right fielder
-infielder
-semifinalist
-foe
-matador
-picador
-banderillero
-buddy
-mate
-flatmate
-pitcher
-closer
-right-handed pitcher
-folk dancer
-square dancer
-morris dancer
-compere
-master of ceremonies
-caricaturist
-performer
-fire-eater
-executant
-dancer
-juggler
-puppeteer
-actor
-clown
-musician
-dancing-master
-ballet dancer
-understudy
-starlet
-tenor saxophonist
-percussionist
-guitarist
-keyboardist
-trumpeter
-sitar player
-singer
-oboist
-cellist
-violist
-flutist
-organist
-rock star
-drummer
-songster
-bass
-fiance
-darling
-fancier
-soul mate
-sweetheart
-kisser
-amputee
-homeless
-casualty
-guard
-fireman
-zoo keeper
-lawman
-military policeman
-attorney general
-policeman
-bobby
-Mountie
-detective
-motorcycle cop
-trooper
-traffic cop
-Kiliwa
-Biloxi
-Chickasaw
-Kickapoo
-Arab
-white man
-Omani
-Bedouin
-Yemeni
-protegee
-heiress
-swami
-Buddhist
-Muslim
-novitiate
-religious
-Muslimah
-Sufi
-mother
-monk
-Sister
-treasurer
-ratepayer
-state treasurer
-bursar
-cobbler
-artist
-choreographer
-farmer
-musician
-stylist
-sculptor
-press photographer
-songwriter
-arranger
-beekeeper
-breeder
-agriculturist
-drinker
-policyholder
-drinker
-concert-goer
-drunkard
-beer drinker
-maid
-employee
-assistant
-gondolier
-skilled worker
-skidder
-boatman
-waiter
-bartender
-staff member
-salesperson
-workman
-settler
-breadwinner
-waitress
-salesman
-gardener
-laborer
-mill-hand
-hired hand
-coal miner
-horse wrangler
-goat herder
-farmhand
-attendant
-cog
-model
-escort
-caddie
-companion
-lifeguard
-steward
-color guard
-honor guard
-cover girl
-artist's model
-electrician
-official
-falconer
-balloonist
-craftsman
-pilot
-blacksmith
-trawler
-mender
-baker
-serviceman
-painter
-diplomat
-judge
-incumbent
-appointee
-presbyter
-ambassador
-high commissioner
-plenipotentiary
-glassblower
-carpenter
-coiffeur
-machinist
-wright
-hairdresser
-fighter pilot
-copilot
-artilleryman
-Navy SEAL
-military officer
-enlisted person
-noncommissioned officer
-commanding officer
-naval commander
-adjutant general
-commander in chief
-commissioned officer
-army officer
-adjutant
-inspector general
-sergeant
-first sergeant
-staff sergeant
-commissioned military officer
-commissioned naval officer
-line officer
-major
-lieutenant
-first lieutenant
-marshal
-captain
-general
-lieutenant colonel
-lieutenant commander
-rear admiral
-soldier
-enlisted man
-tanker
-reservist
-Unknown Soldier
-private
-recruit
-yard bird
-villager
-Tahitian
-American
-Asian
-American
-Polynesian
-European
-New Zealander
-North Carolinian
-Minnesotan
-Nebraskan
-Floridian
-Afghan
-Tibetan
-Mongol
-Papuan
-Indian
-Jordanian
-Japanese
-Malay
-Korean
-Timorese
-Bornean
-Lao
-Iraqi
-Gujarati
-Punjabi
-West Indian
-Latin American
-North American
-South American
-Bahamian
-Barbadian
-Haitian
-Central American
-Canadian
-Mexican
-Nicaraguan
-Mexican-American
-Bolivian
-Guyanese
-Albanian
-Byelorussian
-Monegasque
-Frank
-Scandinavian
-Laconian
-Netherlander
-Slovene
-Sabine
-Bulgarian
-Romanian
-Lithuanian
-Englishwoman
-Britisher
-Yugoslav
-Dubliner
-Parisian
-Eritrean
-Tanzanian
-Zulu
-Black African
-Cameroonian
-Sudanese
-Senegalese
-Kenyan
-Togolese
-Ugandan
-Liberian
-Herero
-Zimbabwean
-Nigerian
-Gambian
-Tuareg
-Guinean
-Ethiopian
-South African
-mayor
-politician
-trainer
-employer
-Speaker
-lawgiver
-cheerleader
-head
-aristocrat
-spiritual leader
-instigator
-mistress
-boss
-demagogue
-Labourite
-animal trainer
-pitching coach
-legislator
-deputy
-senator
-administrator
-department head
-secretary
-manageress
-executive
-hotelier
-chief executive officer
-Treasury
-minister
-Secretary of State
-Secretary of the Interior
-duchess
-viscount
-clergyman
-lama
-rabbi
-Dalai Lama
-officiant
-priest
-cleric
-vicar
-Father
-bishop
-diocesan
-cardinal
-metropolitan
-federalist
-supporter
-ambassador
-protectionist
-loyalist
-cheerleader
-adulteress
-wrongdoer
-hypocrite
-abettor
-skinhead
-biographer
-disk jockey
-speaker
-representative
-reporter
-orator
-interlocutor
-organ-grinder
-head of state
-alderman
-resident commissioner
-President of the United States
-president
-television reporter
-anchor
-retiree
-sunbather
-camper
-scholar
-exponent
-casuist
-futurist
-licentiate
-reader
-brawler
-boxer
-wrestler
-flyweight
-middleweight
-sparring partner
-prizefighter
-light heavyweight
-featherweight
-lightweight
-heavyweight
-flyweight
-sumo wrestler
-bantamweight
-egotist
-fire-eater
-upstart
-bragger
-exhibitionist
-sovereign
-Pharaoh
-Cheops
-sheik
-rider
-motorcyclist
-musher
-astronaut
-pedestrian
-mover
-commuter
-pilgrim
-skin-diver
-settler
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diff --git a/build/darknet/x64/data/9k.tree b/build/darknet/x64/data/9k.tree
deleted file mode 100644
index deb61e2a021..00000000000
--- a/build/darknet/x64/data/9k.tree
+++ /dev/null
@@ -1,9418 +0,0 @@
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diff --git a/build/darknet/x64/data/coco.data b/build/darknet/x64/data/coco.data
deleted file mode 100644
index 6da50e646d6..00000000000
--- a/build/darknet/x64/data/coco.data
+++ /dev/null
@@ -1,8 +0,0 @@
-classes= 80
-train  = data/coco/trainvalno5k.txt
-valid  = data/coco_testdev
-#valid = data/coco_val_5k.list
-names = data/coco.names
-backup = backup/
-eval=coco
-
diff --git a/build/darknet/x64/data/coco.names b/build/darknet/x64/data/coco.names
deleted file mode 100644
index ca76c80b5b2..00000000000
--- a/build/darknet/x64/data/coco.names
+++ /dev/null
@@ -1,80 +0,0 @@
-person
-bicycle
-car
-motorbike
-aeroplane
-bus
-train
-truck
-boat
-traffic light
-fire hydrant
-stop sign
-parking meter
-bench
-bird
-cat
-dog
-horse
-sheep
-cow
-elephant
-bear
-zebra
-giraffe
-backpack
-umbrella
-handbag
-tie
-suitcase
-frisbee
-skis
-snowboard
-sports ball
-kite
-baseball bat
-baseball glove
-skateboard
-surfboard
-tennis racket
-bottle
-wine glass
-cup
-fork
-knife
-spoon
-bowl
-banana
-apple
-sandwich
-orange
-broccoli
-carrot
-hot dog
-pizza
-donut
-cake
-chair
-sofa
-pottedplant
-bed
-diningtable
-toilet
-tvmonitor
-laptop
-mouse
-remote
-keyboard
-cell phone
-microwave
-oven
-toaster
-sink
-refrigerator
-book
-clock
-vase
-scissors
-teddy bear
-hair drier
-toothbrush
diff --git a/build/darknet/x64/data/coco9k.map b/build/darknet/x64/data/coco9k.map
deleted file mode 100644
index 5155b652358..00000000000
--- a/build/darknet/x64/data/coco9k.map
+++ /dev/null
@@ -1,80 +0,0 @@
-5177
-3768
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diff --git a/build/darknet/x64/data/combine9k.data b/build/darknet/x64/data/combine9k.data
deleted file mode 100644
index 06a3e76aefa..00000000000
--- a/build/darknet/x64/data/combine9k.data
+++ /dev/null
@@ -1,10 +0,0 @@
-classes= 9418
-#train  = /home/pjreddie/data/coco/trainvalno5k.txt
-train  = data/combine9k.train.list
-valid  = /home/pjreddie/data/imagenet/det.val.files
-labels = data/9k.labels
-names  = data/9k.names
-backup = backup/
-map = data/inet9k.map
-eval = imagenet
-results = results
diff --git a/build/darknet/x64/data/dog.jpg b/build/darknet/x64/data/dog.jpg
deleted file mode 100644
index 77b0381222e..00000000000
Binary files a/build/darknet/x64/data/dog.jpg and /dev/null differ
diff --git a/build/darknet/x64/data/eagle.jpg b/build/darknet/x64/data/eagle.jpg
deleted file mode 100644
index 8b7509505b0..00000000000
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diff --git a/build/darknet/x64/data/giraffe.jpg b/build/darknet/x64/data/giraffe.jpg
deleted file mode 100644
index a93e8b88398..00000000000
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diff --git a/build/darknet/x64/data/goal.txt b/build/darknet/x64/data/goal.txt
deleted file mode 100644
index c63d157341d..00000000000
--- a/build/darknet/x64/data/goal.txt
+++ /dev/null
@@ -1,3 +0,0 @@
-+++++
-val_eq (Val.add (Val.add (r3 PC) Vone) Vone) (Val.add (x2 PC) Vone)
-*****
diff --git a/build/darknet/x64/data/horses.jpg b/build/darknet/x64/data/horses.jpg
deleted file mode 100644
index 3a761f46ba0..00000000000
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diff --git a/build/darknet/x64/data/imagenet.labels.list b/build/darknet/x64/data/imagenet.labels.list
deleted file mode 100644
index 23268724401..00000000000
--- a/build/darknet/x64/data/imagenet.labels.list
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diff --git a/build/darknet/x64/data/imagenet.shortnames.list b/build/darknet/x64/data/imagenet.shortnames.list
deleted file mode 100644
index e9600eb5b93..00000000000
--- a/build/darknet/x64/data/imagenet.shortnames.list
+++ /dev/null
@@ -1,21842 +0,0 @@
-kit fox
-English setter
-Siberian husky
-Australian terrier
-English springer
-grey whale
-lesser panda
-Egyptian cat
-ibex
-Persian cat
-cougar
-gazelle
-porcupine
-sea lion
-malamute
-badger
-Great Dane
-Walker hound
-Welsh springer spaniel
-whippet
-Scottish deerhound
-killer whale
-mink
-African elephant
-Weimaraner
-soft-coated wheaten terrier
-Dandie Dinmont
-red wolf
-Old English sheepdog
-jaguar
-otterhound
-bloodhound
-Airedale
-hyena
-meerkat
-giant schnauzer
-titi
-three-toed sloth
-sorrel
-black-footed ferret
-dalmatian
-black-and-tan coonhound
-papillon
-skunk
-Staffordshire bullterrier
-Mexican hairless
-Bouvier des Flandres
-weasel
-miniature poodle
-Cardigan
-malinois
-bighorn
-fox squirrel
-colobus
-tiger cat
-Lhasa
-impala
-coyote
-Yorkshire terrier
-Newfoundland
-brown bear
-red fox
-Norwegian elkhound
-Rottweiler
-hartebeest
-Saluki
-grey fox
-schipperke
-Pekinese
-Brabancon griffon
-West Highland white terrier
-Sealyham terrier
-guenon
-mongoose
-indri
-tiger
-Irish wolfhound
-wild boar
-EntleBucher
-zebra
-ram
-French bulldog
-orangutan
-basenji
-leopard
-Bernese mountain dog
-Maltese dog
-Norfolk terrier
-toy terrier
-vizsla
-cairn
-squirrel monkey
-groenendael
-clumber
-Siamese cat
-chimpanzee
-komondor
-Afghan hound
-Japanese spaniel
-proboscis monkey
-guinea pig
-white wolf
-ice bear
-gorilla
-borzoi
-toy poodle
-Kerry blue terrier
-ox
-Scotch terrier
-Tibetan mastiff
-spider monkey
-Doberman
-Boston bull
-Greater Swiss Mountain dog
-Appenzeller
-Shih-Tzu
-Irish water spaniel
-Pomeranian
-Bedlington terrier
-warthog
-Arabian camel
-siamang
-miniature schnauzer
-collie
-golden retriever
-Irish terrier
-affenpinscher
-Border collie
-hare
-boxer
-silky terrier
-beagle
-Leonberg
-German short-haired pointer
-patas
-dhole
-baboon
-macaque
-Chesapeake Bay retriever
-bull mastiff
-kuvasz
-capuchin
-pug
-curly-coated retriever
-Norwich terrier
-flat-coated retriever
-hog
-keeshond
-Eskimo dog
-Brittany spaniel
-standard poodle
-Lakeland terrier
-snow leopard
-Gordon setter
-dingo
-standard schnauzer
-hamster
-Tibetan terrier
-Arctic fox
-wire-haired fox terrier
-basset
-water buffalo
-American black bear
-Angora
-bison
-howler monkey
-hippopotamus
-chow
-giant panda
-American Staffordshire terrier
-Shetland sheepdog
-Great Pyrenees
-Chihuahua
-tabby
-marmoset
-Labrador retriever
-Saint Bernard
-armadillo
-Samoyed
-bluetick
-redbone
-polecat
-marmot
-kelpie
-gibbon
-llama
-miniature pinscher
-wood rabbit
-Italian greyhound
-lion
-cocker spaniel
-Irish setter
-dugong
-Indian elephant
-beaver
-Sussex spaniel
-Pembroke
-Blenheim spaniel
-Madagascar cat
-Rhodesian ridgeback
-lynx
-African hunting dog
-langur
-Ibizan hound
-timber wolf
-cheetah
-English foxhound
-briard
-sloth bear
-Border terrier
-German shepherd
-otter
-koala
-tusker
-echidna
-wallaby
-platypus
-wombat
-revolver
-umbrella
-schooner
-soccer ball
-accordion
-ant
-starfish
-chambered nautilus
-grand piano
-laptop
-strawberry
-airliner
-warplane
-airship
-balloon
-space shuttle
-fireboat
-gondola
-speedboat
-lifeboat
-canoe
-yawl
-catamaran
-trimaran
-container ship
-liner
-pirate
-aircraft carrier
-submarine
-wreck
-half track
-tank
-missile
-bobsled
-dogsled
-bicycle-built-for-two
-mountain bike
-freight car
-passenger car
-barrow
-shopping cart
-motor scooter
-forklift
-electric locomotive
-steam locomotive
-amphibian
-ambulance
-beach wagon
-cab
-convertible
-jeep
-limousine
-minivan
-Model T
-racer
-sports car
-go-kart
-golfcart
-moped
-snowplow
-fire engine
-garbage truck
-pickup
-tow truck
-trailer truck
-moving van
-police van
-recreational vehicle
-streetcar
-snowmobile
-tractor
-mobile home
-tricycle
-unicycle
-horse cart
-jinrikisha
-oxcart
-bassinet
-cradle
-crib
-four-poster
-bookcase
-china cabinet
-medicine chest
-chiffonier
-table lamp
-file
-park bench
-barber chair
-throne
-folding chair
-rocking chair
-studio couch
-toilet seat
-desk
-pool table
-dining table
-entertainment center
-wardrobe
-Granny Smith
-orange
-lemon
-fig
-pineapple
-banana
-jackfruit
-custard apple
-pomegranate
-acorn
-hip
-ear
-rapeseed
-corn
-buckeye
-organ
-upright
-chime
-drum
-gong
-maraca
-marimba
-steel drum
-banjo
-cello
-violin
-harp
-acoustic guitar
-electric guitar
-cornet
-French horn
-trombone
-harmonica
-ocarina
-panpipe
-bassoon
-oboe
-sax
-flute
-daisy
-yellow lady's slipper
-cliff
-valley
-alp
-volcano
-promontory
-sandbar
-coral reef
-lakeside
-seashore
-geyser
-hatchet
-cleaver
-letter opener
-plane
-power drill
-lawn mower
-hammer
-corkscrew
-can opener
-plunger
-screwdriver
-shovel
-plow
-chain saw
-cock
-hen
-ostrich
-brambling
-goldfinch
-house finch
-junco
-indigo bunting
-robin
-bulbul
-jay
-magpie
-chickadee
-water ouzel
-kite
-bald eagle
-vulture
-great grey owl
-black grouse
-ptarmigan
-ruffed grouse
-prairie chicken
-peacock
-quail
-partridge
-African grey
-macaw
-sulphur-crested cockatoo
-lorikeet
-coucal
-bee eater
-hornbill
-hummingbird
-jacamar
-toucan
-drake
-red-breasted merganser
-goose
-black swan
-white stork
-black stork
-spoonbill
-flamingo
-American egret
-little blue heron
-bittern
-crane
-limpkin
-American coot
-bustard
-ruddy turnstone
-red-backed sandpiper
-redshank
-dowitcher
-oystercatcher
-European gallinule
-pelican
-king penguin
-albatross
-great white shark
-tiger shark
-hammerhead
-electric ray
-stingray
-barracouta
-coho
-tench
-goldfish
-eel
-rock beauty
-anemone fish
-lionfish
-puffer
-sturgeon
-gar
-loggerhead
-leatherback turtle
-mud turtle
-terrapin
-box turtle
-banded gecko
-common iguana
-American chameleon
-whiptail
-agama
-frilled lizard
-alligator lizard
-Gila monster
-green lizard
-African chameleon
-Komodo dragon
-triceratops
-African crocodile
-American alligator
-thunder snake
-ringneck snake
-hognose snake
-green snake
-king snake
-garter snake
-water snake
-vine snake
-night snake
-boa constrictor
-rock python
-Indian cobra
-green mamba
-sea snake
-horned viper
-diamondback
-sidewinder
-European fire salamander
-common newt
-eft
-spotted salamander
-axolotl
-bullfrog
-tree frog
-tailed frog
-whistle
-wing
-paintbrush
-hand blower
-oxygen mask
-snorkel
-loudspeaker
-microphone
-screen
-mouse
-electric fan
-oil filter
-strainer
-space heater
-stove
-guillotine
-barometer
-rule
-odometer
-scale
-analog clock
-digital clock
-wall clock
-hourglass
-sundial
-parking meter
-stopwatch
-digital watch
-stethoscope
-syringe
-magnetic compass
-binoculars
-projector
-sunglasses
-loupe
-radio telescope
-bow
-cannon
-assault rifle
-rifle
-projectile
-computer keyboard
-typewriter keyboard
-crane
-lighter
-abacus
-cash machine
-slide rule
-desktop computer
-hand-held computer
-notebook
-web site
-harvester
-thresher
-printer
-slot
-vending machine
-sewing machine
-joystick
-switch
-hook
-car wheel
-paddlewheel
-pinwheel
-potter's wheel
-gas pump
-carousel
-swing
-reel
-radiator
-puck
-hard disc
-sunglass
-pick
-car mirror
-solar dish
-remote control
-disk brake
-buckle
-hair slide
-knot
-combination lock
-padlock
-nail
-safety pin
-screw
-muzzle
-seat belt
-ski
-candle
-jack-o'-lantern
-spotlight
-torch
-neck brace
-pier
-tripod
-maypole
-mousetrap
-spider web
-trilobite
-harvestman
-scorpion
-black and gold garden spider
-barn spider
-garden spider
-black widow
-tarantula
-wolf spider
-tick
-centipede
-isopod
-Dungeness crab
-rock crab
-fiddler crab
-king crab
-American lobster
-spiny lobster
-crayfish
-hermit crab
-tiger beetle
-ladybug
-ground beetle
-long-horned beetle
-leaf beetle
-dung beetle
-rhinoceros beetle
-weevil
-fly
-bee
-grasshopper
-cricket
-walking stick
-cockroach
-mantis
-cicada
-leafhopper
-lacewing
-dragonfly
-damselfly
-admiral
-ringlet
-monarch
-cabbage butterfly
-sulphur butterfly
-lycaenid
-jellyfish
-sea anemone
-brain coral
-flatworm
-nematode
-conch
-snail
-slug
-sea slug
-chiton
-sea urchin
-sea cucumber
-iron
-espresso maker
-microwave
-Dutch oven
-rotisserie
-toaster
-waffle iron
-vacuum
-dishwasher
-refrigerator
-washer
-Crock Pot
-frying pan
-wok
-caldron
-coffeepot
-teapot
-spatula
-altar
-triumphal arch
-patio
-steel arch bridge
-suspension bridge
-viaduct
-barn
-greenhouse
-palace
-monastery
-library
-apiary
-boathouse
-church
-mosque
-stupa
-planetarium
-restaurant
-cinema
-home theater
-lumbermill
-coil
-obelisk
-totem pole
-castle
-prison
-grocery store
-bakery
-barbershop
-bookshop
-butcher shop
-confectionery
-shoe shop
-tobacco shop
-toyshop
-fountain
-cliff dwelling
-yurt
-dock
-brass
-megalith
-bannister
-breakwater
-dam
-chainlink fence
-picket fence
-worm fence
-stone wall
-grille
-sliding door
-turnstile
-mountain tent
-scoreboard
-honeycomb
-plate rack
-pedestal
-beacon
-mashed potato
-bell pepper
-head cabbage
-broccoli
-cauliflower
-zucchini
-spaghetti squash
-acorn squash
-butternut squash
-cucumber
-artichoke
-cardoon
-mushroom
-shower curtain
-jean
-carton
-handkerchief
-sandal
-ashcan
-safe
-plate
-necklace
-croquet ball
-fur coat
-thimble
-pajama
-running shoe
-cocktail shaker
-chest
-manhole cover
-modem
-tub
-tray
-balance beam
-bagel
-prayer rug
-kimono
-hot pot
-whiskey jug
-knee pad
-book jacket
-spindle
-ski mask
-beer bottle
-crash helmet
-bottlecap
-tile roof
-mask
-maillot
-Petri dish
-football helmet
-bathing cap
-teddy
-holster
-pop bottle
-photocopier
-vestment
-crossword puzzle
-golf ball
-trifle
-suit
-water tower
-feather boa
-cloak
-red wine
-drumstick
-shield
-Christmas stocking
-hoopskirt
-menu
-stage
-bonnet
-meat loaf
-baseball
-face powder
-scabbard
-sunscreen
-beer glass
-hen-of-the-woods
-guacamole
-lampshade
-wool
-hay
-bow tie
-mailbag
-water jug
-bucket
-dishrag
-soup bowl
-eggnog
-mortar
-trench coat
-paddle
-chain
-swab
-mixing bowl
-potpie
-wine bottle
-shoji
-bulletproof vest
-drilling platform
-binder
-cardigan
-sweatshirt
-pot
-birdhouse
-hamper
-ping-pong ball
-pencil box
-pay-phone
-consomme
-apron
-punching bag
-backpack
-groom
-bearskin
-pencil sharpener
-broom
-mosquito net
-abaya
-mortarboard
-poncho
-crutch
-Polaroid camera
-space bar
-cup
-racket
-traffic light
-quill
-radio
-dough
-cuirass
-military uniform
-lipstick
-shower cap
-monitor
-oscilloscope
-mitten
-brassiere
-French loaf
-vase
-milk can
-rugby ball
-paper towel
-earthstar
-envelope
-miniskirt
-cowboy hat
-trolleybus
-perfume
-bathtub
-hotdog
-coral fungus
-bullet train
-pillow
-toilet tissue
-cassette
-carpenter's kit
-ladle
-stinkhorn
-lotion
-hair spray
-academic gown
-dome
-crate
-wig
-burrito
-pill bottle
-chain mail
-theater curtain
-window shade
-barrel
-washbasin
-ballpoint
-basketball
-bath towel
-cowboy boot
-gown
-window screen
-agaric
-cellular telephone
-nipple
-barbell
-mailbox
-lab coat
-fire screen
-minibus
-packet
-maze
-pole
-horizontal bar
-sombrero
-pickelhaube
-rain barrel
-wallet
-cassette player
-comic book
-piggy bank
-street sign
-bell cote
-fountain pen
-Windsor tie
-volleyball
-overskirt
-sarong
-purse
-bolo tie
-bib
-parachute
-sleeping bag
-television
-swimming trunks
-measuring cup
-espresso
-pizza
-breastplate
-shopping basket
-wooden spoon
-saltshaker
-chocolate sauce
-ballplayer
-goblet
-gyromitra
-stretcher
-water bottle
-dial telephone
-soap dispenser
-jersey
-school bus
-jigsaw puzzle
-plastic bag
-reflex camera
-diaper
-Band Aid
-ice lolly
-velvet
-tennis ball
-gasmask
-doormat
-Loafer
-ice cream
-pretzel
-quilt
-maillot
-tape player
-clog
-iPod
-bolete
-scuba diver
-pitcher
-matchstick
-bikini
-sock
-CD player
-lens cap
-thatch
-vault
-beaker
-bubble
-cheeseburger
-parallel bars
-flagpole
-coffee mug
-rubber eraser
-stole
-carbonara
-dumbbell
-singles
-Virginia deer
-eastern grey squirrel
-gelding
-pylon
-table-tennis table
-peacock
-Segway
-surfing
-tamandua
-knocker
-steering wheel
-motorcycling
-coati
-sitar
-range
-backhoe
-agaric
-dashboard
-water polo
-concrete mixer
-treadmill
-golf bag
-skateboarding
-royal tennis
-tartan
-four-wheel drive
-sport utility
-sedan
-print
-luggage rack
-softball
-windmill
-ben
-red admiral
-jalousie
-towel rail
-truss
-strand
-ice hockey
-sconce
-wind turbine
-plush
-stained-glass window
-ballpark
-thoroughbred
-love seat
-red-spotted purple
-miller
-Adelie
-freight liner
-clock tower
-acrobatics
-shaving brush
-ewe
-ottoman
-African violet
-bicycle wheel
-cork
-windmill
-satin
-comma
-coffee mill
-baggage
-wasp's nest
-batting glove
-Ferris wheel
-push-bike
-porthole
-football stadium
-gas tank
-barbecue
-handlebar
-hula-hoop
-fairground
-rapier
-garter stitch
-exercise bike
-control tower
-carryall
-minute hand
-cog
-riverbank
-water nymph
-common dandelion
-android
-hairbrush
-redberry
-fret
-display window
-pepper mill
-litterbin
-drapery
-ducking
-fly-fishing
-broad jump
-sprinkler
-water-skiing
-chicory
-sail
-volleyball
-rugby
-Texas bluebonnet
-computer monitor
-tortoiseshell
-airplane propeller
-solar array
-figure skating
-air conditioner
-purple loosestrife
-gearshift
-outboard motor
-cowslip
-Abyssinian
-dip
-workstation
-cosy
-bunker
-neon lamp
-campanile
-casket
-verbena
-amphora
-sumo
-common foxglove
-sprocket
-jelly bean
-emperor penguin
-night-blooming cereus
-clock radio
-black birch
-bomber jacket
-Virginia bluebell
-bayonet
-walker
-altarpiece
-tattoo
-bridle
-rocker arm
-water turkey
-spiderwort
-flange
-mute swan
-laser printer
-carburetor
-coverlet
-mountainside
-baritone
-auto racing
-baluster
-gal
-peach bells
-taffeta
-grandfather
-asparagus
-horizontal stabilizer
-world
-grate
-marsh marigold
-white rhinoceros
-movement
-split rail
-rollerblading
-longhorn
-muffler
-church tower
-light bulb
-American agave
-backpacking tent
-overall
-New World goldfinch
-sectional
-wing chair
-transom
-integrated circuit
-dad
-spar
-picture frame
-no-hit game
-alternator
-drill press
-strawflower
-hepatica
-rangefinder
-blinker
-Welsh pony
-nib
-wagon wheel
-rotor
-tie
-denim
-jetliner
-sculling
-external drive
-window frame
-mourning dove
-censer
-stapler
-batting helmet
-flagon
-machete
-windshield
-hedgehog
-weeping willow
-chief executive officer
-hepatica
-pet
-Asiatic black bear
-chinchilla
-uke
-Atlantic bottlenose dolphin
-hair
-dishtowel
-flintlock
-Bermuda shorts
-lavender
-searchlight
-millwheel
-piano keyboard
-luna moth
-bumper
-parrot
-skirt
-manhole
-coffee table
-footstool
-judo
-Dalai Lama
-armored personnel carrier
-voile
-saber
-thoroughbred
-wild carrot
-gemsbok
-caster
-butterfly orchid
-cow
-sideboard
-horseshoe crab
-match play
-cassette recorder
-photomicrograph
-drafting table
-pediment
-tramline
-shipping
-kitten
-wainscoting
-fried rice
-helix
-marguerite
-pumpkin
-white-bellied swallow
-Tulipa gesneriana
-common dolphin
-face
-red squirrel
-bicycling
-shipwreck
-banded purple
-cornice
-pendant earring
-forsythia
-aardvark
-seashell
-spat
-shoulder bag
-fallow deer
-yearling
-common teasel
-tufted titmouse
-ancient
-professional golf
-purl
-vehicle
-okra
-great grandmother
-common lilac
-rose mallow
-newspaper
-crucifix
-chukka
-armlet
-fulmar
-wapiti
-doily
-Greco-Roman wrestling
-bleeding heart
-kitchen table
-bluebonnet
-Cape buffalo
-spun yarn
-crape myrtle
-dewdrop
-great blue heron
-medalist
-vaulting horse
-spinning wheel
-skyscraper
-Tahitian
-forget-me-not
-watercourse
-guitarist
-gargoyle
-bee balm
-pumpkin
-hunting knife
-flutist
-lectern
-skateboarder
-foil
-pant leg
-hedge sparrow
-dresser
-automatic pistol
-chicory
-dialog box
-chamberpot
-black rhinoceros
-fireweed
-half-mast
-pillow sham
-pavilion
-scarf joint
-microprocessor
-filly
-dressing gown
-shell
-Arabian
-child
-radio antenna
-butterweed
-morris dancer
-sparrow hawk
-groom
-brioche
-floret
-rainbow
-earthworm
-cellist
-tine
-toupee
-balldress
-map
-angel's trumpet
-ruin
-fur
-pronghorn
-speed skating
-used-car
-stick
-early spider orchid
-stuffed peppers
-snowdrift
-flats
-least sandpiper
-stick
-console table
-ventilator
-portable
-kepi
-pylon
-viceroy
-shoreline
-Olympian Zeus
-pestle
-great-niece
-life
-air compressor
-fanjet
-scuba diving
-fieldfare
-tree swallow
-personnel carrier
-night-blooming cereus
-sonogram
-assembly hall
-circuit breaker
-chair
-speed skate
-soapwort
-worsted
-raspberry
-burlap
-flat panel display
-Pyracantha
-cemetery
-turban
-deer hunting
-bottle green
-dandelion green
-pieta
-aigrette
-turntable
-cover girl
-clutch bag
-kiwi
-pea jacket
-color guard
-Malay
-shire
-crock
-french fries
-credenza
-hockey stick
-mourning cloak
-potty seat
-glass
-balsamroot
-medal play
-red clover
-gravy boat
-garter belt
-Guinness
-meadow buttercup
-jackass penguin
-coursing
-tooth
-hawfinch
-housetop
-fluorescent lamp
-black-backed gull
-bookshelf
-earplug
-millipede
-fawn
-baseball bat
-soup-strainer
-organ loft
-bugloss
-tomahawk
-blackcap
-black-necked stilt
-hand truck
-bedstead
-tempura
-rose window
-crimson
-snow thrower
-lesser whitethroat
-palomino
-ball
-staff sergeant
-wicker
-garbage heap
-great-nephew
-parquet
-coupe
-nave
-eggs Benedict
-damask
-flush toilet
-Angora
-pedometer
-control room
-bristle brush
-kookaburra
-telephone booth
-Windsor chair
-red-winged blackbird
-cinnamon roll
-briefs
-cloister
-sundress
-mammillaria
-unicyclist
-covered bridge
-coelogyne
-fairy bluebird
-phoebe
-beer mug
-headstock
-parhelion
-gorse
-common European dogwood
-fire-eater
-professional football
-rock climbing
-cyclamen
-tin
-marjoram
-Japanese morning glory
-pipe
-smasher
-hang glider
-abutment
-birdbath
-jotter
-litter
-artist's model
-butterfly bush
-dining area
-sausage dog
-piggery
-English sparrow
-Turk's-cap
-platinum blond
-song sparrow
-alarm clock
-tortoiseshell
-chaise longue
-flintlock
-academic costume
-graffito
-Arnica montana
-adding machine
-waterside
-director
-jonquil
-pipefitting
-stud
-Swedish meatball
-musk rose
-Venus's flytrap
-raven
-bougainvillea
-little brother
-field bindweed
-finder
-white admiral
-tinfoil
-serval
-sheet
-carthorse
-people
-potto
-stockroom
-sphinx
-slate roof
-mountain laurel
-majolica
-coal black
-repository
-bufo
-pique
-binder
-tread
-attorney general
-hydraulic press
-videocassette recorder
-bumper car
-professional baseball
-cow parsley
-ern
-blue peafowl
-common hyacinth
-jack-in-the-pulpit
-ice hockey rink
-sport
-camper
-tailback
-flash
-stacks
-pulp
-Christmas cactus
-netball
-calliandra
-curler
-large periwinkle
-cobweb
-forward
-Roman arch
-cross bun
-stoneware
-banana bread
-cape jasmine
-settle
-tongue
-frock
-pepper shaker
-pitching coach
-CD-R
-casing
-faience
-hand cream
-CD-ROM
-recliner
-striped bass
-clary
-sketch
-risotto
-reticle
-white clover
-touch football
-kitty
-great-aunt
-Japanese maple
-sidecar
-muscovy duck
-hack
-rope bridge
-organist
-stinging nettle
-pocket watch
-Indian pipe
-amorphophallus
-bird's-foot violet
-caller ID
-furnishing
-carriageway
-dish rack
-heiress
-nail polish
-beldam
-Dall sheep
-teriyaki
-stateroom
-laughing gull
-chow
-bookmark
-timer
-toga virilis
-deviled egg
-coltsfoot
-Papuan
-native
-cygnet
-automation
-portfolio
-cabbage palm
-cube
-broiler
-radish
-broodmare
-castor-oil plant
-pith hat
-talus
-lass
-thatch
-common marigold
-young buck
-igloo
-prairie rattlesnake
-soccer player
-spoke
-place
-slide fastener
-tapestry
-toy
-headboard
-cross-country skiing
-harness
-sconce
-rim
-ballet skirt
-transvestite
-saddlebag
-common evening primrose
-taillight
-challah
-willet
-ready-to-wear
-cloud
-answering machine
-waterfront
-vane
-granddaughter
-Chinese gooseberry
-tureen
-cab
-truffle
-viola
-bootlace
-chemise
-taro
-petal
-candied apple
-soccer
-miniature golf
-front porch
-asparagus
-Sauvignon blanc
-daisy fleabane
-ceiling
-slip-on
-bottle-nosed whale
-redbud
-black squirrel
-snowsuit
-ribbing
-gravestone
-creme brulee
-ambassador
-local
-archery
-love-in-a-mist
-garbage
-thyme
-night-blooming cereus
-goshawk
-cuckoopint
-azure
-German iris
-salad bowl
-puppy
-cockhorse
-giant clam
-biplane
-stele
-necklet
-sea otter
-crest
-door
-reformer
-comforter
-Byelorussian
-bottle
-hemline
-book bag
-leotard
-owlet
-spoon
-sari
-bidet
-Latin
-reticulated python
-bowling shoe
-futon
-gaiter
-coypu
-tea urn
-waders
-bangle
-snowbank
-pencil
-porter
-azalea
-English lavender
-red spruce
-team sport
-cruet
-high-rise
-O ring
-vodka
-cormorant
-Canada thistle
-clasp
-showjumping
-rattan
-red fox
-sun parlor
-Charolais
-Tommy gun
-bird's foot trefoil
-sedge warbler
-knot
-chives
-car tire
-steam engine
-adapter
-spirea
-common allamanda
-oyster shell
-harbor seal
-baobab
-wick
-plumbago
-downy woodpecker
-coconut
-leash
-kasbah
-hour hand
-upholstery
-mallard
-cricket bat
-lady
-kitchenware
-right-hander
-leopard
-olive green
-common valerian
-blue whale
-blackboard
-redhead
-periwinkle
-fingerboard
-hard hat
-locker
-breakfast table
-capybara
-beekeeper
-harness
-feeder
-water hyacinth
-hexapod
-brown thrasher
-percale
-lever
-patriarch
-arete
-book
-book
-senator
-bunya bunya
-couch
-durian
-common lady's-slipper
-mountain ash
-golden barrel cactus
-bicycle seat
-beret
-pop
-musk mallow
-manatee
-cotton candy
-boxing glove
-backboard
-tongue
-saguaro
-playground
-capitol
-sanderling
-wagtail
-deputy
-tractor
-tap
-lady's smock
-noseband
-worsted
-radiotelephone
-camisole
-forelock
-muscat
-sweet scabious
-crane fly
-butterfly weed
-chestnut
-pinata
-inositol
-borage
-aquatic
-belly
-broadcaster
-gondolier
-egg yolk
-blush wine
-bufflehead
-rambutan
-oleander
-horse-trail
-sea holly
-yard bird
-conference room
-lacrosse
-belted kingfisher
-defile
-extremum
-whistle
-bear cub
-grainfield
-potage
-watermelon
-lasagna
-sheik
-Cooper's hawk
-bulb
-basketball court
-paella
-cassette tape
-scatter rug
-kid
-impala lily
-Minnesotan
-Sudanese
-chocolate
-tail
-quack-quack
-whistling swan
-shoulder patch
-frozen custard
-sumo wrestler
-smoothie
-bock
-meat grinder
-latch
-palisade
-radial
-sake
-kestrel
-corn chowder
-airframe
-electrician
-reamer
-metropolitan
-cotton flannel
-cassowary
-crossbill
-operating room
-winter aconite
-flute
-Tasmanian devil
-billboard
-suds
-kilt
-aperitif
-cooling tower
-avocado
-hooded merganser
-coleslaw
-bee balm
-ladder-back
-insurance broker
-scaffolding
-polo mallet
-double bed
-two-hitter
-bluff
-gamboge
-baby
-lawn chair
-frond
-pistol grip
-fancy dress
-marquetry
-jambalaya
-fireweed
-Eurasian kingfisher
-cue ball
-ice plant
-horseweed
-rose moss
-musher
-sun
-viscount
-white-breasted nuthatch
-gin and tonic
-thermos
-Kenyan
-first-aid kit
-four-wheeler
-tourist
-stairwell
-Gambian
-liqueur glass
-hovercraft
-cocktail dress
-twin
-coriander
-blister pack
-Barrow's goldeneye
-canteen
-irrigation ditch
-great white heron
-tree sparrow
-canal boat
-lens
-food processor
-common raccoon
-Baltimore oriole
-black-eyed Susan
-bush hibiscus
-corolla
-sire
-mustachio
-professional wrestling
-elk
-clustered bellflower
-pannier
-musk ox
-crapaud
-animal trainer
-rosebud
-ring-necked pheasant
-little egret
-cappuccino
-rocker
-bristlecone pine
-cheerleader
-hedge violet
-semaphore
-central processing unit
-speedskater
-delivery truck
-assembly
-hedgehog cactus
-bergenia
-bull thistle
-bladder campion
-cinquefoil
-inula
-cellulose tape
-main rotor
-bootee
-autogiro
-ice
-grey
-meadow cranesbill
-hummus
-valise
-chassis
-mountain goat
-blacktail prairie dog
-Chardonnay
-romper
-street
-shoveler
-wood ibis
-topiary
-chalice
-silo
-circus acrobat
-Rollerblade
-cosmos
-woof
-heroine
-cold cream
-marabou
-herb robert
-garden lettuce
-nymph
-floor lamp
-automobile engine
-heel
-radiator
-seeded player
-fedora
-father-in-law
-peahen
-Bahamian
-wiper
-wood pigeon
-barn owl
-pegboard
-chorus frog
-kin
-roller skate
-stob
-rosemary
-cowbird
-hortensia
-cranberry sauce
-shot glass
-Dixie cup
-gnu
-fire alarm
-diet
-booster
-oxeye daisy
-twayblade
-high-definition television
-truss bridge
-bunk bed
-mule
-blackbuck
-facsimile
-frog orchid
-point-and-shoot camera
-brocade
-gazebo
-prairie gentian
-concert
-paintball
-Cognac
-maid
-afghan
-barbecued spareribs
-pintail
-tramway
-commissioner
-finger-painting
-beef stew
-caftan
-Aberdeen Angus
-demonstrator
-sea trout
-pigtail
-thrush nightingale
-barbados cherry
-sashimi
-ridgeling
-lamppost
-gabardine
-red-shouldered hawk
-bath salts
-cavern
-cymbid
-Haitian
-boater
-southern buckthorn
-arctic
-motorcycle cop
-red gum
-Clydesdale
-Zamboni
-beagling
-villa
-demitasse
-Sheetrock
-lollipop
-hybrid petunia
-post horse
-carabiner
-brussels sprouts
-Durham
-stylist
-pothole
-sleigh bed
-scallop shell
-harrier eagle
-papaya
-Japanese persimmon
-sachet
-wild rice
-chipboard
-gun enclosure
-menorah
-chinook
-headset
-white campion
-ocean
-Secretary of State
-G-string
-bone china
-basil
-greenish blue
-camcorder
-concrete
-screech owl
-trumpet honeysuckle
-flugelhorn
-layette
-cattle egret
-case knife
-mandarin duck
-robber fly
-salwar
-dressing table
-doughnut
-facade
-runner
-honeypot
-surf casting
-diver
-angel's trumpet
-spin dryer
-chameleon
-wand
-snow
-vitamin A1
-manageress
-volleyball net
-antiperspirant
-street clothes
-tree sparrow
-cords
-sundew
-bricks and mortar
-caryatid
-bridesmaid
-trestle bridge
-eyepiece
-celebrant
-scarlet pimpernel
-gas range
-onion
-green salad
-squill
-creepy-crawly
-hunk
-little owl
-salad nicoise
-earflap
-bird feeder
-spray gun
-bunny
-Cheops
-amazon
-blue tit
-Nissen hut
-Kalashnikov
-skylark
-kremlin
-shoebill
-shopping bag
-frigate bird
-telephoto lens
-peplum
-moss pink
-echidna
-wastepaper basket
-wood ibis
-workroom
-ankle brace
-telpherage
-Michaelmas daisy
-figure skate
-swami
-nylons
-cardoon
-cocotte
-headstall
-twin bed
-parsley
-dirndl
-corn poppy
-nut bread
-cloche
-light heavyweight
-mayor
-lip-gloss
-punch bowl
-pottage
-mango
-fledgling
-mousse
-four-wheel drive
-barrel
-banana boat
-trouser
-bathroom
-Sauterne
-ring
-settee
-lavaliere
-safe-deposit
-godson
-leatherette
-schoolmate
-radish
-hedge trimmer
-dahlia
-euphonium
-palace
-vaulter
-singlet
-slicer
-Pilsner
-cockateel
-kangaroo paw
-Cub Scout
-master bedroom
-hexagon
-cenotaph
-Barberton daisy
-Netherlander
-intersection
-Korean
-gravel
-chandelier
-hospital bed
-flash memory
-pier
-whole wheat flour
-maroon
-pale ale
-special
-snow bunting
-crinoline
-dustpan
-barrette
-common wood sorrel
-yolk
-pothos
-speakerphone
-tendril
-cabinetwork
-farm horse
-brake disk
-streetlight
-superhighway
-bandsaw
-panting
-pressure cooker
-girdle
-old man
-cereal bowl
-felt
-hurling
-architecture
-harmonium
-chain
-blueberry
-cellar
-smocking
-scrub brush
-tablespoon
-sweet corn
-graining
-library
-street
-bill
-felt-tip pen
-monkshood
-crowd
-log cabin
-newel post
-hack
-elephant seal
-golden pothos
-popcorn
-outhouse
-patch pocket
-fish and chips
-tape
-wax plant
-eaves
-fried egg
-emerald
-tea cart
-fan blade
-daily
-Bowie knife
-rowing boat
-leaf shape
-man
-crayon
-trumpetfish
-chipping sparrow
-whiskey bottle
-pillion
-city hall
-golden pheasant
-cheerleader
-creeping bugle
-couch
-Dumpster
-Homo sapiens sapiens
-cranberry juice
-cockpit
-demagogue
-joinery
-scrambled eggs
-technician
-sidewalk
-sheep
-keyhole
-power line
-polyanthus
-roulette
-first lieutenant
-checkout
-tabletop
-nasturtium
-schnapps
-engineering
-skateboard
-ground fir
-bouquet
-bunk
-resort area
-fleur-de-lis
-power steering
-opera
-Bolivian
-Friesian
-buckskins
-bay
-slider
-frozen yogurt
-cabin cruiser
-saunterer
-lean-to
-fishing eagle
-bog star
-cantaloupe
-mouth
-music stand
-fiddlestick
-brilliantine
-pinball machine
-bairn
-barred owl
-bath oil
-signorina
-Mason jar
-nymph
-rubber band
-garden nasturtium
-razorbill
-Japanese beetle
-batting cage
-trestle
-borage
-Secretary of the Interior
-scanner
-baguet
-baseball cap
-chow mein
-pen
-jewelweed
-barbet
-chasm
-pectoral sandpiper
-holster
-glasses case
-sand
-crevice
-Kickapoo
-snowboard
-locket
-satchel
-tankard
-alpinist
-moorhen
-cow pen
-whooper
-crown
-chain
-silversword
-wild geranium
-hi-fi
-Tibetan
-waterwheel
-bee orchid
-ruby-crowned kinglet
-common broom
-tabloid
-javelin
-sauna
-klammath weed
-zebra finch
-spider orchid
-velour
-chiffon
-lecture room
-barrel
-loggia
-millstone
-flatlet
-soupspoon
-econometrician
-golf-club head
-daphnia
-parlor
-fire-eater
-juggler
-attache case
-hay bale
-kisser
-knitting needle
-news magazine
-flatbed
-Senegalese
-trumpeter
-trampoline
-brogan
-bone
-caftan
-lobster pot
-gazpacho
-anthill
-ramekin
-mainsail
-penitentiary
-spotted flycatcher
-cookstove
-root beer
-broom beard grass
-pogo stick
-plywood
-epee
-gas oven
-Global Positioning System
-sweet false chamomile
-breakfast area
-bullring
-second cousin
-wave
-decolletage
-rodeo
-won ton
-swastika
-bobby pin
-papaw
-retaining wall
-Muscadet
-heavyweight
-energizer
-banner
-amusement park
-whinchat
-drugstore
-waxwork
-meander
-congee
-heat sink
-switch grass
-commuter
-peony
-western white pine
-wild raspberry
-nightgown
-saute
-cardinal
-claret
-pollinator
-biryani
-pina colada
-cassette deck
-European sandpiper
-block
-flan
-birdcage
-baby
-lieutenant colonel
-ticking
-European white lily
-dog violet
-coat hanger
-premature baby
-organza
-string bean
-balloonist
-hurricane deck
-window box
-hang glider
-bullfighting
-piste
-seahorse
-hard cider
-batik
-common mullein
-petite marmite
-stuffed mushroom
-tequila
-ground ivy
-fountain grass
-stray
-putter
-buffer
-comet
-bomber
-woodcarving
-baseball glove
-halter
-garnish
-selvage
-megaphone
-sea fan
-rabbit hutch
-very important person
-analog watch
-long-head coneflower
-northern pike
-roll-on
-cigarette butt
-terraced house
-penknife
-windshield wiper
-cricket
-straightener
-snow pea
-cockerel
-canister
-sour bread
-recovery room
-toilet bowl
-tyrannosaur
-big sister
-quartz battery
-television receiver
-vitamin C
-tailpipe
-field thistle
-stonechat
-col
-monstrance
-gift wrapping
-herbivore
-quarter horse
-ice-cream sundae
-rumpus room
-eyepatch
-clary sage
-French lavender
-snorkel
-choir
-tent-fly
-cat box
-horse racing
-high priest
-barrel cactus
-pin oak
-wild thyme
-keyboardist
-raiser
-hammock
-hail
-bungee
-chocolate mousse
-major
-buzzard
-gopher tortoise
-Chablis
-water meter
-benthos
-donna
-blender
-Mauser
-avocet
-rye
-mulch
-chancel
-dusty miller
-mate
-corbel
-minaret
-frittata
-French toast
-mosaic
-home brew
-water faucet
-beard
-swivel chair
-acropolis
-largemouth
-abbey
-tabby
-driver
-copperhead
-stirrup
-Boston fern
-Tennessee walker
-artichoke
-honor guard
-chapatti
-enchantress
-sweat pants
-electric organ
-column
-dry vermouth
-range hood
-Red Delicious
-rape
-splint
-catapult
-gourd
-antipasto
-plaza
-carnation
-star
-wood anemone
-English primrose
-male fern
-boot
-atrium
-Japanese deer
-carnivore
-yearling
-doe
-guelder rose
-chicory
-stretch pants
-ice-cream cake
-frogfish
-tarpaulin
-chicken soup
-balaclava
-tor
-feverfew
-three-hitter
-flyweight
-aqua vitae
-locker room
-wether
-teacup
-wide-angle lens
-hook
-ladder-back
-osprey
-awning
-wedding
-chest protector
-pooch
-rose mallow
-orange daisy
-fondant
-envelope
-duckling
-blackberry
-goosander
-snorkeling
-philatelist
-broad bean
-Frank
-bok choy
-basket
-absinth
-cayenne
-blackbird
-bottled water
-trooper
-timber
-stable
-chestnut
-tomatillo
-bell
-banquet
-rainbow trout
-macrame
-appointee
-heart
-chipmunk
-purple clematis
-safety bicycle
-shuttle bus
-Japanese black pine
-lentil soup
-downhill
-field mustard
-brass
-hand-me-down
-greater yellowlegs
-fanny pack
-croquet mallet
-hip roof
-duffel bag
-Ritz
-document
-pie plant
-staff member
-lifeguard
-white-throated sparrow
-Cameroonian
-hydrofoil
-platter
-common ageratum
-middleweight
-chairlift
-brunch
-pharmacist
-lemon
-driveshaft
-green snake
-lip
-London plane
-mangrove
-crystal
-siskin
-common jasmine
-hollandaise
-villa
-cross-country riding
-mother-in-law's tongue
-generator
-Tanzanian
-whisk
-seeder
-ashtray
-griddle
-evening bag
-bluebird
-bran muffin
-square dancer
-luggage compartment
-tropical pitcher plant
-autofocus
-tape drive
-silencer
-Hawaiian guitar
-swamp sparrow
-Zimbabwean
-drawing room
-weekender
-liparis
-streambed
-samosa
-hitter
-water heater
-tidal basin
-ossuary
-dik-dik
-camouflage
-fiance
-Jordanian
-rolling pin
-slingback
-turret
-hen
-jennet
-playpen
-woodhewer
-bushing
-church bell
-bear grass
-double knit
-tennis pro
-Joe-Pye weed
-pave
-pochard
-painted beauty
-crinoline
-gumbo
-trestle table
-schnitzel
-balloon flower
-Turkish coffee
-extension cord
-wireless local area network
-sluice
-umbel
-microeconomist
-sky
-aisle
-commander in chief
-hydroplane racing
-poll
-Coca Cola
-fuel injection
-bird pepper
-monkey puzzle
-English muffin
-riverbed
-varietal
-kachina
-airport
-saltwort
-oolong
-red-hot poker
-mihrab
-cocoa
-jersey
-Walkman
-syndic
-Hessian boot
-millstone
-carpenter
-outfall
-curbstone
-mocha
-field pansy
-patriarch
-slacks
-switchblade
-killdeer
-whelk
-pampas grass
-racquetball
-platform bed
-Indian rhinoceros
-Japanese iris
-blacktop
-dinner jacket
-stud
-jodhpurs
-telephone pole
-business district
-kurta
-basil
-handset
-file folder
-gloriosa
-orphan
-cantle
-cookie sheet
-cafe au lait
-drawbridge
-hill myna
-Western diamondback
-watch case
-cardcase
-bowling alley
-mattress cover
-canvasback
-pompadour
-cornice
-matador
-cigar cutter
-skunk cabbage
-baptismal font
-bitters
-refectory
-egg
-parula warbler
-tiger lily
-field house
-nanny
-skin-diver
-soda water
-lymphocyte
-carport
-chocolate fudge
-amphitheater
-sugar candy
-sea hare
-open-face sandwich
-dessert spoon
-staple gun
-envelope
-worker bee
-general
-garment bag
-maypop
-autobahn
-Atlantic puffin
-polo shirt
-Humvee
-spice rack
-grotto
-banderillero
-gaillardia
-black-crowned night heron
-oboist
-weigela
-Dictaphone
-dwarf iris
-marsh mallow
-yarrow
-eccentric
-catsup
-jade green
-mistress
-henbit
-beachwear
-head
-commuter
-strawberry tree
-chickpea
-clothespin
-fleabane
-brussels sprout
-winter melon
-Laconian
-great horned owl
-caricaturist
-nan
-flowerbed
-triple sec
-dairy
-round of golf
-cardinal
-kauri
-Zulu
-Armagnac
-cowberry
-mouthpiece
-wild calla
-bling
-puppeteer
-beer drinker
-adder
-field sparrow
-chocolate pudding
-blacksmith
-finback
-Shetland pony
-cheese fondue
-panty girdle
-soda can
-electrolytic
-florist's chrysanthemum
-yellow jasmine
-tudung
-equalizer
-ridge
-dulcimer
-grappa
-barn swallow
-coneflower
-enamel
-poached egg
-halfback
-yak
-toby
-Fleet Street
-blue catfish
-sand tiger
-flying buttress
-snaffle
-stoop
-first base
-cultivated land
-first lady
-waratah
-headquarters
-arnica
-lovebird
-common morel
-parasol
-disk clutch
-Xerox
-vitamin P
-vitamin B12
-long sleeve
-certified public accountant
-hot pants
-pitch pine
-pantie
-drawers
-cake mix
-boar
-grey
-bride
-false sago
-bullion
-coach house
-bass guitar
-Japanese banana
-meadow clary
-black belt
-Canterbury bell
-smallmouth
-treadmill
-great white heron
-enchilada
-rummer
-captain
-camisole
-wild garlic
-oak fern
-ultramarine
-peach
-hawkweed
-autostrada
-adit
-anaconda
-artwork
-skinhead
-jello
-hermit thrush
-Bewick's swan
-dress suit
-trail bike
-stubble
-common polypody
-Riesling
-Easter lily
-telegraph key
-envelope
-garlic bread
-perianth
-salad bar
-steppe
-club sandwich
-nude
-garden forget-me-not
-Tuareg
-flood
-Statehouse
-charcoal
-boy scout
-Rhone wine
-parfait
-spoor
-lanyard
-octagon
-brown bread
-quarterback
-quilted bedspread
-hookah
-Pepsi
-hamburger bun
-entrepreneur
-saddle oxford
-snake's head fritillary
-undies
-chemise
-skidder
-chickpea
-carnation
-honey bun
-mortar
-Montrachet
-automobile horn
-skylight
-gingham
-rafter
-pantile
-climbing frame
-scarlet runner
-cable
-cornstalk
-mockingbird
-raisin bread
-chili sauce
-hand calculator
-concert-goer
-detached house
-coq au vin
-lasso
-hyssop
-globe thistle
-paper clip
-slide
-Jerusalem artichoke
-tetrahedron
-mock orange
-lemon lily
-finger
-little sister
-handcuff
-horse wrangler
-pavlova
-oilcloth
-snow-in-summer
-common mugwort
-greenshank
-ice-cream cone
-rubber boot
-gunnysack
-disk jockey
-long trousers
-sorghum
-pontoon
-calf
-fire extinguisher
-cotton thistle
-pilot whale
-ao dai
-steamroller
-wristwatch
-tawny owl
-city
-country store
-ironweed
-kennel
-bathrobe
-rattan
-drawer
-fly tent
-choline
-musk thistle
-courthouse
-Yugoslav
-bush
-trawler
-shellflower
-jade vine
-ragged orchid
-pea soup
-King Charles spaniel
-hubcap
-snook
-paddy
-bow and arrow
-shovel
-dill
-cliff swallow
-cadaver
-hijab
-masterpiece
-fish geranium
-kettle
-sanitary napkin
-carrot stick
-Mountie
-peanut brittle
-dam
-jackal
-windowsill
-butterfly orchid
-bodice
-picador
-pale yellow
-beanie
-petiole
-tenor saxophonist
-bungalow
-gnomon
-stock saddle
-field glass
-rigging
-wood grain
-Speaker
-settlement house
-swamp milkweed
-paper nautilus
-tangerine
-champagne
-crescent roll
-library
-Schmidt telescope
-stemless carline thistle
-motorcyclist
-alpine ash
-planchet
-water closet
-casuist
-hand luggage
-hyssop
-spaghetti and meatballs
-cannelloni
-cedar waxwing
-water dog
-brick red
-linkage
-sweep hand
-purple heather
-macaroni and cheese
-butter knife
-refreshment
-malt
-St. Augustine grass
-wainscot
-compass
-gas heater
-tamale
-table saw
-referee
-borsch
-projector
-dracaena
-peppermint
-Reuben
-Abyssinian banana
-glassblower
-floss
-small stores
-artilleryman
-lapwing
-ranch
-garbage man
-dwarf banana
-commelina
-currant
-adulteress
-landlocked salmon
-pasqueflower
-nan
-tiger lily
-Eritrean
-rotunda
-catsup bottle
-mezzanine
-royal fern
-blended whiskey
-bowler hat
-mistletoe
-manor
-fusee drive
-pistachio
-dispensary
-swamp
-amputee
-sculptor
-schoolmaster
-Chinese anise
-dwarf iris
-livestock
-chronograph
-nectarine
-jockey
-plaster
-motel room
-swamp azalea
-hippeastrum
-space station
-duchess
-catacomb
-dovetail
-cockscomb
-common spotted orchid
-brittlebush
-cleats
-cloche
-hotchpotch
-cabin car
-prey
-indigo
-light beer
-bear's breech
-jonquil
-analyzer
-alyssum
-spur gear
-ice tea
-honey buzzard
-twayblade
-dirndl
-atlas moth
-croquette
-carafe
-flyweight
-professional basketball
-multivitamin
-air terminal
-phial
-roll-on
-skunk cabbage
-bird of paradise
-rose
-cooter
-camping
-divided highway
-herbage
-sweet vermouth
-common comfrey
-eggplant
-office building
-glutton
-gefilte fish
-bicycle rack
-swamp birch
-Venetian blind
-Pernod
-Norway spruce
-portrait camera
-bastion
-vitamin Bc
-Ugandan
-Indian red
-okapi
-emu
-vin ordinaire
-chintz
-shrimp cocktail
-numbat
-tall oat grass
-cable car
-stopcock
-ham sandwich
-Yemeni
-stanhopea
-plate
-chicken broth
-common yellowthroat
-California poppy
-radio
-chocolate egg
-mess jacket
-tea table
-physostegia
-Japanese flowering cherry
-confectionery
-chicken cacciatore
-painted nettle
-popover
-white rice
-strapless
-mohair
-electrical cable
-coil spring
-arterial road
-miniature fan palm
-spectator pump
-pesto
-interlocutor
-eastern kingbird
-dongle
-vitamin B6
-stuffed tomato
-cough drop
-okra
-black
-barbecue
-burial mound
-firstborn
-corn snake
-amberjack
-bollard
-horn
-Black African
-elbow pad
-Camembert
-circle
-Japanese apricot
-hearing aid
-rock star
-creature
-taster
-bubble gum
-scull
-lemon balm
-chaetodon
-anemometer
-brake drum
-fuselage
-courthouse
-aqualung
-yellow adder's tongue
-reception desk
-guy
-buffalo wing
-ginger beer
-robin
-pantothenic acid
-marsh hawk
-yellow journalism
-exhaust
-cardamom
-Tabasco
-ax handle
-patriarch
-floor
-pine snake
-spoiler
-hood
-sphagnum
-parrotfish
-orphanage
-redpoll
-beef Wellington
-white spruce
-cherry plum
-scapular
-field lens
-broomstick
-mouser
-wood thrush
-Nebraskan
-hotelier
-milk thistle
-soya milk
-Munich beer
-boucle
-snowy egret
-dust storm
-steward
-kudzu
-oriental poppy
-presbytery
-burro
-orange soda
-stonecrop
-splashboard
-menagerie
-dormer
-wire cutter
-yellow bells
-Dubliner
-shore pine
-cousin
-racing gig
-Morgan
-gold plate
-villager
-snifter
-granny's bonnets
-egg roll
-Spode
-amabilis fir
-babbler
-pestle
-heliopsis
-halter
-black spruce
-President of the United States
-ski slope
-chocolate fondue
-lockstitch
-motel
-Epipactis helleborine
-tabbouleh
-Yorkshire pudding
-overpass
-Timorese
-presbyter
-tablefork
-bottle gourd
-tiara
-vintage
-pilgrim
-reindeer moss
-shower stall
-towel rack
-kachina
-chef's salad
-breeder
-cow parsnip
-walker
-Black woman
-Irish coffee
-portrait lens
-lateen
-gilt
-successor
-cargo container
-Lithuanian
-mayapple
-paisley
-highchair
-strawberry jam
-flying fox
-field scabious
-blue-eyed grass
-screw
-Frisbee
-dressing room
-cholla
-walkie-talkie
-red currant
-centrifugal pump
-smorgasbord
-hot rod
-marcher
-rowanberry
-welwitschia
-amphitheater
-pew
-concert band
-bosom
-pillbox
-seagrass
-openwork
-meadow goldenrod
-shower
-chicken sandwich
-Boston ivy
-plastron
-oilfield
-stuffed tomato
-juniper berries
-frame
-Spanish mackerel
-family room
-powder horn
-fight
-maguey
-bunker
-work-shirt
-air filter
-nosh
-sugar bowl
-foothill
-reliquary
-tugboat
-horsebox
-grater
-palace
-board member
-campsite
-halibut
-geneva
-ginger ale
-high commissioner
-genet
-bodywork
-spaghetti
-protractor
-pipe cutter
-wood anemone
-turkey cock
-surge suppressor
-green turtle
-spoiler
-bedsitting room
-television room
-ballot box
-shasta daisy
-impeller
-capote
-bitter
-California wine
-lock
-spinnaker
-gill fungus
-baby's breath
-nut and bolt
-moonflower
-houseboat
-distributor cap
-coffee bean
-gusset
-bowling ball
-knitwear
-frieze
-mistflower
-roadster
-cue
-circuitry
-brake
-butt hinge
-Chickasaw
-leopard frog
-wing tip
-puree
-mantel
-pantheon
-grandfather clock
-cockchafer
-pomegranate
-cleaners
-eyeshadow
-Oregon cedar
-rock hopper
-hawksbill turtle
-agriculturist
-yellow-crowned night heron
-Albanian
-pumpkin seed
-chateau
-goggles
-camper trailer
-bracket fungus
-cigarette case
-signal box
-saddle blanket
-poison ivy
-set gun
-cattleya
-dry fly
-concert hall
-personal digital assistant
-talcum
-deodorant
-common starling
-painted turtle
-kea
-plenipotentiary
-pantyhose
-masjid
-buskin
-hurdle
-cocktail lounge
-belting
-sour dock
-knife blade
-sugar snap pea
-paddle
-dickeybird
-brace
-keep
-call center
-yacht
-lead pencil
-tumbler
-production line
-tetra
-private
-French window
-express
-ski boot
-pinto
-broad bean
-American crow
-screech owl
-snapper
-power cord
-Manx
-rambutan
-sun deck
-stonefish
-golden eagle
-national monument
-readout
-cork oak
-hacksaw
-beer can
-bathe
-tussock bellflower
-wet suit
-mihrab
-big game
-highlighter
-sprocket
-measuring worm
-grapefruit
-samovar
-distributor point
-steak knife
-incubator
-loon
-temporary hookup
-hippodrome
-hot spring
-spacesuit
-flea market
-clay pigeon
-catbird
-earmuff
-tetherball
-yellowfin
-cellophane
-lanolin
-clapperboard
-velveteen
-police dog
-cashew
-sequencer
-mango
-duplex house
-bazaar
-Golden Delicious
-red carpet
-collet
-kickstand
-broadloom
-diskette
-tank engine
-compact
-diesel-electric locomotive
-whale shark
-water moccasin
-mountain avens
-tropic bird
-ginkgo
-ski cap
-fixative
-glockenspiel
-chopine
-ethernet
-herring gull
-skeleton key
-finger paint
-conference table
-great crested grebe
-harbor
-white-crowned sparrow
-Bullock's oriole
-guestroom
-boutique
-cable television
-roulette wheel
-Luger
-Latin American
-trumpeter
-blindfold
-baby
-freshwater bass
-home plate
-bonefish
-giant sunflower
-giant tortoise
-planking
-pigeon hawk
-oceanfront
-door
-bazaar
-common wasp
-conformation
-kick starter
-kid glove
-corydalis
-shuttlecock
-writing desk
-ivory gull
-shirttail
-diving suit
-weka
-downy birch
-altar
-wild sage
-tufted puffin
-cabinet
-Orpington
-cineraria
-bottom
-dial
-coracle
-resort hotel
-soap dish
-spotted owl
-billiard room
-ghetto blaster
-red-breasted nuthatch
-hatchling
-chalet
-bracteole
-crusher
-mixer
-net melon
-farmhouse
-Dutch oven
-transept
-penlight
-palmyra
-stewing pan
-solar cell
-crochet needle
-black-winged stilt
-germander speedwell
-crinkleroot
-truncheon
-bunchberry
-hatchback
-sounding board
-mixing faucet
-chess master
-bisque
-Brie
-Sitka spruce
-pawn
-Mexican-American
-space rocket
-choreographer
-collared peccary
-duffel
-nacho
-patchcord
-carpet snake
-omnivore
-watering can
-hall of residence
-streamer fly
-sunroof
-great grandson
-oil refinery
-billiard player
-ivy geranium
-key palm
-pinwheel
-yellow-shafted flicker
-purple onion
-soldering iron
-condominium
-fishing gear
-heat pump
-marine iguana
-cuckoo clock
-Bletilla striata
-headrest
-spotted salamander
-field hockey ball
-pound
-carboy
-vertical stabilizer
-groundsheet
-cinnamon bread
-acorn squash
-sheathing
-lakefront
-Jeffrey pine
-synthesizer
-olive
-apple
-pannier
-ponderosa
-Jew's-ear
-latch
-equatorial
-metasequoia
-permit
-bloomers
-town hall
-fava bean
-casino
-bier
-jampot
-common snapping turtle
-clary sage
-oatmeal
-Dutchman's breeches
-massif
-Guyanese
-heifer
-handball
-sweat suit
-pomelo
-Iceland moss
-customhouse
-sandbag
-archer
-gyrfalcon
-sword cane
-marmite
-whole snipe
-blue crab
-sugar spoon
-brownstone
-chicken wire
-lizardfish
-dump truck
-chicken yard
-chamois
-electric
-idle pulley
-jujube
-wrestling mat
-aoudad
-Burmese cat
-water shamrock
-dormitory
-Unknown Soldier
-hearse
-bumper
-clipper
-desert pea
-critter
-semitrailer
-backboard
-common St John's wort
-Atlantic manta
-song thrush
-jukebox
-quoin
-eastern chipmunk
-copper beech
-paintball gun
-bull
-package store
-fraise
-royal poinciana
-niqab
-traction engine
-objective
-day nursery
-ski lodge
-orphan
-summer house
-cereal box
-router
-sleuth
-jodhpur
-polyp
-croquet
-sport kite
-green onion
-tulle
-etagere
-tussock caterpillar
-rest house
-elderberry
-bridal wreath
-Torrey pine
-silver wattle
-kidney bean
-pentode
-laelia
-Allen wrench
-sporran
-red drum
-tricot
-heterodyne receiver
-magazine rack
-stone curlew
-trawler
-suckling
-niblick
-sandwich plate
-double door
-Togolese
-pitching wedge
-desert tortoise
-cloth cap
-date palm
-webbing
-jumper
-frogmouth
-copperhead
-covered couch
-black mallee
-riser
-scraper
-gauntlet
-pantheon
-food court
-muntjac
-grocery bag
-bread-bin
-transmission shaft
-primigravida
-window seat
-crab apple
-seat
-Fresnel lens
-dendrobium
-hatchback
-little theater
-butter dish
-back porch
-umbrella tree
-carrot
-seventy-eight
-coconut
-music stool
-Tesla coil
-bay willow
-American basswood
-sabot
-wheel and axle
-gazette
-lute
-bassinet
-hart
-mecca
-breadbasket
-silverfish
-handball
-Scotch pine
-box camera
-stately home
-Hereford
-tread
-single-breasted jacket
-desk phone
-deodar
-professional boxing
-fly casting
-box wrench
-black oak
-martello tower
-red campion
-bullock
-sweet William
-bay leaf
-dollhouse
-flounder
-fox hunting
-beanbag
-king mackerel
-rouge
-film advance
-common mallow
-parasitic jaeger
-satellite receiver
-nurse shark
-chesterfield
-tomatillo
-plimsoll
-hatbox
-bloomer
-foul-weather gear
-longleaf pine
-horse mackerel
-tree lizard
-bark
-belfry
-Treasury
-perch
-purple finch
-stag beetle
-fragrant orchid
-tachymeter
-tadpole
-cookie jar
-knee piece
-agueweed
-bones
-chick
-golf glove
-toothpick
-taboret
-rotor blade
-field artillery
-purple willow
-redhead
-spark plug
-guava
-voice mail
-cross
-butterfly valve
-star magnolia
-olive
-room light
-Australian turtledove
-embassy
-Iraqi
-singles
-nestling
-spinning rod
-radial engine
-rowan
-sandbox
-boss
-moccasin flower
-veneer
-mint
-American chestnut
-white whale
-CPU board
-florist
-press box
-hurricane lamp
-giant kangaroo
-greater whitethroat
-winter jasmine
-blue
-department store
-southern red oak
-saber saw
-corn muffin
-bellbottom trousers
-toaster oven
-red eft
-condominium
-galago
-sunbather
-redpoll
-common European earwig
-songbird
-linnet
-light meter
-bracer
-tepee
-gumbo
-water glass
-roofing
-spathiphyllum
-shofar
-sand lizard
-washroom
-Brussels carpet
-brachyuran
-home room
-floatplane
-knee brace
-solar heater
-felucca
-gas ring
-maguey
-manse
-blue columbine
-cuppa
-cigar band
-male orchis
-mudskipper
-couscous
-Chinese parasol tree
-dude ranch
-banyan
-gopher snake
-sundrops
-aviary
-African daisy
-missel thrush
-Photostat
-stone pine
-circus tent
-tangle
-printer cable
-grease-gun
-rose chafer
-light pen
-plantain
-hearth
-bullfinch
-post oak
-slow loris
-Newtonian telescope
-head
-punt
-spindle
-New England aster
-spotted sandpiper
-pond pine
-grass skirt
-bug
-black rat snake
-tabasco
-bull shark
-tennis camp
-scrambler
-popinjay
-bing cherry
-ministry
-cash register
-redheaded woodpecker
-kameez
-farmer's market
-roan
-harpy
-European toad
-pizzeria
-camshaft
-hemp nettle
-chicken coop
-cottage pink
-daybed
-observatory
-airdock
-mountain devil
-newsstand
-kingfish
-snow gum
-jackdaw
-lacquerware
-peeler
-miro
-sister ship
-damask rose
-pack
-snowshoe
-Liberian
-paramecium
-tidytips
-professional tennis
-bookend
-wood swallow
-cayuse
-cranberry
-rock squirrel
-steak au poivre
-soul patch
-female mammal
-sash fastener
-songwriter
-oxeye daisy
-apse
-floor joist
-hand towel
-wheatear
-cero
-soul mate
-golden fig
-bus stop
-psycholinguist
-convenience store
-manor hall
-mountain sandwort
-Euopean hoopoe
-haricot vert
-mausoleum
-violist
-flashlight battery
-chard
-fixer-upper
-bank martin
-testudo
-diving duck
-kohlrabi
-Omani
-sphygmomanometer
-greyhound racing
-chestnut
-rattlesnake plantain
-chaffinch
-wolf pup
-teakettle
-cairn
-souk
-resident commissioner
-chuckwalla
-gaiter
-capercaillie
-liver chestnut
-bean sprout
-land line
-ambassador
-green pepper
-common chickweed
-Sharpie
-Oriental arborvitae
-oncidium
-pallone
-currawong
-sweet alyssum
-fire tower
-eyebrow pencil
-redfish
-apricot
-clementine
-blucher
-wigwam
-pangolin
-buggy
-common oak
-jumbojet
-laser
-cigarette holder
-racquetball
-georgette
-cleft
-scouring pad
-drum printer
-pond scum
-American red squirrel
-caranday
-swamp willow
-blindworm
-brook trout
-defense system
-nyala
-three-way calling
-mizzen
-shuttle
-African lily
-Oregon white oak
-rain tree
-fuel gauge
-oriental cherry
-wahoo
-pear
-jungle gym
-bass fiddle
-outrigger
-angelfish
-Old World coot
-lime
-battlement
-yarmulke
-herpes varicella zoster
-burp gun
-Alpine glacier
-stun gun
-pilot boat
-Southern crab apple
-bushtit
-pullet
-polo pony
-jackfruit
-raw vegetable
-French marigold
-golden shower tree
-spike lavender
-wahoo
-brass knucks
-cabbage palm
-diesel-hydraulic locomotive
-red jungle fowl
-prairie sunflower
-rye
-loofa
-icecap
-shade tree
-secretary bird
-saffron
-cos
-muskrat
-videodisk
-Carolina wren
-candy bar
-Bohemian waxwing
-flowering almond
-cold frame
-raglan
-pine siskin
-quince
-western red cedar
-red maple
-adobe
-agora
-kumquat
-tenement
-bantam
-bayberry
-water jump
-great granddaughter
-snips
-porcupinefish
-brochette
-love-in-a-mist
-Iceland poppy
-common sage
-pace car
-camel racing
-slipcover
-nopal
-shoehorn
-calypso
-rhea
-in-basket
-maple syrup
-cold chisel
-Pacific ridley
-dietary
-aperture
-lapin
-rock hyrax
-house wren
-litchi
-ragged robin
-control center
-shoebox
-arabesque
-eider
-silver birch
-bantamweight
-ax head
-softball
-blue gum
-Bechtel crab
-tomato sauce
-green douglas fir
-sweet gum
-macaroni salad
-red phalarope
-budgerigar
-Bedford cord
-Uzi
-green woodpecker
-ohmmeter
-bacon-lettuce-tomato sandwich
-hackney
-Easter egg
-motmot
-red pine
-opium poppy
-gat
-pussy willow
-greater scaup
-ocelot
-persimmon
-western hemlock
-carambola
-pinion
-Malcolm stock
-bobsled
-larkspur
-wood drake
-pinetum
-red gum
-draft beer
-funnel
-terrarium
-Pinot blanc
-doodlebug
-brittle star
-salsa
-cantaloup
-pollack
-stockpot
-eastern hemlock
-rock wren
-burqa
-squash
-aircraft engine
-billy
-flamingo flower
-odontoglossum
-old squaw
-redstart
-sheepskin coat
-mate
-flathead catfish
-gentianella
-bilberry
-bog rein orchid
-incense cedar
-mew
-Colorado spruce
-cob
-portmanteau
-grenadine
-common ginger
-masdevallia
-compound microscope
-sobralia
-white fungus
-guppy
-chapterhouse
-honey
-green frog
-sea swallow
-African marigold
-astrolabe
-verdigris
-yellowhammer
-carrot juice
-oxlip
-medicine ball
-highboy
-grass frog
-gamebag
-surgery
-mincer
-mulloway
-cactus wren
-box office
-resonator
-table-mountain pine
-European curlew
-supernova
-cabbageworm
-peach
-plane seat
-asp
-Yquem
-tomato hornworm
-rook
-quadruped
-chador
-micrometer
-dabchick
-Afro-wig
-balsam fir
-bucket seat
-sage green
-macon
-blue poppy
-chinquapin oak
-black pine
-spinach
-chrysalis
-carnauba
-tee
-bearberry
-shirt button
-tree of heaven
-southern white cedar
-covered wagon
-brood hen
-spadix
-European catfish
-winter wren
-bulldog clip
-carpetbag
-study hall
-chino
-simian
-closeup lens
-cookie cutter
-grapefruit
-mandola
-sassaby
-Allegheny plum
-piaffe
-scorpion fly
-booby
-draft animal
-field tent
-cumin
-laurel oak
-smooth-leaved elm
-American arborvitae
-American toad
-grinding wheel
-mountain ash
-cuttlefish
-pipistrelle
-parer
-safety rail
-Clark's nutcracker
-side-blotched lizard
-giant hornet
-wicket
-dugout
-electric toothbrush
-dhow
-common four-o'clock
-long-eared owl
-anchor
-near beer
-tansy
-creme caramel
-guided missile frigate
-shelduck
-durian
-compact
-iron tree
-shiitake
-polo
-camouflage
-pedal pusher
-salon
-tangerine
-lacebark
-Swiss mountain pine
-goalpost
-poolroom
-space capsule
-wild cherry
-dress hat
-wave
-raglan sleeve
-cassia
-Jerusalem artichoke
-cabbage palmetto
-marsh harrier
-American redstart
-sea squirt
-cliff diving
-sparrow hawk
-watch cap
-frankfurter bun
-police boat
-flash camera
-neem
-eastern meadowlark
-Italian cypress
-orb-weaving spider
-graniteware
-sewing basket
-latex paint
-rock dove
-stator
-leaf lettuce
-roulette
-broadcloth
-Spork
-panicle
-sternwheeler
-cider vinegar
-brown creeper
-cowfish
-closed gentian
-chickpea
-port
-pimento
-sheeting
-matilija poppy
-hawk owl
-guava
-papaya
-huisache
-European shrike
-racing skiff
-yellow warbler
-gumbo-limbo
-North Carolinian
-staysail
-court
-iced coffee
-money belt
-shaver
-Psychopsis papilio
-sumo ring
-refection
-kingfish
-clock pendulum
-greater butterfly orchid
-disk harrow
-tawny eagle
-polyphemus moth
-pieplant
-Nicaraguan
-bocce ball
-California box elder
-porbeagle
-crown of thorns
-Mexican sunflower
-fennel
-stream orchid
-slip ring
-white fir
-fold
-moss campion
-fairy ring
-hose
-pony-trekking
-western larch
-meadow pipit
-Cape May warbler
-longan
-bookmobile
-junk shop
-lemon shark
-smelling bottle
-solan
-widow
-sea pen
-universal joint
-day game
-goldcrest
-maiden pink
-biographer
-rotunda
-oriel
-arranger
-gambrel
-Angora
-fen orchid
-leading rein
-Wilson's snipe
-European nuthatch
-natterjack
-athletic supporter
-mouflon
-emergency room
-swallow-tailed coat
-western meadowlark
-feather star
-Navy SEAL
-toilet bag
-loquat
-lesser butterfly orchid
-thumbhole
-breathalyzer
-featherweight
-collards
-mayfly
-confessional
-mountain ebony
-redwing
-Norway maple
-refractometer
-stagecoach
-gasoline gauge
-octopus
-baker
-Rhode Island red
-European tortoise
-cardiologist
-Punjabi
-Arkansas kingbird
-tamarind
-drum brake
-flash
-yellowtail
-stokes' aster
-emperor
-free house
-sour gum
-ruddy duck
-hamadryad
-command module
-tinamou
-Norway lobster
-washstand
-European hornbeam
-roaster
-black-necked grebe
-tallgrass
-leopard lizard
-anastigmat
-Blackburn
-deutzia
-ground rattler
-Christmas fern
-wild pink
-sesame seed
-carrycot
-Italian parsley
-nectar
-roll-on roll-off
-true laurel
-anisette
-candy corn
-flowering maple
-revers
-dun
-tobacco hornworm
-common sunflower
-common grape hyacinth
-cardiograph
-electric meter
-herb Paris
-goalmouth
-spruce grouse
-canopy
-wind poppy
-stemma
-gateleg table
-lumper
-speckled rattlesnake
-gudgeon
-rough-legged hawk
-internal drive
-pomelo
-piece de resistance
-storm door
-clementine
-Japanese pink
-settler
-yellow jacket
-Fraser fir
-royal palm
-cicada killer
-cayenne
-guava
-bluewing
-red baneberry
-lesser yellowlegs
-cache
-bog rose
-sparring partner
-ski jumping
-sherry
-glacier lily
-beer mat
-shredder
-American widgeon
-protectionist
-green olive
-black-tailed deer
-Alpine fir
-dispatch case
-whipping cream
-African daisy
-cantilever bridge
-maraschino
-rhea
-ink bottle
-dacha
-hagberry tree
-lesser rorqual
-orchard oriole
-candidate
-cuticle
-breadfruit
-fishbowl
-giant puffball
-closed gentian
-Joshua tree
-tie rod
-beard lichen
-flame tree
-stegosaur
-acerola
-Swan River daisy
-common murre
-flowering almond
-protegee
-loggerhead shrike
-Wilson's warbler
-Japanese honeysuckle
-basilisk
-skimmer
-hybrid tuberous begonia
-pumpkin ash
-chafing dish
-collared lizard
-iced-tea spoon
-scrubbird
-Iceland poppy
-grey kingbird
-wallflower
-slick
-diesel
-Swiss pine
-ethernet cable
-ketch
-lightship
-black cherry
-swordtail
-Monterey cypress
-lightweight
-Floridian
-Sabine
-stall
-contact
-viola da gamba
-hemstitch
-upland sandpiper
-box spring
-sassafras
-radome
-lesser scaup
-bluefin
-yellow-bellied sapsucker
-armored car
-cabin class
-Moorish arch
-webcam
-aquavit
-overall
-sergeant major
-soft shield fern
-gin and it
-bobolink
-subcompact
-falconer
-black morel
-roadrunner
-lab bench
-thong
-coffee urn
-weeping beech
-caladenia
-southern live oak
-scanner
-wine vinegar
-common speedwell
-European roller
-fuji
-snag
-piping plover
-concertina
-secateurs
-meat thermometer
-supercomputer
-funnel
-dais
-western fence lizard
-spruce pine
-pommel horse
-Cassegrainian telescope
-pitta
-India-rubber tree
-mangosteen
-tamp
-aposematic coloration
-dustcloth
-birth
-Atlas cedar
-reed bunting
-jabiru
-sainfoin
-press photographer
-golden oriole
-laryngoscope
-thermal printer
-winder
-doubles
-cricket ball
-dabbling duck
-tonic
-Buddhist
-Morris chair
-swatter
-quaking aspen
-ancient pine
-American larch
-evaporative cooler
-click beetle
-yellow-breasted chat
-souchong
-bluegill
-pied-billed grebe
-tricorn
-spring beauty
-southern magnolia
-rowel
-chili
-hard roll
-flathead
-satsuma
-gangplank
-bourguignon
-cockfighting
-greenwing
-plum tomato
-fly orchid
-gnatcatcher
-spotted eagle ray
-ovenbird
-brassavola
-mocha
-candy cane
-afterburner
-thriftshop
-study
-winter crookneck
-grinder
-muskellunge
-sacred ibis
-inverter
-sandwort
-deer fern
-stair-carpet
-Cotes de Provence
-ovenbird
-rex begonia
-American woodcock
-poison ash
-lowland fir
-pawpaw
-loblolly pine
-kinkajou
-European hackberry
-pest
-coralwood
-Bedouin
-acetate rayon
-snuffbox
-radiator cap
-basket oak
-table-tennis racquet
-smew
-midge
-telescopic sight
-radish
-great burdock
-separate
-damask violet
-broadbill
-bourbon
-blacktip shark
-gift shop
-khimar
-date
-woodland caribou
-policeman bird
-grey birch
-American elm
-strawflower
-officiant
-hart's-tongue
-straight razor
-Spanish elm
-radicchio
-white croaker
-vicuna
-soft-shell clam
-flannel
-adonis
-bonito
-kittiwake
-English walnut
-soldierfish
-hipflask
-spotted crake
-Streptopelia turtur
-American maidenhair fern
-corn cockle
-telephone cord
-canopy
-playback
-diocesan
-marsh orchid
-manakin
-purple grackle
-cob
-fishmonger
-otoscope
-vermillion flycatcher
-inhaler
-instar
-licentiate
-myrtle warbler
-goat herder
-benthos
-toggle
-drumhead
-piranha
-doorplate
-vault
-triptych
-red-necked grebe
-transporter
-vernier caliper
-flathead
-Portuguese man-of-war
-countrywoman
-vacation home
-Bactrian camel
-night-light
-module
-lemon curd
-carancha
-painted daisy
-bok choy
-ratatouille
-troll
-escarpment
-cinnabar
-computerized axial tomography scanner
-lychgate
-sowbread
-bedside
-guided missile cruiser
-reel
-cleat
-hemostat
-blue shark
-Seven Wonders of the Ancient World
-motorized wheelchair
-pillow block
-horned puffin
-prickly pear
-electric range
-mother's daughter
-vein
-Oregon maple
-bird dog
-faceplate
-wren warbler
-feather reed grass
-common alder
-Adam's needle
-straitjacket
-organ-grinder
-gantry
-bikini pants
-peristyle
-herpes
-terry
-toad lily
-celandine
-red-breasted sapsucker
-bragger
-green peafowl
-fuschia
-quoits
-house martin
-dome
-herpes simplex 1
-touraco
-meeting house
-vacuum gauge
-cat's-ear
-crisphead lettuce
-carpet moth
-European rabbit
-puff adder
-Old World scops owl
-fire pink
-fruit punch
-ant bear
-black walnut
-stroboscope
-white mangrove
-pine grosbeak
-cast
-check-in
-ring-necked parakeet
-matai
-shingle oak
-fieldwork
-rue anemone
-landing net
-ouzo
-herringbone
-lyceum
-hydrogen bomb
-mullein pink
-masher
-evening grosbeak
-water vole
-livingstone daisy
-tomatillo
-cavalier hat
-interphone
-wild lupine
-goosefish
-sugar maple
-plantain
-white dead nettle
-Monterey pine
-bugle
-veloute
-marsh gentian
-Bermuda buttercup
-alehouse
-Peter Pan
-thong
-LP
-tulip tree
-scanner
-scarlet tanager
-music hall
-angel shark
-pecan
-eight ball
-rosy boa
-outboard motorboat
-garage
-fanlight
-black cottonwood
-notornis
-mountain fern
-lunar crater
-reddish orange
-whitetip shark
-executant
-European ladies' tresses
-washboard
-revolving door
-case knife
-balloonfish
-greater kudu
-tarpan
-cog
-wet fly
-Irish soda bread
-basement
-broken arch
-canopic jar
-muscat
-kazoo
-bobsledding
-loaner
-black guillemot
-English saddle
-garlic mustard
-Foucault pendulum
-mulberry
-clotted cream
-dove's foot geranium
-Atlantic ridley
-convector
-ground floor
-European wildcat
-poinsettia
-hideaway
-great barracuda
-black beech
-bushy aster
-cornflower
-tam
-true slime mold
-carving knife
-holly fern
-railroad tunnel
-crimson clover
-disposal
-etamine
-suspension
-plasmodium
-political scientist
-minnow
-Spanish rice
-twist bit
-subway train
-Scleroderma citrinum
-saw palmetto
-console
-gimlet
-hand pump
-waratah
-rock rattlesnake
-keel
-server
-curlew sandpiper
-hone
-sable antelope
-inkle
-photostat
-foresail
-sallet
-tiger salamander
-chutney
-onlooker
-Exmoor
-tiramisu
-drawing room
-battery
-sour orange
-juniper berry
-beeper
-funeral home
-fescue
-Maksutov telescope
-ranch house
-jai alai
-carob
-socket
-popcorn
-sandbar shark
-pipal
-summer tanager
-oast
-skipjack
-rolling stock
-dropper
-great snipe
-turnip greens
-cowpea
-honeycomb
-ichneumon fly
-maternity hospital
-harp seal
-nylon
-bomb shelter
-horse tick
-litchi
-camel's hair
-mimosa
-bur oak
-anvil
-belay
-pinhead
-continental breakfast
-burglar alarm
-Mojave rattlesnake
-auxiliary storage
-lightwood
-ratepayer
-cecropia
-retractor
-quadrate
-pepper tree
-Venus' slipper
-abattoir
-strawflower
-firewater
-purple saxifrage
-black rat
-pack
-pepper pot
-mayweed
-winger
-whitetip shark
-great yellow gentian
-snowdrop anemone
-garden angelica
-soy sauce
-white poplar
-inkwell
-crouton
-gas gun
-honey locust
-house of cards
-ice maker
-moquette
-arrack
-casualty
-butterfly orchid
-eau de vie
-mosquitofish
-prairie smoke
-haft
-horseshoe
-steel
-peach orchard
-Mexican hat
-encaustic
-shoe
-pennywhistle
-sweet woodruff
-hull
-doorsill
-globe amaranth
-day school
-housedog
-crown princess
-oxbow
-maxi
-positron emission tomography scanner
-compere
-European turkey oak
-peanut
-sentry box
-house physician
-hot line
-loquat
-rove beetle
-riband
-flowering fern
-fan vaulting
-ceibo
-bongo
-bat boy
-omelet pan
-European ash
-breadwinner
-gaff topsail
-clerestory
-bushbuck
-bluethroat
-khukuri
-Father
-portcullis
-candy egg
-brake lining
-lawn furniture
-buckskin
-garden pea
-Brazilian rosewood
-Italian bread
-horn poppy
-silk tree
-Christmasberry
-hotel-casino
-poplin
-false lupine
-desert sunflower
-mimeograph
-alpenstock
-cork tree
-cultivar
-common mosquito
-pollard
-black marlin
-understudy
-lancet window
-college
-breadfruit
-Herero
-Labourite
-bar printer
-squaw grass
-stelis
-firing chamber
-sycamore
-artificial horizon
-radiologist
-pansy orchid
-bicycle pump
-wraparound
-bell gable
-home computer
-orchard grass
-carving fork
-bergamot
-honeycreeper
-sewing room
-radiator
-core
-brown bat
-goose grass
-adjutant general
-Erlenmeyer flask
-massasauga
-tail rotor
-cardinal tetra
-Drambuie
-wine palm
-Sarcoscypha coccinea
-shantung
-Calvados
-garganey
-vicar
-house mouse
-creeping oxalis
-digital subscriber line
-cedar elm
-backgammon board
-blackberry-lily
-pallid bat
-New Zealander
-Barbadian
-rose geranium
-European spider crab
-gharry
-electric hammer
-mustard
-Chinese lantern
-laundry cart
-filament
-mozzarella
-gooseberry
-sukiyaki
-porkpie
-culvert
-altazimuth
-plum pudding
-serin
-Spanish dagger
-Asian crocodile
-crevalle jack
-mascara
-pig bed
-alderman
-northern shrike
-Sufi
-purple-fringed orchid
-derringer
-linseed
-hockey skate
-bell jar
-Japanese wistaria
-mantled ground squirrel
-western toad
-lieutenant commander
-mechanical piano
-ovoid
-paddlefish
-demijohn
-coast live oak
-brick
-gearset
-tailstock
-phonograph needle
-winery
-tuberose
-mother's boy
-shot tower
-crucian carp
-carpet pad
-lamb's-quarter
-Menorah
-common white dogwood
-hypanthium
-rosebay
-wild medlar
-soil horizon
-sweet orange
-bitterroot
-hand glass
-cloisonne
-towpath
-gum ball
-margay
-carambola
-bolt cutter
-charger
-vibraphone
-gueridon
-elephant tree
-wood-frog
-ash grey
-duffel coat
-third base
-chunga
-glebe house
-lake trout
-encephalartos
-Japanese oak
-northern red oak
-pruner
-blue orchid
-Biloxi
-western wood pewee
-corselet
-alabaster
-anechoic chamber
-grass pink
-wax begonia
-blue daisy
-pennyroyal
-Asian tiger mosquito
-cheese souffle
-flat bench
-caramel
-sump pump
-bush violet
-common fennel
-corner
-skullcap
-asparagus fern
-white mangrove
-calceolaria
-sateen
-saltbox
-hollowware
-head nurse
-coal miner
-mountain lily
-tufted vetch
-European perch
-line officer
-steamer
-stickball
-shin guard
-cauliflower
-Monegasque
-hatpin
-wolffish
-trackball
-khaki
-arthrogram
-rocket larkspur
-naval commander
-Gemini
-ski binding
-department head
-Chenin blanc
-wingstem
-knothole
-aerides
-sweet bay
-tautog
-gangway
-waterspout
-Hudsonian godwit
-armyworm
-incinerator
-kidney vetch
-pine nut
-cypress vine
-hip tile
-sorrel tree
-relay
-bench press
-Kentucky coffee tree
-dobson
-sapling
-false lily of the valley
-veld
-phaius
-vitamin B2
-beaker
-wall tent
-sieva bean
-dusty miller
-sewing kit
-cavalry horse
-diaper
-butterfly pea
-Spam
-saddlebill
-pearly everlasting
-kowhai
-Sister
-moneywort
-organdy
-pine marten
-bareboat
-hot-water bottle
-baby blue-eyes
-silver lime
-common cotton grass
-malmsey
-blue pea
-baggage car
-pineapple
-folding saw
-cotton rose
-brawler
-black duck
-Weizenbock
-pool player
-Gujarati
-wild duck
-purple sage
-sage grouse
-mail train
-arm guard
-short-spurred fragrant orchid
-queen
-eparchy
-spring peeper
-ortolan
-shoulder
-fighter pilot
-American beech
-snowcap
-novitiate
-roller
-butcherbird
-canyon oak
-brompton stock
-firebrick
-rudder
-light cream
-Primus stove
-nonsmoker
-probationer
-harp
-kosher
-surcoat
-videotape
-zebu
-first class
-yam
-car
-rissole
-miso
-funambulism
-attic
-curling iron
-shutter
-encolure
-split-pea soup
-yellow rocket
-gas oven
-ultracentrifuge
-chamomile
-canteen
-eyeliner
-yellow squash
-Irish stew
-collar
-doublet
-machinist
-septic tank
-snap bean
-Polyporus squamosus
-western tanager
-creeping St John's wort
-back
-sinkhole
-perforation
-Romanian
-epergne
-fez
-comfrey
-sidecar
-beach pea
-screen door
-instigator
-plughole
-woodbine
-pigweed
-hip pocket
-common scoter
-squeegee
-Surinam cherry
-porringer
-body stocking
-eatage
-shallot
-enlarger
-common canary
-trophy case
-gun case
-plow horse
-hot plate
-pearl oyster
-margarita
-madras
-backspace key
-pigeon guillemot
-pajama
-buckthorn berry
-homestead
-bedbug
-Linotype
-trundle bed
-granadilla
-theremin
-chin rest
-bouillabaisse
-tumble-dryer
-truffle
-cassava
-kurrajong
-gyroscope
-European silver fir
-C-clamp
-politician
-green soybean
-exponent
-flame tree
-scissortail
-achimenes
-crown daisy
-soft tree fern
-spaghetti squash
-pale violet
-beaver
-dashiki
-washboard
-driving wheel
-sack
-foulard
-sputnik
-boatbill
-English elm
-sack coat
-grog
-golliwog
-Malayan tapir
-May wine
-calash
-stile
-windjammer
-American sycamore
-rotor head
-fast food
-balata
-dragonet
-Emmenthal
-metronome
-negative
-meadow saxifrage
-rabbit ears
-chenille
-round
-hobby
-crankshaft
-Wilson's phalarope
-Murphy bed
-soil pipe
-forecourt
-policyholder
-tarmacadam
-loyalist
-gyro
-Queen's crape myrtle
-shortcake
-apple butter
-pumpkinseed
-heronry
-yellow perch
-baggage claim
-escarpment
-diaphragm
-mescal bean
-shunter
-flax
-columbarium
-Joe-Pye weed
-Neandertal man
-casement
-hole-in-the-wall
-Verdicchio
-futurist
-eaglet
-tassel hyacinth
-pup tent
-fawn lily
-cabbage palm
-pogonia
-hospital ship
-water mill
-Oregon grape
-lentil
-grindstone
-banana split
-inkberry
-coonskin cap
-bazooka
-wrap
-anise hyssop
-Java sparrow
-red-eyed vireo
-common opossum
-clintonia
-bustle
-booster
-tribesman
-soy
-panhandle
-jaboticaba
-locking pliers
-Sauvignon grape
-ghat
-screw
-oximeter
-white croaker
-saucepot
-eggbeater
-reticule
-cabbage bark
-looking-glass plant
-head gasket
-California sycamore
-cowbell
-Aleuria aurantia
-Herr
-lever
-spider orchid
-cashew
-shift key
-solar house
-wood chisel
-white
-mantilla
-stamp
-bolero
-rear admiral
-garden rake
-Lao
-crowbar
-lapdog
-buttermilk biscuit
-yellow bedstraw
-pickerel frog
-dowel
-serjeant-at-law
-mill-hand
-lambrequin
-state treasurer
-red silk-cotton tree
-coiffeur
-star anise
-shoulder pad
-marshal
-sitar player
-gown
-ground cedar
-hedge maple
-caddie
-pitahaya
-corn marigold
-stick cinnamon
-woodland star
-Eurasian green toad
-anti
-blueweed
-medicinal leech
-gaur
-chocolate kiss
-kit fox
-mother
-butte
-audio CD
-blast furnace
-vitamin D
-nutgrass
-cornice
-black sheep
-hearing aid
-lingonberry
-quad
-lentil
-riding crop
-pratincole
-pentagon
-sea lavender
-nerita
-flatmate
-catboat
-water clover
-angiopteris
-mushy peas
-crown imperial
-music school
-woodshed
-platy
-Turk's-cap
-rundle
-reading teacher
-hardtack
-balloon sail
-oriental spruce
-bluefish
-white mulberry
-horned violet
-satin bowerbird
-treasure flower
-sustaining pedal
-mimosa
-spurge nettle
-sea green
-hasp
-lederhosen
-pink cockatoo
-long johns
-basket weave
-freewheel
-thrust bearing
-timber tree
-orphan
-falafel
-common camas
-bird of passage
-bird's foot trefoil
-electric eel
-fizz
-grape arbor
-serape
-brace
-hazelnut
-kylix
-horse mackerel
-cassia bark
-lizard orchid
-spat
-Brown Swiss
-pocket flap
-pillory
-purplish blue
-rolling mill
-tappet
-broccoli rabe
-semi-detached house
-mushroom coral
-fly orchid
-nougat bar
-ball hawk
-sand wedge
-shirred egg
-black locust
-strip lighting
-drop scone
-brush turkey
-ball
-tragopan
-dallisgrass
-tuatara
-great knapweed
-potentiometer
-Kiliwa
-Pacific bottlenose dolphin
-accelerator
-Darwin tulip
-osteopath
-Arizona cypress
-manna ash
-butterbur
-cornelian cherry
-American holly
-nopal
-tanker
-foreshore
-ditty bag
-gas lamp
-safety razor
-chanter
-fomite
-chip
-striped killifish
-catalytic converter
-plaice
-dusty miller
-takin
-gerenuk
-corn chamomile
-Japanese pagoda tree
-boneset
-common osier
-Guinean
-taro
-plotter
-celandine poppy
-churn
-steenbok
-edible mussel
-sensitive fern
-triode
-black raspberry
-zoo keeper
-feather ball
-dredger
-starlet
-cornpone
-coat button
-rosinweed
-toy Manchester
-crested cariama
-finger food
-basilisk
-shotgun shell
-comfort food
-mountain hemlock
-candytuft
-Stilton
-record changer
-anklet
-ball valve
-Mediterranean snapdragon
-BVD
-sand cat
-Galloway
-nutmeg
-water-mint
-woodwaxen
-citron
-ark shell
-federalist
-drone
-cheekpiece
-hyperbaric chamber
-addax
-field-emission microscope
-synchronous converter
-men's room
-medlar
-electronic fetal monitor
-Sazerac
-false indigo
-roof
-passe-partout
-meadow spittlebug
-Phytophthora infestans
-oast house
-hedge nettle
-voting booth
-slender salamander
-telephone jack
-true bug
-scouring rush
-Scotch egg
-matchbook
-aperea
-cytomegalovirus
-garlic press
-cove
-whitebark pine
-Slovene
-narrow wale
-mother's milk
-Audubon's warbler
-prickly poppy
-cowl
-tailorbird
-mud brick
-bamboo palm
-welt
-Afghan
-Virginia spring beauty
-dinner bell
-night jasmine
-fly rod
-microtome
-aerie
-carinate
-picker
-brick trowel
-loving cup
-swathe
-green mayonnaise
-rivet
-bandbox
-newsroom
-tea tortrix
-bobby
-gig
-hush puppy
-garlic chive
-piston rod
-aspidistra
-bluejack oak
-harvest-lice
-strap hinge
-sour mash
-macadamia nut
-histiocyte
-fan belt
-shelf bracket
-abelia
-Hottentot fig
-fish chowder
-abettor
-compote
-beige
-dioon
-hop
-haymaker
-oilskin
-magnetometer
-tool bag
-tambour
-call girl
-gringo
-fairy light
-broad-leaved plantain
-second base
-zebra mussel
-Japanese cedar
-pistia
-swamp chestnut oak
-cashmere
-double cream
-samisen
-lamb curry
-companion
-kapok
-julep
-sweet woodruff
-gardener
-jewfish
-inspector general
-collembolan
-wheel bug
-bass
-scrubland
-wryneck
-macrozamia
-trouser press
-clove
-tiger cowrie
-yawl
-collard
-dildo
-pony cart
-ormer
-annual
-tessera
-chancellery
-two-toed sloth
-queen
-old lady
-wringer
-spritzer
-baggage
-black mangrove
-black-eyed Susan
-semifinalist
-highlighter
-alfalfa
-Easter daisy
-escapement
-operating table
-neutral spirits
-bursar
-roble
-entablature
-girl wonder
-farm boy
-ring ouzel
-permanent press
-auklet
-beefsteak tomato
-gaming table
-tea bag
-manul
-giant bamboo
-Ozark chinkapin
-matzo
-furrow
-smoothhound
-CD-ROM drive
-powdery mildew
-copilot
-garden
-American merganser
-bunsen burner
-Asian longhorned beetle
-lead tree
-creeping buttercup
-Percheron
-back brace
-axseed
-cub
-soul food
-rabbi
-edelweiss
-mineshaft
-fox grape
-sandwort
-torque wrench
-leisure wear
-Mae West
-broccoli
-loach
-maraschino
-heavy cream
-silkworm
-cirque
-vintner
-whitewash
-butterfly pea
-two-toed sloth
-midiron
-ceriman
-Bulgarian
-operating microscope
-sambuca
-California fuchsia
-silver maple
-tangelo
-black bean
-lugsail
-starting gate
-leek
-sunflower seed
-fish fry
-clinker
-synagogue
-coscoroba
-brae
-uphill
-common limpet
-golden plover
-cedar of Lebanon
-amphibian
-Canary wine
-taipan
-agua
-feeder
-parallel
-mater
-pink calla
-meat counter
-yagi
-crab cactus
-cacao bean
-bowfin
-alley cat
-stonefly
-Eastern cottonwood
-vernier scale
-marginal wood fern
-dancing-master
-detective
-yam
-textile screw pine
-hooch
-spinet
-single prop
-sassafras
-goose barnacle
-triple cream
-China tree
-peeper
-dressmaker
-snatch block
-ironmongery
-dressing case
-creeping bellflower
-silver sage
-honeydew
-eastern red-backed salamander
-peg
-nombril
-danish
-mashie
-anarchist
-alligator snapping turtle
-shepherd
-American white pine
-runner
-chalice vine
-rheumatologist
-defibrillator
-yellow chamomile
-lemon balm
-peacekeeper
-native beech
-sandwich board
-Bavarian
-titrator
-paneling
-deer mouse
-poteen
-sugar snap pea
-meadow salsify
-town crier
-best
-basinet
-common myrtle
-night lizard
-cushaw
-Tampax
-camphor tree
-gentile
-orange peel
-putty knife
-pyromaniac
-Brummie
-fever tree
-double
-nest
-inferior
-cabbage tree
-graduated cylinder
-mucor
-woodborer
-earthwork
-potato salad
-four-hitter
-gooseberry
-water vole
-ziggurat
-grapefruit juice
-four-in-hand
-cranberry bush
-diode
-videotape
-Mohican
-niacin
-beetroot
-shirtsleeve
-cork tree
-two-eyed violet
-white ash
-drawing chalk
-baked Alaska
-bone-ash cup
-toastrack
-diastema
-bed jacket
-dwarf astilbe
-yellow honeysuckle
-cow pasture
-sheet pile
-saxhorn
-upholstery material
-California white oak
-Spanish bayonet
-horsemint
-littleneck
-deflector
-magician
-standard transmission
-blue marlin
-shallot
-feijoa
-collar
-board
-jump suit
-common staghorn fern
-priory
-Xhosa
-Loranthaceae
-barbecued wing
-barmaid
-spit
-lemon juice
-umbrella plant
-field pennycress
-centenarian
-queen bee
-fish stick
-black bread
-dirk
-secularist
-German American
-spotted weakfish
-iron foundry
-speed bump
-yellow-fever mosquito
-gag
-frame
-black-eyed pea
-alcoholic
-involucre
-sperm whale
-balanced diet
-wax bean
-butcher's broom
-winter heath
-Mainer
-Australian pine
-gas guzzler
-double-breasted jacket
-pod
-palo verde
-trimmer
-wattmeter
-dyer's woad
-crotalaria
-vine maple
-sulky
-jack pine
-thumb
-Wilton
-Panchen Lama
-welder
-badminton court
-business editor
-Arabian coffee
-Kamchatkan sea eagle
-foamflower
-steep
-plane
-freckle
-cerebral cortex
-Vouvray
-tea
-forest tent caterpillar
-neckerchief
-accelerator
-jig
-bridal wreath
-highball glass
-New England clam chowder
-beach strawberry
-call waiting
-baton twirler
-double boiler
-Dutch elm
-car bomb
-filmy fern
-breviary
-Florida gallinule
-dace
-parsnip
-riparian forest
-crescent
-earplug
-grab bar
-cusk
-foglamp
-screwtop
-black mangrove
-mascot
-Welsh poppy
-gas holder
-support hose
-salsify
-red beech
-Indian python
-caroler
-pineapple juice
-lowboy
-terra sigillata
-black olive
-hypodermic needle
-radio-phonograph
-moussaka
-miter joint
-creche
-tuning fork
-black wattle
-affiliate
-vertical tail
-kiwi
-red morning-glory
-piping crow
-runway
-Kashmiri
-studio apartment
-sea feather
-Judas tree
-boatbuilder
-corn earworm
-fallboard
-Victrola
-lechwe
-goat willow
-turret clock
-Canada anemone
-leaf lettuce
-savoy cabbage
-headpiece
-Lebanese
-fothergilla
-hemlock
-toolshed
-silver tree
-blue-headed vireo
-weatherman
-cylinder
-caltrop
-adjutant bird
-driving iron
-millet
-European woolly thistle
-rose apple
-clown
-schoolfriend
-eastern coral snake
-barbecue
-executive vice president
-long-billed marsh wren
-brittle bladder fern
-tank destroyer
-left-hander
-matting
-catchment
-balsa raft
-eastern fence lizard
-color tube
-corncrib
-electric typewriter
-westland pine
-elder statesman
-whey
-plonk
-mound
-cittern
-nest egg
-copyholder
-China aster
-basking shark
-gavial
-common duckweed
-vanilla orchid
-red-shafted flicker
-granadilla
-sylph
-sty
-vest pocket
-potherb
-little brown bat
-Trapezium
-ordinary
-adult
-purple-fringed orchid
-abseiler
-disco
-metal detector
-beefsteak fungus
-ilang-ilang
-barley grass
-hawser
-suture
-brake shoe
-staghorn coral
-barbecue sauce
-Browning machine gun
-sarcophagus
-disa
-oven thermometer
-rosemary
-track
-gorget
-quince
-royal
-piston ring
-teak
-pin cherry
-Komi
-walking fern
-sloe
-synchronous motor
-fire-bellied toad
-Teleprompter
-co-star
-cape gooseberry
-oscillograph
-bass clarinet
-cock of the rock
-Tyke
-showy milkweed
-safety valve
-branch water
-sweet marjoram
-hugger
-crampon
-fairy godmother
-band-tailed pigeon
-snow-on-the-mountain
-minibar
-foreland
-grosgrain
-dita
-rampion
-calligrapher
-jointed charlock
-master
-sheepshead
-barrelhouse
-Carolina allspice
-mastic
-brake pad
-whiskey sour
-casement window
-conveyer belt
-stolon
-pavonia
-shinny
-witch elm
-logwood
-hostel
-pageboy
-vesper sparrow
-pyrrhuloxia
-common carline thistle
-wafer
-boysenberry
-screw augur
-hack
-American white oak
-governor general
-Mother Hubbard
-game fowl
-drosophila
-delft
-nymphet
-tollbooth
-chough
-Russian dressing
-plum tomato
-American saddle horse
-dusky salamander
-black medick
-red valerian
-cordage
-Elastoplast
-conacaste
-backlighting
-swell
-riveting machine
-cowpen daisy
-openbill
-water speedwell
-picture hat
-crested myna
-servo
-bletia
-garden trowel
-muscadine
-common caper
-false lily of the valley
-aralia
-sharp-tailed grouse
-cigar smoker
-bandoneon
-Chinese alligator
-crazy
-point lace
-charcoal
-Texas horned lizard
-marinara
-backstay
-Gatling gun
-piston
-game fish
-fall armyworm
-grammarian
-beer hall
-guadalupe fur seal
-sugar palm
-peanut
-velvet ant
-light machine gun
-rya
-cling film
-adobo
-myrtle oak
-angelica
-balsam apple
-windbreak
-brother-in-law
-snap brim
-automobile factory
-clavichord
-dusky shark
-edible banana
-altar boy
-California lady's slipper
-schoolbag
-wax bean
-Atlantic walrus
-bullpen
-straw wine
-thatch palm
-potluck
-tamarind
-charcuterie
-sod house
-tie rack
-liebfraumilch
-clinician
-scarlet lychnis
-Spanish iris
-bread knife
-water oak
-bedpan
-Angolan
-bassarisk
-Alaska fur seal
-African wild ass
-milk float
-froghopper
-Verpa bohemica
-water cooler
-chop suey
-ranker
-red helleborine
-Prince of Wales
-marmalade tree
-car train
-giant red paintbrush
-desert sand verbena
-right whale
-baron
-stevia
-asterism
-five-spot
-catapult
-Silex
-fiberscope
-refresher
-beef Bourguignonne
-snood
-divot
-waterproof
-crabeater seal
-Missouri primrose
-bumper guard
-rock opera
-Lilo
-coffee can
-smokehouse
-buffalo grass
-propjet
-ice tongs
-poop deck
-acorn barnacle
-veal parmesan
-shower room
-collins
-ringhals
-silage
-jawfish
-trouser cuff
-contour feather
-songstress
-rachis
-White Russian
-stanchion
-mastaba
-flatbed press
-viand
-legal representative
-espalier
-organic light-emitting diode
-sushi
-scorer
-haricot
-pinna
-plectranthus
-jungle cat
-dried apricot
-coach horse
-white fringed orchis
-veal cordon bleu
-bath
-dallier
-marching order
-donkey jacket
-Panama tree
-aerator
-klaxon
-pinnacle
-shouldered arch
-lesser celandine
-common eland
-Grand Marnier
-cock of the rock
-phlomis
-Japanese umbrella pine
-morning room
-dead-man's-fingers
-little auk
-bascule
-house paint
-home fries
-great skua
-cesspool
-flying gurnard
-wild crab
-checkerbloom
-Wollemi pine
-cheese dip
-coif
-charwoman
-tea ball
-waif
-Arctic ground squirrel
-parishioner
-stabilizer bar
-potentiometer
-black cohosh
-medlar
-willow oak
-cascara buckthorn
-scoutmaster
-Canada lily
-poppy seed
-paper mulberry
-blackthorn
-garrison cap
-inductee
-aeschynanthus
-interior live oak
-black spleenwort
-wild service tree
-sling
-nicad
-swab
-sego lily
-eiderdown
-fruit cocktail
-pallasite
-weeping spruce
-shiv
-sea lamprey
-coachman
-half binding
-American white birch
-gainer
-Concord grape
-yellow birch
-fucus
-common room
-io moth
-red osier
-crucible
-galangal
-salmagundi
-pepper steak
-cap opener
-swizzle stick
-tomato juice
-Nobelist
-Sarawakian
-African monitor
-sleeping beauty
-stereoscope
-curd
-pyramid bugle
-applejack
-dosser
-rake handle
-pilot light
-Eames chair
-Scotch and soda
-bell heather
-dinette
-blackpoll
-dogie
-sound camera
-cattle guard
-mashie niblick
-edible cockle
-monocle
-steak tartare
-partaker
-sidesaddle
-communications satellite
-porkfish
-water hemlock
-drawbar
-ultramicroscope
-Jamaican cherry
-craftsman
-lovage
-common apricot
-drum majorette
-backsword
-smooth alder
-Amniota
-dribbler
-theosophist
-dolman
-ivory tree
-Green Beret
-pipe smoker
-mayoress
-mignonette
-crampon
-henbane
-kirtle
-death's-head moth
-instep
-great St John's wort
-lorry
-black-necked cobra
-ball carrier
-Jordan almond
-byway
-earless lizard
-marble
-andiron
-high-protein diet
-buzzer
-ice floe
-crankcase
-Bofors gun
-sockeye
-veery
-Delaware
-caravansary
-prairie coneflower
-star apple
-suiting
-cot
-call forwarding
-American gallinule
-glossy snake
-rose chafer
-instant coffee
-placket
-Tarahumara
-pulsar
-philodendron
-orange tortrix
-cypress spurge
-Welsh rarebit
-music box
-giant crab
-vanilla bean
-water thrush
-prayer shawl
-gouge
-promoter
-dagga
-black currant
-bitter cassava
-drain basket
-snare
-digital audiotape
-retainer
-olive drab
-gluten bread
-graham cracker
-cheddar pink
-caregiver
-spray paint
-Anglo-American
-boatyard
-backbencher
-Link trainer
-bell arch
-weir
-arbor
-millionairess
-sour cream
-earthtongue
-crawlspace
-crossjack
-balalaika
-crupper
-western redbud
-guinea hen
-rangeland
-gaboon viper
-common louse
-single-leaf
-horseshoe
-balsam poplar
-triskelion
-jack-in-the-box
-jester
-rain stick
-glove compartment
-imperial moth
-Japanese beech
-biotin
-turnip
-oligarch
-western skink
-mudguard
-retsina
-data system
-green bristlegrass
-visiting professor
-beaded lizard
-weathercock
-Sloppy Joe
-high tea
-lightweight
-record sleeve
-cooler
-nodding onion
-pigs in blankets
-torque converter
-district attorney
-bunting
-orrery
-radiator hose
-common plum
-wood spurge
-calamus
-chicken Kiev
-pin
-lath
-telephone bell
-thistledown
-audiotape
-gypsy moth
-snuffer
-pari-mutuel machine
-peanut butter
-hearthrug
-sack
-Old World yew
-chives
-stovepipe
-xenolith
-mattock
-mangle
-electric chair
-backup system
-Empire
-blackwash
-dodder
-Allegheny chinkapin
-finger plate
-junk
-brown rice
-wild angelica
-chinaberry
-mason
-rasp
-den
-violet wood sorrel
-nosewheel
-plenum
-merino
-kirtle
-Igbo
-ensign
-sex symbol
-Belgian endive
-sugarberry
-yellow salsify
-purple emperor
-atlas
-African clawed frog
-leatherjacket
-midwife
-sac fungus
-European cuckoo
-three-day event
-Mexican poppy
-wagon tire
-armyworm
-rain gauge
-Oregon ash
-columbarium
-spectrophotometer
-Milanese
-pointing trowel
-casualty
-Eastern hop hornbeam
-lobe
-mouthpiece
-au pair girl
-giant water bug
-Browning automatic rifle
-laser-guided bomb
-drone
-white alder
-cockleshell
-mufti
-gravy
-berm
-boat hook
-marshmallow
-pet shop
-cowpea
-tactician
-wading pool
-anchovy dressing
-flip
-shackle
-Wedgwood
-thick-billed murre
-erecting prism
-giant salamander
-sleeper
-quiver
-chain store
-wing tip
-New World tapir
-witches' butter
-gendarme
-ginseng
-common maidenhair
-graduate nurse
-balsam pear
-hoatzin
-philanthropist
-axle bar
-gas meter
-moth mullein
-ragbag
-Chinese cabbage
-celery stick
-rutabaga
-scalpel
-cape marigold
-variometer
-argali
-brig
-shuffleboard
-wort
-Orlon
-epiphyllum
-allice shad
-coffee filter
-solar telescope
-Japanese linden
-thinning shears
-golden wattle
-queen triggerfish
-millinery
-surfbird
-flame fish
-clove
-dicamptodon
-red-bellied terrapin
-turmeric
-baya
-air horn
-Indian coral tree
-punnet
-sharkskin
-water crowfoot
-bight
-desert iguana
-Texas toad
-volva
-dredge
-Turkey red
-chemical plant
-gemma
-dice cup
-orange marmalade
-mistletoe
-surveyor
-frozen orange juice
-pallette
-poultryman
-burbot
-courlan
-captain
-saddlery
-bodyguard
-dwarf tulip
-black ash
-pulse
-nailbrush
-tickseed sunflower
-legless lizard
-shirtwaist
-polling booth
-chickeree
-garlic chive
-common thyme
-multichannel recorder
-screw thread
-sangoma
-calliopsis
-geoduck
-colleen
-bandicoot rat
-pastis
-swamp sunflower
-scorekeeper
-Honduras mahogany
-Australian pitcher plant
-triangle
-elevator shaft
-green pea soup
-carrel
-prairie aster
-bird's-nest fungus
-scarlet clematis
-gook
-mescal button
-carcase
-mulatto
-ejection seat
-strawberry daiquiri
-goat grass
-car battery
-babu
-chief of staff
-monilia
-Siberian crab
-ridge rope
-Morchella semilibera
-nutmeg
-moosewood
-graham bread
-California four o'clock
-zwieback
-velvetleaf
-abelmosk
-shadow box
-corned beef hash
-newsreader
-backstairs
-cutwork
-sherbert
-tooth fungus
-angel-wing begonia
-greasepaint
-common milkwort
-potato vine
-CD drive
-crepe de Chine
-sporting man
-koto
-armet
-barking frog
-celeriac
-drainage ditch
-black box
-steel blue
-clotheshorse
-corn speedwell
-drawknife
-spritsail
-vichyssoise
-modeler
-pocketcomb
-limey
-suslik
-cockpit
-digester
-brig
-raita
-troll
-benedictine
-rock wren
-lock
-Barnaby's thistle
-school bell
-school ship
-Soave
-falchion
-swaddling clothes
-terrine
-smoke screen
-rivulus
-sweet lemon
-cullis
-bustier
-peppermint
-Philadelphia fleabane
-Hampshire
-active
-charnel house
-face guard
-Quebecois
-facilitator
-tongue depressor
-bitternut
-heath aster
-sapodilla
-bluestem
-centrist
-Canterbury bell
-needlenose pliers
-groats
-tapa
-Qatari
-paper feed
-tilt-top table
-plastering trowel
-brazil nut
-rotogravure
-patriot
-manicurist
-bacon and eggs
-puffbird
-lightweight
-golden willow
-kaiser roll
-duff
-girandole
-seaside daisy
-Kurdistan
-Skivvies
-showboat
-fire bell
-lock-gate
-greater masterwort
-weald
-ice ax
-toetoe
-mess kit
-bucking bronco
-black turnstone
-backscratcher
-backpacker
-basement
-marbleization
-trigger
-satsuma
-fall-blooming hydrangea
-mountain lady's slipper
-yellow oleander
-crookneck
-ex-president
-Venn diagram
-psaltery
-bulwark
-old boy
-linear leaf
-aril
-butt weld
-fall webworm
-pruner
-bald-faced hornet
-nougat
-tailgate
-field speedwell
-potsherd
-center punch
-long beech fern
-desert paintbrush
-canyon treefrog
-bushel basket
-Eurasian
-swamp horsetail
-cryptanalyst
-wicket
-school newspaper
-captive
-spider brake
-electric mixer
-tumbleweed
-mason wasp
-sash window
-paddock
-wet bar
-oxtongue
-stevia
-wheat rust
-scute
-switch engine
-mud dauber
-dotterel
-snailflower
-common barberry
-mulligatawny
-cinnamon bark
-cigar box
-trivet
-proof spirit
-cream soda
-western grey squirrel
-baby powder
-Bren
-Japanese yew
-sailcloth
-Basket Maker
-bannock
-basidiocarp
-aphelion
-erect bugle
-limiter
-bosc
-Przewalski's horse
-helmet orchid
-audiometer
-battle cruiser
-grass widower
-staphylococcus
-Congolese
-common pitcher plant
-parliamentary agent
-Virginia snakeroot
-mockernut
-Siberian elm
-backbench
-rough
-chervil
-chlamys
-nationalist
-galantine
-screwdriver
-falsifier
-cancerweed
-spur
-jerkin
-porte-cochere
-dill pickle
-Montagu's harrier
-tetrode
-true fungus
-American quaking aspen
-vitamin B1
-leopard lily
-eggdrop soup
-aurochs
-core bit
-Jaws of Life
-trousseau
-parquetry
-Disciotis venosa
-tender
-beef goulash
-vitamin K1
-pepper spray
-covered smut
-hook
-sports announcer
-weapons carrier
-foxtail grass
-sloe gin
-mezereon
-antifouling paint
-pavior
-pile driver
-security consultant
-monkey-wrench
-Indian hemp
-amaretto
-American wistaria
-A-line
-market strategist
-rainbow runner
-souvlaki
-binturong
-stiletto
-gastrula
-Vietnamese
-Old World hop hornbeam
-cold cathode
-pier table
-houndstooth check
-prop root
-leaf-footed bug
-sedge wren
-Dutch iris
-drop curtain
-opossum rat
-lame
-pollen tube
-doubletree
-compression bandage
-pinon pine
-catmint
-pier arch
-kingmaker
-deanery
-loofah
-fullback
-fencing mask
-flying boat
-carpet sweeper
-lemon-scented gum
-Accipitriformes
-kit
-pigfish
-clipper
-dolmas
-lesser centaury
-blood agar
-water violet
-raw milk
-lemonade
-vicar-general
-supply closet
-Anzac
-confectioner
-ignition key
-velvet grass
-white willow
-John Dory
-ruddiness
-wheel
-common horsetail
-hubbard squash
-speculum
-Spanish bayonet
-mountain mint
-glint
-foxhole
-housemate
-bootjack
-sleigh bell
-clog dancer
-Mexican mint
-rendering
-Hausa
-star saxifrage
-spring squill
-clothesbrush
-liquid metal reactor
-Columbia tiger lily
-sorrel
-cartwheel
-Jersey
-Caucasian walnut
-desert willow
-surveyor
-elbow
-Santa Gertrudis
-fringe bush
-industry analyst
-lyrebird
-Cortland
-arroz con pollo
-catechist
-tank top
-jew's harp
-cereal oat
-heartleaf
-short sleeve
-butty
-butterfly plant
-stud finder
-felloe
-beer garden
-clevis
-wood warbler
-demerara
-cornetfish
-mince
-Jamaica rum
-Spanish broom
-binnacle
-camise
-ferrule
-Copt
-hall
-minicar
-scimitar
-cryptogam
-miter box
-limestone fern
-Marsala
-Parliamentarian
-gravy
-woolly bear moth
-formula
-squash bug
-pigmentation
-plate
-skin graft
-radiotelegraph
-hellbender
-soft pedal
-lavender cotton
-propagator
-Bailey bridge
-cottage pie
-rotgut
-A battery
-pintle
-off-line equipment
-European swift
-shrimp butter
-plumb bob
-trunk lid
-succotash
-yellow cypress
-heartleaf
-antelope squirrel
-sambar
-maternity ward
-deciduous plant
-bartlett
-Riesling
-sour cherry
-Klansman
-poke
-academician
-sociolinguist
-bird's nest fern
-common privet
-scale fern
-tachograph
-oyster stuffing
-pusher
-green June beetle
-staghorn sumac
-lockage
-master
-bap
-harlequin
-blackfly
-spotted coral root
-kahikatea
-cabana
-riot gun
-apple mint
-kob
-praline
-confidant
-pahautea
-float
-city father
-Zen Buddhist
-pessimist
-conference center
-banksia rose
-comfit
-sweet cicely
-winged bean
-henroost
-myope
-bunt
-nailfile
-yellow mountain saxifrage
-cruise control
-abandoned ship
-water chinquapin
-spanker
-wing nut
-puccoon
-pier glass
-Atlantic sailfish
-medlar
-buttercrunch
-rough-skinned newt
-planter's punch
-Dutch iris
-control key
-committeewoman
-torpedo-boat destroyer
-garambulla
-tree heath
-gladiator
-September elm
-inclinometer
-snowbell
-call-in
-sunsuit
-microfiche
-bluestocking
-cheval glass
-server
-franking machine
-sugar syrup
-Macoun
-transport ship
-alderfly
-wash-and-wear
-Abbe condenser
-bush nasturtium
-wild leek
-canary seed
-Northern Baptist
-sweet wormwood
-jaboticaba
-cardroom
-autoradiograph
-ash-pan
-sprinkler system
-rattrap
-claymore
-parts bin
-forest red gum
-thermonuclear reactor
-Indian crocus
-lector
-heir apparent
-leafy spurge
-masquerader
-varicella zoster virus
-cucumber tree
-hedger
-Shumard oak
-zooplankton
-quartermaster
-arrester
-bridge
-hop clover
-meadow foxtail
-winter hazel
-portable circular saw
-penuche
-limpa
-blue toadflax
-mesophyte
-Alpine anemone
-pet sitter
-avocado
-streptococcus
-fiber optic cable
-river red gum
-hornist
-chicken taco
-red spider
-tape grass
-densitometer
-salmonberry
-tiger snake
-hot toddy
-silver fern
-candlenut
-buckram
-local call
-defoliator
-king
-mahoe
-lever lock
-social insect
-winter purslane
-bootblack
-fireball
-ramie
-bellbird
-prepuce
-capote
-Chinese forget-me-not
-Pisces
-costume
-California black oak
-tree lupine
-golden polypody
-liger
-California whipsnake
-urodele
-sapodilla
-skillet bread
-duckpin
-supremo
-asparagus bean
-kampong
-endameba
-cow pony
-rider
-motherwort
-Persian iris
-soursop
-kohlrabi
-Parisienne
-irons
-doubles
-feijoa
-farmplace
-cottage cheese
-bezoar goat
-subcontractor
-blunderbuss
-down
-purple martin
-Lapp
-crenate leaf
-tobacco pouch
-beach towel
-Santa Lucia fir
-monetarist
-stringer
-ocellated turkey
-Texas purple spike
-ackee
-caddy
-hedge mustard
-second-rater
-strawberry bush
-valedictorian
-steak sauce
-prairie gourd
-aspirant
-mint
-Valenciennes
-vodka martini
-American persimmon
-big brown bat
-Mycenaen
-mouthpiece
-norfolk island pine
-pennyroyal
-Jewish rye bread
-granadilla
-tract house
-wall
-shuttle helicopter
-blackjack oak
-Lippizan
-storm window
-white zinnia
-sickle
-sushi bar
-polish
-baldric
-brooklime
-church hat
-control circuit
-vicuna
-death adder
-eukaryote
-durmast
-field soybean
-jacket potato
-wild basil
-queen consort
-brooklime
-octant
-blue false indigo
-broccoli raab
-step-down transformer
-date bread
-blue ash
-duffer
-oak chestnut
-pennant
-wedge
-Florentine iris
-morion
-weakfish
-morning dress
-public address system
-spearmint
-Ashkenazi
-sow
-interpreter
-Metis
-pita
-iron lung
-parfait glass
-cylinder lock
-immortelle
-obstetrical toad
-tee hinge
-successor
-western
-working girl
-julienne
-AND circuit
-spaghetti junction
-fer-de-lance
-enlisted woman
-star
-lightning rod
-bilge pump
-pacer
-horse nettle
-African oil palm
-blastocyst
-air hammer
-bamboo fern
-remote terminal
-lambkin
-money cowrie
-Pelham
-clinical thermometer
-wiggler
-guru
-false indigo
-tea bag
-foredeck
-king
-baby shoe
-mule
-grab bag
-silver-bell tree
-knitting machine
-cobia
-roulette ball
-larder
-button pink
-rumble seat
-noria
-queen mother
-solar thermal system
-aquaplane
-highbrow
-rusty blackbird
-desktop
-lima bean
-pontoon bridge
-watercress
-wild cabbage
-tumbleweed
-dressing sack
-compact-disk burner
-spittoon
-marrow
-sporophyte
-second fiddle
-pot-au-feu
-specialty store
-dry
-mole
-khadi
-japonica
-lovage
-squamous cell
-lobe
-European creeper
-brown pine
-bladderpod
-rumble
-French Canadian
-mascarpone
-Pacific halibut
-perennial ryegrass
-wine lover
-turbot
-longwool
-silver tree fern
-dust cover
-synchromesh
-corn pudding
-alpine azalea
-garboard
-cane sugar
-observation dome
-condensation pump
-hind
-taximeter
-hand drill
-gas thermometer
-jammer
-buffing wheel
-handstamp
-prairie mallow
-turkey stew
-sun spurge
-duck pate
-kibble
-Cassin's kingbird
-apadana
-Devon
-grinner
-oocyte
-blank
-header
-schoolmaster
-guard ship
-intravenous pyelogram
-rimu
-luff
-Mediterranean fruit fly
-singlestick
-lady-in-waiting
-curb
-birch
-limekiln
-orthoscope
-serotine
-Spanish oak
-swamp cottonwood
-edger
-city man
-picnicker
-white basswood
-Parsons table
-Christmas begonia
-perspirer
-Pacific tree toad
-Cape tulip
-finger bowl
-blue pike
-greengage
-handcar
-milkweed
-potbelly
-river dolphin
-creel
-typewriter carriage
-banteng
-pawnbroker's shop
-huon pine
-biennial
-man of action
-foundress
-caveman
-featheredge
-jordan almond
-sandblaster
-coralberry
-low-calorie diet
-hoot owl
-garter
-bain-marie
-wrecker
-fenugreek
-double-hung window
-idol
-scullery
-balloon vine
-summer savory
-winged spindle tree
-Helvella crispa
-walrus mustache
-gas engine
-boulle
-rush grass
-rue
-hoe handle
-cat fancier
-deerstalker
-dunker
-American red plum
-fall dandelion
-groover
-sprag
-stair-rod
-wish-wash
-pricket
-architrave
-California laurel
-net melon
-Arizona sycamore
-executive secretary
-silverweed
-silky cornel
-surface ship
-square sail
-common purslane
-villa
-holly-leaved cherry
-sweet birch
-pecan
-artillery shell
-breast pocket
-pirogi
-scarlet runner
-rabbit brush
-mealworm
-leather carp
-palette knife
-Jerusalem sage
-boneshaker
-slit lamp
-digital voltmeter
-polar glacier
-square-rigger
-homogenized milk
-Sten gun
-lesser calamint
-pyrograph
-Korean lawn grass
-Zinfandel
-crepe fern
-western ragweed
-clasp knife
-distributor housing
-cartouche
-scooter
-ski parka
-jackknife
-Carolina spring beauty
-soft diet
-candlesnuffer
-horse trader
-step stool
-agouti
-accelerometer
-annual fern
-judge advocate
-angelica
-roll film
-treehopper
-ombu
-comer
-sultanate
-kitchen help
-hooded ladies' tresses
-milking machine
-knuckle joint
-Jamaica honeysuckle
-music teacher
-sauerkraut
-Weston cell
-slivovitz
-Worcester sauce
-tall bellflower
-chancery
-prophetess
-casquet
-shortfin mako
-sorus
-visual display unit
-asp
-grenadier
-black pepper
-crottle
-erasable programmable read-only memory
-jabot
-ratchet
-disk controller
-chief petty officer
-tap wrench
-white mountain ash
-cultivated rice
-flying phalanger
-skillet corn bread
-BB gun
-Elamite
-European red elder
-reed rhapis
-ciderpress
-inga
-torpedo
-wild teasel
-bean curd
-oeil de boeuf
-acuminate leaf
-bitter lemon
-hitchrack
-Lorraine cross
-hostess
-European dogtooth
-adz
-polonaise
-rock sandwort
-Waldorf salad
-myrmecophile
-klystron
-mole rat
-draba
-corn borer
-robusta coffee
-chub mackerel
-leatherleaf
-chronometer
-Moselle
-sea aster
-fennel
-slop basin
-constable
-Brunswick stew
-hydraulic pump
-French omelet
-icebreaker
-Manx shearwater
-press of sail
-ninepin
-blue succory
-bootstrap
-hallstand
-chit
-firefly
-bearded seal
-fuel filter
-jezebel
-mate
-Roquefort
-cheesecloth
-plasterer
-blue pimpernel
-lake dwelling
-shrink-wrap
-goat cheese
-common gum cistus
-coastland
-Sunday best
-wild tobacco
-mandrake
-common unicorn plant
-barbican
-culotte
-blockhouse
-German iris
-tarragon
-caramel
-wild rosemary
-grain
-voyager
-squirting cucumber
-eastern narrow-mouthed toad
-creeping fern
-luge
-saffron
-garland flower
-furnace room
-starship
-Oriental scops owl
-Italian honeysuckle
-berserker
-Chinese elm
-scrubber
-bishop pine
-French polish
-compromiser
-skimmer
-river shad
-lobster thermidor
-leadwort
-man-of-the-earth
-razorblade
-vicegerent
-empress
-link
-ham and eggs
-wild lily of the valley
-blackfish
-splicer
-fossa
-mara
-moneygrubber
-brachiopod
-fauteuil
-caldera
-finish coat
-croupier
-termer
-leopard's-bane
-sei whale
-molucca balm
-dolly
-dog food
-term infant
-soft roll
-episcia
-sewer
-inquiry agent
-active citizen
-perry
-California newt
-moon shell
-bladderwrack
-common shrew
-dill
-Dutch elm fungus
-key lime
-electrometer
-divorce lawyer
-lamb's-quarters
-apple turnover
-shipmate
-Guernsey
-legionnaire
-electric blanket
-Rocky mountain pinon
-tobacco mildew
-stinking iris
-forestiera
-departure lounge
-wiper motor
-jurist
-scarlet runner
-pallbearer
-batter's box
-inertial guidance system
-fines herbes
-oilcan
-sisal
-mustache cup
-steamed pudding
-Visayan
-fiesta flower
-lady tulip
-lungless salamander
-batiste
-electrical system
-blazing star
-car carrier
-Walloon
-mother hen
-stump
-mulled cider
-secondary coil
-Alexandria senna
-etui
-scrumpy
-Havasupai
-jawbreaker
-glume
-ex-husband
-Eskimo
-Joint Direct Attack Munition
-number theorist
-five-hitter
-pinstripe
-Olympian
-common mackerel
-stone bass
-bigos
-Bahraini
-airbrush
-great ragweed
-glass lizard
-hand fern
-roundel
-riding master
-shoetree
-yellow avens
-old fashioned
-dolman
-stinger
-nursling
-legate
-faille
-golden fern
-bedpost
-shop steward
-kidney bean
-bladderwort
-internist
-limeade
-Bruneian
-Coloradan
-playsuit
-wintergreen oil
-Cantabrigian
-mutton snapper
-shot putter
-hand grenade
-moccasin
-cobnut
-marrow
-separatist
-cockscomb
-discharge pipe
-Gabonese
-spade bit
-chicken cordon bleu
-varnish tree
-European wood mouse
-striped gentian
-Ayrshire
-curassow
-moo goo gai pan
-malarial mosquito
-glow tube
-ledger board
-bib-and-tucker
-European chestnut
-suffragette
-color wash
-gaffsail
-golden larch
-voting machine
-Kahlua
-lungi
-amusement arcade
-Uzbek
-butternut
-mold
-mule's ears
-dickey
-shrimper
-trophozoite
-dreadnought
-shepherd's purse
-greenhouse whitefly
-spotted gum
-copperware
-perfect game
-semigloss
-spawn
-telecom hotel
-stakeholder
-mason wasp
-flibbertigibbet
-chin strap
-fringed pink
-saki
-urchin
-memorizer
-roulade
-whiting
-cling
-corncrake
-Queen of England
-choo-choo
-empty
-heating pad
-playmate
-visualizer
-popcorn ball
-absconder
-sou'wester
-target acquisition system
-mock-up
-dental floss
-tray cloth
-haddock
-bulblet fern
-housing commissioner
-delayed action
-anchor light
-harbor porpoise
-water wings
-PT boat
-night latch
-fennel
-doorframe
-green-tailed towhee
-grey polypody
-torture chamber
-American germander
-Chinese wistaria
-cattalo
-accompanist
-rifleman
-alpine clover
-contrarian
-lemon peel
-Mexican cypress
-sprog
-dado
-Galilean telescope
-desmid
-lockup
-Latin
-American raspberry
-mescal
-butternut
-prairie orchid
-downy yellow violet
-green hellebore
-radio compass
-bread and butter pickle
-Cherokee rose
-knish
-destroyer escort
-Arkansan
-langlaufer
-pyxis
-winter savory
-velocipede
-motley
-winter savory
-law student
-barren ground caribou
-apple dumpling
-field hospital
-works
-city editor
-European flatfish
-Morchella crassipes
-life office
-boot camp
-cream sauce
-cape aloe
-acetate disk
-devil ray
-tile cutter
-Plymouth Rock
-microspore
-godown
-Syrian
-tiercel
-American cranberry
-lesser spearwort
-anopheline
-Spanish oyster plant
-wire cloth
-attic fan
-birch beer
-small computer system interface
-crook
-ribbon fern
-explorer's gentian
-nagami
-I-beam
-rosebud cherry
-Jerusalem artichoke
-Stillson wrench
-pluralist
-district manager
-Levantine
-orangeade
-part-timer
-post horn
-Oregon grape
-contadino
-cargo helicopter
-silverpoint
-chaja
-California bluebell
-case
-Shasta
-cheese cutter
-Leishmania
-avalanche lily
-iron horse
-bialy
-Yana
-Delawarean
-Prussian
-nonpareil
-hammer
-hoper
-chewink
-anil
-skim milk
-desert four o'clock
-crescent wrench
-white marlin
-blue jasmine
-malacca
-anadama bread
-purple poppy mallow
-ganglion cell
-ligature
-no-parking zone
-golden clematis
-Cotswold
-aliterate
-shebeen
-yardarm
-superbug
-fanaloka
-stinking cedar
-spirochete
-wort
-pater
-heaume
-thermocouple
-ironing
-naval tactical data system
-European goatsucker
-prairie cordgrass
-accused
-foreign agent
-halberd
-western mugwort
-esthetician
-Persian lilac
-cracked-wheat bread
-crosscut saw
-rock penstemon
-paper cutter
-crematory
-ideologist
-cattley guava
-margarine
-creosote bush
-hoary plantain
-spark gap
-lumberjack
-Greek valerian
-mission bells
-tight end
-bigeye
-large crabgrass
-stone marten
-cleat
-lentil
-bay scallop
-lector
-charger
-assemblywoman
-second lieutenant
-boil smut
-sarsaparilla
-hydromel
-cat flea
-pinfish
-whole milk
-hairnet
-myeloblast
-peasant
-blind curve
-first offender
-dwarf-white trillium
-Brother
-coatdress
-gun emplacement
-tamarisk gerbil
-snap
-air cushion
-trailing edge
-potato vine
-gig
-everlasting pea
-champion
-dibble
-rattail cactus
-timothy
-prince's-feather
-cutlas
-lockring
-sealing wax
-Brussels lace
-corn mint
-highboard
-she-oak
-wild celery
-pillar
-Burberry
-Hakka
-leucothoe
-bell tent
-gallery
-coontie
-leather fern
-smack
-adenovirus
-linoleum
-chain wrench
-tammy
-gas fixture
-nut bar
-baneberry
-butterscotch
-goat's rue
-bullock
-grey snapper
-mother-in-law
-hyson
-wayfaring tree
-mollie
-needle spike rush
-buckwheat
-bayberry
-brush-tailed phalanger
-dry rot
-harborage
-stormy petrel
-Oriental beetle
-Atlantic halibut
-coping saw
-simple fruit
-viscose rayon
-surgeonfish
-upstairs
-security system
-common ragweed
-verticillium
-pancake batter
-hawk's-beard
-Dutchman's-pipe
-refrigeration system
-European parsley fern
-Ivy Leaguer
-totalitarian
-gonococcus
-towhead
-showy sunflower
-pallium
-multiengine airplane
-hair trigger
-rabbit-eared bandicoot
-siskiyou lewisia
-fuel system
-flat arch
-broad beech fern
-Alpine lady fern
-bracken
-Kentucky black bass
-rut
-mountain maple
-tunaburger
-umbrella fern
-white-headed stilt
-meat hook
-panhandler
-washhouse
-barnyard
-safety lamp
-leg
-ripple mark
-paper
-sagebrush lizard
-light heavyweight
-common nutcracker
-operator
-stalking-horse
-horseless carriage
-fishhook
-suction cup
-peg
-Ungulata
-false teeth
-round-bottom flask
-Luba
-campaign hat
-firebox
-rudder
-parapet
-ice pack
-appellant
-spirit stove
-metheglin
-common bamboo
-soapwort gentian
-pannikin
-time capsule
-burn bag
-folk poet
-tropical prawn
-end man
-new caledonian pine
-linen
-web
-free trader
-jury box
-railing
-pignut
-leaker
-potboy
-rubber boa
-white snakeroot
-plumber
-Candida albicans
-surfboat
-woman
-promulgator
-eyecup
-wild China tree
-rattlesnake master
-Viyella
-alpine salamander
-ailanthus silkworm
-Albatrellus ovinus
-war room
-meadow vole
-robotics equipment
-rotary actuator
-Engelmann spruce
-pinesap
-beefcake
-native speaker
-ridge
-injector
-water chute
-salmonberry
-decoupage
-bottlebrush
-date plum
-circlet
-American mountain ash
-pocketbook
-horsemint
-sweet four o'clock
-kirpan
-pinto bean
-chervil
-equator
-range animal
-candy thermometer
-calanthe
-cul
-stipendiary
-brahman
-pelican crossing
-topgallant
-wild senna
-sliding window
-carrier pigeon
-Tatar
-quadruplet
-bumboat
-spearmint oil
-slip clutch
-young Turk
-golden yarrow
-shank
-glasswort
-dental plaque
-Manduca sexta
-Northern bedstraw
-dent corn
-Life Saver
-western wall flower
-bedder
-wherry
-Tuscarora
-scrapple
-borstal
-reflux condenser
-problem solver
-nondriver
-perforation
-eastern cricket frog
-white wood aster
-broad buckler-fern
-Cape primrose
-herringbone
-head louse
-earl
-baton
-recording system
-primary color for light
-cherry laurel
-pomfret
-ratafia
-chocolate milk
-obscurantist
-revisionist
-rood screen
-magnetic needle
-commensal
-oil tycoon
-celebrant
-domicile
-harvest mouse
-California nutmeg
-greater spearwort
-black-billed cuckoo
-winepress
-demographer
-straw boss
-diabetic diet
-sweetmeat
-rabbet
-ming tree
-basketweaver
-freestone
-walk-in
-Aryan
-box coat
-audio amplifier
-chicken salad
-churidars
-whydah
-box
-batman
-siren
-selectman
-gouger
-drip coffee
-Caesar salad
-interpreter
-whinstone
-grey goldenrod
-minicomputer
-honey crisp
-hypercoaster
-Irishman
-swamp white oak
-reed canary grass
-globeflower
-cynthia moth
-fennel seed
-canthus
-chino
-blind date
-tar pit
-watermelon begonia
-fishtail palm
-overcast
-Pearmain
-primary color for pigments
-coal seam
-wherry
-safety bolt
-cretonne
-Michigan lily
-inflater
-moneybag
-huckleberry
-brassard
-bush vetch
-looking glass tree
-pinwheel roll
-alfalfa sprout
-sea kale
-clinometer
-achira
-lorgnette
-potter wasp
-gilded flicker
-tody
-capulin
-captain's chair
-crackle
-gerardia
-prie-dieu
-venture capitalist
-New Jerseyan
-block and tackle
-elf cup
-bur reed
-automatic transmission
-wax palm
-flytrap
-crack willow
-coachwhip
-swizzle
-lugger
-Dewar flask
-baster
-oxyacetylene torch
-Culex quinquefasciatus
-St Peter's wort
-wild hyacinth
-Russian almond
-burrfish
-wintergreen
-katsura tree
-butcher knife
-perfumery
-thresher
-porte-cochere
-sheepwalk
-hypotenuse
-Dalmatian iris
-buttercup squash
-demiglace
-goldenseal
-preceptor
-rigger
-poikilotherm
-old-age pensioner
-posthouse
-wood horsetail
-repeater
-reciprocating engine
-Rambouillet
-terra cotta
-togs
-battledore
-horizontal tail
-missile defense system
-trier
-morello
-woolly adelgid
-munition
-double creme
-in-fighting
-squirrel corn
-crow's nest
-antler moth
-brake cylinder
-bandoleer
-noticer
-Parmesan
-hipline
-cheapskate
-Dubonnet
-mole rat
-bog aster
-ribbon tree
-meadow rue
-nard
-ratel
-loose smut
-snapping shrimp
-golden glow
-basil thyme
-Florida strap fern
-moonshine
-flume
-lace fern
-black bream
-orchestra pit
-archerfish
-exile
-ringdove
-career man
-godfather
-bottom-feeder
-pasteurized milk
-dental implant
-pedicel
-Catalpa speciosa
-yellow foxglove
-lancet arch
-steam shovel
-sampan
-patrol boat
-sailor cap
-tollgate
-monal
-velociraptor
-cacique
-jack oak
-cursed crowfoot
-creep
-Parry manzanita
-common matrimony vine
-grace cup
-caecilian
-spurge laurel
-prickly lettuce
-Regius professor
-camail
-Sitka willow
-Courtelle
-gin sling
-dogmatist
-guest
-saltine
-dust cover
-sport
-sweeper
-feist
-lady's-eardrop
-vibist
-wire stripper
-tenpin
-interplanetary space
-beet green
-pruning knife
-drainage system
-gunnery
-ballet master
-lime juice
-flak catcher
-lacrosse ball
-Canadian aspen
-beatnik
-railhead
-utilizer
-spadefish
-Arizona white oak
-city university
-dense blazing star
-hedger
-chain pickerel
-right-hand man
-namby-pamby
-nacelle
-redneck
-tumbler
-Chief Secretary
-cannon
-cupola
-kummel
-papaya juice
-Burton
-Stanley Steamer
-loganberry
-stylus
-square meal
-rock bass
-western ladies' tresses
-dramatist
-assignee
-tandoor
-trumpetwood
-segregator
-green adder's mouth
-coral necklace
-ani
-iceboat
-densimeter
-oxtail soup
-kernel
-cos lettuce
-greenishness
-panchromatic film
-Parker House roll
-oatmeal
-backsaw
-double Gloucester
-bailey
-storage cell
-giant
-coconut milk
-broadtail
-barouche
-loir
-soybean meal
-white-leaved rockrose
-junction barrier
-spandrel
-sweat bag
-goldilocks
-flowering wintergreen
-cockspur
-beef fondue
-holding cell
-cardamom
-cagoule
-Kamia
-tangelo
-Herschelian telescope
-wine bar
-kachina
-sand sage
-guy
-ivory palm
-citrus mealybug
-topper
-ladyfish
-force pump
-fanion
-calaba
-Iowa
-orrisroot
-ivorybill
-Secretary of Agriculture
-gagman
-dry cell
-hypnotist
-kenaf
-grey alder
-deathwatch beetle
-gagman
-magnetic stripe
-trap door
-abdominal wall
-prefab
-broomcorn millet
-architeuthis
-angler
-Pacific giant salamander
-barbette carriage
-low-fat diet
-veal scallopini
-B battery
-wallah
-landing flap
-pistachio
-jaguarundi
-nagi
-cicerone
-felt fungus
-Aertex
-stocks
-smooth aster
-patchouli
-lemon sole
-sleeper
-basket fern
-dundathu pine
-anjou
-Moreton Bay chestnut
-broom sedge
-candid camera
-red angel's trumpet
-oilstone
-cinnamon toast
-Pacific walrus
-fruit custard
-Jehovah's Witness
-mate
-voyeur
-Esselen
-achromatic lens
-sanguine
-brine shrimp
-dunce cap
-swot
-transit instrument
-grey willow
-pack
-bench clamp
-Nova Scotian
-gadgetry
-silvery spleenwort
-enchantress
-rough fish
-morula
-giant taro
-sorus
-roux
-polyhedral angle
-spruce beer
-Chicano
-cola extract
-outfielder
-kohleria
-white-rumped shrike
-car-ferry
-subway token
-spoon bread
-totara
-corn borer
-bowhead
-tensimeter
-water scooter
-flickertail
-Catholicos
-pleaser
-blue-eyed Mary
-calabash
-handyman
-cascades frog
-facing
-scarlet oak
-lutist
-ginger
-tree tomato
-Harvey Wallbanger
-tent peg
-insectivore
-fusil
-swale
-chinning bar
-bladderpod
-New Dealer
-dhoti
-proscenium arch
-common vetchling
-channel
-collect call
-safflower
-Texas tortoise
-test equipment
-theca
-RAM disk
-sheep sorrel
-rammer
-buttonhook
-honey mesquite
-dominus
-babirusa
-queen
-Aspergillus fumigatus
-crash barrier
-nonmember
-Muscovite
-verdin
-Australopithecus afarensis
-Turkish Delight
-stalked puffball
-giardia
-divider
-mountain skink
-head smut
-pacemaker
-evaporated milk
-rattlesnake fern
-flamethrower
-navy bean
-bather
-steed
-showy orchis
-stone crab
-artichoke heart
-phantom orchid
-space helmet
-swamp laurel
-privateer
-junior
-surcoat
-bristlegrass
-flower girl
-aphid lion
-penthouse
-lemonade mix
-coude telescope
-natal plum
-scriber
-wood nettle
-rape suspect
-resplendent quetzel
-western poppy
-choir loft
-fore-topsail
-thyme-leaved sandwort
-erotic
-short circuit
-outdoors
-flowering tobacco
-hookup
-aviatrix
-corker
-horehound
-horn
-swamp pine
-water biscuit
-cherimoya
-vaporizer
-courtier
-European sole
-full skirt
-Mother Carey's chicken
-cymule
-huck
-white snapdragon
-mountain nyala
-country borage
-bonduc
-casein paint
-grampus
-shrimpfish
-lodge
-dragee
-black walnut
-caraway seed
-roper
-glass cutter
-tab key
-Richardson's geranium
-demigod
-chichipe
-Italian ryegrass
-cadet
-electrograph
-rudd
-carpenteria
-foie gras
-lignum vitae
-hedge nettle
-pledger
-American hackberry
-flageolet
-beaked hazelnut
-reflectometer
-sticky geranium
-marriage bed
-white pepper
-japanese clover
-whiteface
-gnat
-extrovert
-Canada plum
-talipot
-chicken stew
-egg foo yong
-fraxinella
-skibob
-saucer magnolia
-jacket
-green smut fungus
-cloakroom
-landing skid
-booth
-ice milk
-dipole
-striped coral root
-red buckeye
-roughcast
-breaststroker
-cowherb
-razor clam
-first-aid station
-briarroot
-clambake
-lander
-Bramley's Seedling
-frail
-jird
-minisub
-luging
-poison milkweed
-European lobster
-epidemiologist
-spandex
-paloverde
-marumi
-bypass condenser
-punter
-petty spurge
-Coryphaena hippurus
-bilberry
-vermillion rockfish
-witness box
-viscometer
-pulque
-Massachusetts fern
-herring salad
-ridge tile
-mesa
-dwarf grey willow
-southern aster
-punch pliers
-tarnished plant bug
-hoop pine
-Japanese red pine
-benedick
-rebozo
-silver plate
-silver willow
-mouse-ear hawkweed
-bonito shark
-abutment arch
-noble cane
-tiger rattlesnake
-pongee
-jumping plant louse
-pattypan squash
-giant ryegrass
-railroad bed
-stiff aster
-imperial Japanese morning glory
-laundry
-winter cress
-large white petunia
-tea maker
-pen-and-ink
-early warning system
-lug
-monocot
-sea wormwood
-breechblock
-postage meter
-third rail
-Mongoloid
-Australopithecus boisei
-umbrella tent
-stirrer
-Dumpy level
-beroe
-post and lintel
-green spleenwort
-tomato paste
-dishpan
-stentor
-sweatband
-cobbler
-New York fern
-gaff
-prairie willow
-cyclops
-jigsaw
-rotavirus
-pallet
-eastern ground snake
-boiling water reactor
-acute triangle
-agora
-European cranberry
-roebuck
-surgical dressing
-busboy
-cannikin
-feedlot
-common pond-skater
-cochin
-horsehair lichen
-fetter
-sapote
-fichu
-dermatologist
-fire tongs
-creme anglais
-foster-mother
-laurelwood
-chicken snake
-mincemeat
-rocker
-wild spinach
-powder and shot
-butterwort
-auxiliary engine
-mamey
-hart's-tongue
-sucking pig
-American turkey oak
-troopship
-buttermilk
-divi-divi
-boatswain's chair
-soda fountain
-southern flying squirrel
-elastic
-cutaway
-housekeeper
-renegade
-apple rust
-bridoon
-machicolation
-stunt
-keyhole limpet
-personality
-solitary vireo
-epidendron
-Jihadist
-boffin
-bettong
-terror
-partial denture
-pusher
-saltcellar
-capstan
-large poodle
-Bibb lettuce
-low-bush blueberry
-staple
-banded krait
-sickroom
-barnyard grass
-wandflower
-woodworm
-bluegrass
-squirrel's-foot fern
-rabbitfish
-delta wing
-milking shorthorn
-limber pine
-guru
-gamine
-scythe
-sweetsop
-Gruyere
-bloodmobile
-mine detector
-American mistletoe
-silver beech
-hound's-tongue
-Lombardy poplar
-basket fern
-pink-and-white everlasting
-redtail
-Aladdin's lamp
-mace
-outtake
-condensed milk
-Canada wild rye
-silver perch
-waxflower
-taxer
-Chinese chestnut
-Our Lord's candle
-mugwump
-school system
-salp
-osso buco
-dress shirt
-butterweed
-low-fat milk
-couchette
-broomcorn
-proscenium
-mill agent
-smut grass
-humpback
-southern spadefoot
-military leader
-canebrake rattlesnake
-tailor-made
-ebony
-beach house
-flying gecko
-hoary alison
-typhoid bacillus
-Romanov
-vanilla pudding
-sweet cicely
-Spodoptera exigua
-dress rack
-flannel
-skipjack
-bolognese pasta sauce
-rooibos
-thunderer
-blessed thistle
-gauntlet
-mahatma
-granadilla
-laurel sumac
-Yuma
-thyme-leaved speedwell
-encyclical
-twill
-linocut
-manna gum
-spark arrester
-cocklebur
-Indian hemp
-lemon oil
-Hall's honeysuckle
-raceway
-flop
-Himalayan lilac
-one-flowered wintergreen
-photosphere
-silvery spleenwort
-convex polygon
-canarybird flower
-foster-sister
-fluffy omelet
-palanquin
-roll
-dandelion green
-Javanese
-workpiece
-Carmelite
-bread mold
-schlemiel
-wild lily of the valley
-grugru
-solenoid
-puff batter
-skep
-balance wheel
-Gadaba
-portia tree
-mobcap
-two-man tent
-scuffle
-firebrat
-ant lion
-anise
-caster
-giant petrel
-American water spaniel
-naboom
-treasure ship
-foster-son
-fiddleneck
-alidade
-sugar refinery
-wild oat
-water beetle
-generic
-damson plum
-abrocome
-detainee
-pitch pipe
-coast
-nilgai
-radiotherapy equipment
-heart-leaved aster
-gristmill
-grocer
-Appaloosa
-Cheviot
-brake pedal
-lantana
-cave myotis
-Rob Roy
-sea spider
-latrine
-carpophore
-recycling plant
-coondog
-brace and bit
-funambulist
-eggar
-mantelet
-postdoc
-mezzanine
-coco plum
-pulse generator
-high-vitamin diet
-menhaden
-mechanical engineer
-bergamot mint
-Chuvash
-grated cheese
-helicon
-belladonna
-beet armyworm
-eelgrass
-resuscitator
-interrupted fern
-arrow grass
-cistern
-Pacific herring
-colostrum
-journal bearing
-Fauve
-wrist pin
-canape
-choice morsel
-quadraphony
-guard boat
-shortgrass
-claymore mine
-hitching post
-cargo door
-decoder
-gym rat
-Cocopa
-commander
-apple of Peru
-seckel
-yellow goatfish
-dog flea
-dodo
-oconee bells
-Tudor arch
-turkey stuffing
-ebony spleenwort
-wheat flag smut
-scolopendrium
-Brazilian pepper tree
-gusset
-inspector
-lunar excursion module
-baron
-plantigrade mammal
-Creole
-phosphate
-aromatic aster
-ghee
-audiovisual
-onychophoran
-cotton stainer
-lieutenant junior grade
-spheroid
-amen corner
-caper sauce
-Caladium bicolor
-dyer's rocket
-seaside goldenrod
-flint corn
-Very pistol
-rotifer
-steeplechaser
-rouleau
-escape wheel
-Namibian
-millivoltmeter
-emmer
-climatologist
-agateware
-sea lyme grass
-inclinometer
-water fennel
-saddle seat
-vicar
-garden cress
-ski rack
-Norfolk jacket
-casaba
-coast rhododendron
-sericea lespedeza
-hematocrit
-autopilot
-tilter
-finish coat
-Pennsylvanian
-shrubby St John's wort
-podocarp
-percussion cap
-ceriman
-peanut bar
-gean
-jack
-durra
-rotor
-carob
-cottage tulip
-three-spined stickleback
-trencher
-elevator
-kalumpang
-abaca
-Australopithecus robustus
-active matrix screen
-water bed
-hatmaker
-lodestone
-cat food
-overcup oak
-balletomane
-popgun
-rheometer
-process cheese
-frog legs
-heartleaf arnica
-p-n-p transistor
-steam turbine
-Tulu
-scalene triangle
-licorice fern
-coffee break
-trade unionist
-starved aster
-firing pin
-water gum
-Masonite
-hairspring
-seminarian
-blue racer
-forecastle
-scrub pine
-Atlantic spiny dogfish
-kopje
-orphrey
-fan tracery
-gee-gee
-vixen
-interstellar space
-Harris Tweed
-sawmill
-lemon mint
-bitewing
-ringlet
-Chinese mustard
-paleontologist
-American hazel
-brigantine
-clay-colored robin
-zombie
-nectarine
-West Indian jasmine
-pineapple weed
-rusher
-gynecologist
-pole
-thylacine
-myrtle beech
-golden cup
-woodruff
-T-bar lift
-terebinth
-service club
-homegirl
-Blue Mountain tea
-figwort
-New Hampshirite
-Stayman
-tonometer
-white turnip
-messuage
-cruet-stand
-colliery
-connecting room
-lesser twayblade
-bland diet
-crown prince
-beggarwoman
-restharrow
-bower actinidia
-firebug
-hepatic tanager
-telegraph
-Spodoptera frugiperda
-spackle
-carpenter's square
-pyx
-supermom
-thickhead
-whorled milkweed
-Arctic char
-Chinese rhubarb
-pince-nez
-wolverine
-tomato concentrate
-cascarilla bark
-red underwing
-leather flower
-Jerusalem thorn
-bullpen
-Salisbury steak
-anode
-coffeeberry
-bottling plant
-fritter batter
-aerial torpedo
-matrix
-local oscillator
-stalked puffball
-bruin
-three-cornered leek
-wassail
-stabling
-damping off fungus
-myriapod
-osier
-lesser kudu
-cownose ray
-chokecherry
-wagon
-obstetrician
-Glengarry
-even-pinnate leaf
-wine sauce
-osteocyte
-baker's yeast
-heir presumptive
-blackjack
-tympanist
-golden fern
-fipple
-Japanese oak
-bar mask
-stamping machine
-argus
-knobcone pine
-oil beetle
-lanai
-upper berth
-condenser
-proctologist
-catechu
-wild spurge
-vestry
-ground snake
-proton accelerator
-walker
-scarlet bush
-transom
-lagging
-bouillon
-slender loris
-black currant
-developer
-football hero
-plum sauce
-striped mullet
-prince charming
-fictional animal
-prosimian
-lug wrench
-lemonwood
-kirsch
-spy satellite
-black caraway
-Thompson Seedless
-bead tree
-purple fringeless orchid
-Virginia strawberry
-chigetai
-punkie
-gall wasp
-addressing machine
-rock polypody
-good-king-henry
-spring cankerworm
-wimple
-noncandidate
-saskatoon
-hacienda
-Darjeeling
-snowberry
-lounging pajama
-ascospore
-ski-plane
-hedgehog cereus
-Welsh onion
-yautia
-coaster brake
-sickle cell
-parrot's beak
-fuller's teasel
-painted greenling
-scablands
-stuffed cabbage
-barrel organ
-etcher
-dwarf maple
-camp
-Australian blacksnake
-currycomb
-obtuse triangle
-rose gum
-psychrometer
-abridger
-torpedo
-carpet loom
-sodalist
-slender rush
-loligo
-sclerometer
-wimp
-dotted gayfeather
-green ash
-pinstripe
-moralist
-medusa's head
-garden centipede
-heath aster
-fool's parsley
-olla podrida
-Potawatomi
-Edam
-toothache tree
-hulk
-seabag
-narthex
-compartment
-prairie star
-lookdown
-B-flat clarinet
-event planner
-clip lead
-shirting
-milk punch
-supercharger
-macadamia nut
-giant coreopsis
-computer store
-martingale
-keyboard buffer
-summer flounder
-squash ball
-gas turbine
-object ball
-plier
-black mulberry
-reef squirrelfish
-scampi
-willow aster
-bowler
-striped marlin
-smooth muscle cell
-diplodocus
-Liberty ship
-sponge cloth
-guitarfish
-walking leaf
-showroom
-California bluebell
-bolo
-turnbuckle
-boysenberry
-hardware
-Gael
-imago
-endorser
-jujube
-dust bag
-rapporteur
-field wormwood
-low-water mark
-naval missile
-Pacific yew
-reversible
-crabapple jelly
-poniard
-barricade
-spawner
-simnel
-seltzer
-deckle edge
-needle
-timbale
-satellite transmitter
-organization man
-job candidate
-orderly
-native cranberry
-fir clubmoss
-coaming
-chartered accountant
-electron accelerator
-Sierra plum
-American foxhound
-long underwear
-Penobscot
-blueberry yogurt
-biretta
-cascara
-Paranthropus
-Dorian
-nun's habit
-lenten rose
-Augustinian
-designer
-northern phalarope
-mombin
-hazel mouse
-reeve
-waffler
-telegraphy
-Verpa conica
-ignition coil
-Japanese oyster
-S-shape
-divining rod
-ant thrush
-throat protector
-interlocutor
-Desmodus rotundus
-pere david's deer
-attenuator
-Cypriot
-red sandalwood
-pendulum watch
-broadcloth
-striped drum
-sequence
-safety arch
-diapensia
-hog
-western spadefoot
-chlorella
-comb-footed spider
-Chechen
-darning needle
-C-ration
-hard beech
-piano action
-scaling ladder
-Nepal trumpet flower
-ravigote
-screw wrench
-ramekin
-Lyonnaise sauce
-dinner napkin
-partial veil
-masseuse
-coatrack
-mooring tower
-blue-eyed African daisy
-English horn
-baton
-rope tow
-toll bridge
-massage parlor
-quark cheese
-lounging jacket
-tall goldenrod
-flying jib
-coordinate axis
-barley-sugar
-integrator
-worm gear
-captain
-sweatshop
-class
-layer
-chili powder
-dripping pan
-oatcake
-newsroom
-tadpole shrimp
-rake
-trade magazine
-silks
-ram's-head
-senior
-knower
-masseur
-yam
-peg
-wheel tree
-hardbake
-test room
-long-spurred violet
-creeping spike rush
-shrapnel
-coffee senna
-matchbox
-creeping soft grass
-welder's mask
-pickaback plant
-urial
-hooded pitcher plant
-incense cedar
-Ohio buckeye
-ant cow
-skeleton fork fern
-Indiaman
-swamp ash
-testatrix
-marang
-spherocyte
-Winesap
-Indian mallow
-teju
-Yersinia pestis
-dye-works
-sauerbraten
-coral bean tree
-safe house
-postulator
-eyas
-lotus
-wood vise
-lady-of-the-night
-East German
-cymling
-rock candy
-western omelet
-anoa
-rainbow seaperch
-crossover voter
-Finn
-tree shrew
-hog plum
-Federal
-shagbark
-clockwork
-Alexandrian laurel
-metal wood
-brill
-military chaplain
-trend-setter
-call-back
-Indian rat snake
-spurred gentian
-Japanese maple
-forest goat
-bee moth
-viola da braccio
-duckboard
-armyworm
-hangnail
-counterbore
-cream-of-tartar tree
-Mullah
-bonbon
-water hazard
-temple orange
-corporatist
-rough bindweed
-Turkish bath
-mistletoe fig
-beach sand verbena
-caddisworm
-English plantain
-brown Betty
-power pack
-lion's-ear
-Francis turbine
-stayer
-dichondra
-marsh St-John's wort
-squab
-energizer
-common horehound
-mantispid
-pullback
-handwheel
-spark arrester
-yakuza
-Virginian witch hazel
-grunter
-waterworks
-bondwoman
-chain printer
-stockjobber
-coconut milk
-yardgrass
-blue chip
-bridle path
-riser
-pleurothallis
-saltwort
-salal
-broadside
-blackboard eraser
-bastard
-Para rubber tree
-red bat
-digital-analog converter
-calabash
-cashier
-cow shark
-horned pout
-microphage
-monologist
-woolly monkey
-Illinoisan
-marsh horsetail
-distaff
-siris
-eparch
-gooseneck loosestrife
-sounding rocket
-multiprocessor
-saiga
-xerographic printer
-madrona
-right triangle
-sweet gale
-red maids
-wolfsbane
-pork-and-veal goulash
-French sorrel
-mutterer
-Venetian sumac
-drumlin
-white crappie
-squire
-large-flowered calamint
-northern cricket frog
-mushroom sauce
-supertanker
-morello
-auxiliary boiler
-Virginia thimbleweed
-cottage tent
-bubble shell
-big shellbark
-wormwood sage
-cider gum
-coast lily
-American feverfew
-Peruvian balsam
-purple silkweed
-tobacco moth
-desk dictionary
-rock elm
-eastern indigo snake
-Japanese privet
-lamb
-levee
-L-plate
-soapfish
-painted tongue
-scuttle
-markhor
-Marburg virus
-mackinaw
-major
-crypt
-ball and chain
-domestic silkworm moth
-bottom feeder
-mistress
-death house
-freight elevator
-bellyband
-Pulex irritans
-Bacillus anthracis
-fire control radar
-hysterosalpingogram
-turbogenerator
-decompound leaf
-vambrace
-scentless camomile
-Medinilla magnifica
-prima ballerina
-Northern Spy
-quartz lamp
-grains of paradise
-justiciar
-felt fern
-seismograph
-Madagascar jasmine
-imaret
-white perch
-Alpine mouse-ear
-tea bread
-yellow bass
-poseuse
-espionage agent
-punching bag
-eurypterid
-orange sneezeweed
-banded stilt
-armhole
-postern
-mother
-kapuka
-catechumen
-Soubise
-Sauvignon blanc
-gunnery sergeant
-self-starter
-ceratozamia
-Atlantic cod
-Reoviridae
-blood cup
-horseshoe bat
-oriental plane
-voussoir
-fetterbush
-samara
-truncated pyramid
-lingcod
-athenaeum
-shyster
-Carolina hemlock
-submarine torpedo
-floating fern
-yataghan
-sun tea
-viola d'amore
-conenose
-ventilation shaft
-walk-up apartment
-saury
-wild wheat
-porcupine ball
-tahini
-kris
-grass fern
-drip pan
-black bryony
-Scotch broth
-tapioca pudding
-southwestern toad
-Hare Krishna
-guimpe
-wild madder
-megalocyte
-teaching fellow
-shrubby penstemon
-lesser wintergreen
-privet hedge
-Fahrenheit thermometer
-stern chaser
-prickly ash
-pump room
-ricer
-chicken mousse
-wing commander
-sun gear
-bolus
-alpine milk vetch
-opera cloak
-twinjet
-Goldie's fern
-abnegator
-alphabet soup
-node
-grape jelly
-early coral root
-Tarzan
-quarterstaff
-greeter
-Eurasian woodcock
-primary coil
-quirt
-tinkerer
-bolt
-creme de fraise
-voltage regulator
-news photography
-Jat
-bristly locust
-Gouda
-dickey
-lobster butter
-dwarf flowering almond
-fagot stitch
-Reform Jew
-ostrich fern
-bathyscaphe
-purple mullein
-alpaca
-civic leader
-jellaba
-Arizona ash
-wasabi
-Irishwoman
-choke
-stockinet
-religionist
-sewage disposal plant
-bittersweet
-Hyphantria cunea
-pheasant under glass
-screen actor
-chapterhouse
-quoit
-horseshoe bat
-rapper
-cupule
-planetary gear
-cascade penstemon
-redoubt
-salt
-areaway
-megalomaniac
-bush willow
-amethystine python
-plains spadefoot
-colour supplement
-kick pleat
-bell apple
-narwhal
-slippery elm
-stenograph
-baa-lamb
-quadrant
-balker
-jobcentre
-spit curl
-bastard indigo
-malacca
-serow
-adobe lily
-yacca
-palestra
-penalty box
-scrub beefwood
-reenactor
-screening
-white bryony
-alderleaf Juneberry
-harpoon
-alpine clubmoss
-neurosurgeon
-surrey
-sweet calabash
-Scotch laburnum
-coquille
-French honeysuckle
-extrados
-pipe cleaner
-southwestern white pine
-Virginian stock
-scaly lentinus
-aileron
-carob bar
-swordfish
-Alpine woodsia
-negus
-wireworm
-sweep
-goldfields
-drop arch
-European bream
-roly-poly
-pin
-bastard wing
-fustian
-wild buckwheat
-lake whitefish
-overcoat
-water filter
-Bermuda chub
-New Zealand spinach
-high-hat cymbal
-European larch
-radiologic technologist
-fine-tooth comb
-brunch coat
-splice
-electronic converter
-overmantel
-extern
-taper
-cluster bomb
-teletypewriter
-pinwheel
-trailing arbutus
-quipu
-creeping zinnia
-orange milkwort
-tabard
-Australopithecus africanus
-melancholy thistle
-insole
-courser
-darkroom
-surface-to-air missile system
-bark-louse
-Confederate
-neritina
-clip-on
-spouter
-trench knife
-outside caliper
-dhak
-Limburger
-chuck wagon
-buttercup squash
-shirtdress
-pouter pigeon
-dirty old man
-zodiac
-fennel flower
-mother figure
-appointment
-Manichaean
-lignum
-bouffant
-rum sling
-Ravenna grass
-hibachi
-gin rickey
-American harvest mouse
-cocozelle
-western wheatgrass
-black crappie
-rhombus
-Missouri goldenrod
-barndoor
-wild mango
-pneumococcus
-Boston lettuce
-ratline
-desert holly
-cobweb
-fluoroscope
-ethnologist
-tor
-bullshot
-stockade
-greave
-rock sea bass
-slip-joint pliers
-taxi dancer
-schizophrenic
-zill
-creme de menthe
-orange-blossom orchid
-divot
-supplejack
-busybody
-casemaking clothes moth
-ramrod
-gearbox
-birdcall
-Wiffle
-thwart
-beauty consultant
-chicken paprika
-trawl
-skep
-spirometer
-hopper
-kvass
-doggie bag
-bath chair
-showy daisy
-wild tamarind
-Tarsius syrichta
-glyptics
-Algerian
-cargo area
-bunk
-Velveeta
-iconoclast
-clinch
-New Caledonian yew
-false mallow
-Japanese tree lilac
-convex polyhedron
-water boatman
-cruise missile
-finisher
-colonoscope
-cumin
-wickiup
-saccharin
-whipcord
-trailer camp
-eryngo
-cuckold
-yam bean
-fighting chair
-forewoman
-galingale
-citron
-positivist
-four-lined plant bug
-suet pudding
-field pea
-Circaea lutetiana
-deer grass
-trap-door spider
-common corn salad
-mirror carp
-sounder
-second-in-command
-seaside alder
-burgoo
-ming tree
-curry sauce
-courbaril
-green alder
-figure loom
-fauld
-halfbeak
-squelch circuit
-cladode
-winter cress
-tongue and groove joint
-dwarf dandelion
-joss house
-western buttercup
-welted thistle
-potato tree
-anglewing
-cookfire
-marzipan
-hood latch
-seed shrimp
-common moonseed
-toasting fork
-bevel
-three-quarter binding
-midwife toad
-stage director
-Pentecostal
-technical sergeant
-golden-beard penstemon
-drunk
-silky oak
-corn gluten feed
-T-square
-stoker
-selling agent
-cruse
-server
-rope-a-dope
-bicorn
-matzo meal
-wide wale
-roadblock
-false foxglove
-tuck box
-bandsman
-smoke bush
-machinist's vise
-Highlander
-scholiast
-self-starter
-Swedish rye bread
-spark transmitter
-maverick
-maquiladora
-cabinetmaker
-compress
-rainbow shower
-huntsman's horn
-mackinaw
-copper rockfish
-lappet
-nitrate bacterium
-telephone plug
-soutache
-Dacron
-toboggan
-sissoo
-yogi
-laurel-tree
-vice chancellor
-Christ's-thorn
-cartridge fuse
-serial port
-quassia
-tarweed
-pecopteris
-beggarweed
-anchovy pear
-bookbindery
-woodland oxeye
-toad rush
-sandalwood tree
-marsh andromeda
-Tyrian purple
-boothose
-tragedienne
-fragrant cliff fern
-festoon
-bondwoman
-melancholic
-butternut squash
-exhaust valve
-semi-skimmed milk
-glowworm
-Virginia oyster
-Identikit
-ayah
-gallows tree
-Carioca
-monoplane
-jewels-of-opar
-scallop
-moth miller
-marsh cress
-lobed spleenwort
-ricotta
-emitter
-arame
-tub gurnard
-army attache
-maniac
-organizer
-pheasant's-eye
-Melba toast
-homeboy
-Bavarian cream
-Maximilian's sunflower
-backstop
-Tremella foliacea
-yellow avens
-spreading fleabane
-plumb level
-false rue anemone
-zabaglione
-climbing maidenhair
-doeskin
-walking shoe
-lancewood
-material
-jacksnipe
-South American poison toad
-agonist
-hinny
-paper mill
-psychophysicist
-valley girl
-toast mistress
-jorum
-tiler
-chicken Tetrazzini
-trivet
-grasshopper
-three-mile limit
-kink
-kiang
-pole horse
-jig
-Cornish heath
-hedge thorn
-false alumroot
-Popper
-remount
-photojournalist
-sideroblast
-stonecress
-Agave tequilana
-Japanese lilac
-hawse
-maenad
-air bag
-leaf spring
-dwarf willow
-soda cracker
-contralto
-moleskin
-pilaster
-Audubon's caracara
-pia
-American organ
-bleu cheese dressing
-betel palm
-PC board
-almond willow
-socializer
-tone arm
-stammerer
-free-liver
-scaler
-Gentianopsis crinita
-leak
-black haw
-hound's-tongue
-grass pea
-Stassano furnace
-coralbells
-ministrant
-perihelion
-Luxemburger
-powder-post termite
-arboreal salamander
-cushion flower
-foramen magnum
-pyrethrum
-poacher
-woolly mammoth
-horned chameleon
-tearaway
-father-figure
-tufted gentian
-salmi
-finger millet
-physa
-registrar
-polyoma
-bamboo shoot
-matchlock
-seine
-congress boot
-bulgur pilaf
-monosodium glutamate
-Kentucky wonder
-mycologist
-kedgeree
-ragweed pollen
-boarfish
-yellow pimpernel
-tan
-northern Jacob's ladder
-macrobiotic diet
-migrant shrike
-big-cone spruce
-colonialist
-white dogtooth violet
-bath asparagus
-webbing clothes moth
-ladies' room
-experimenter
-prairie bird's-foot trefoil
-bootleg
-cognitive neuroscientist
-fire chief
-flagfish
-dendrite
-stinking goosefoot
-fore edge
-hogfish
-Spanish cedar
-hotel-casino
-Tory
-life-support system
-pea flour
-cash bar
-Chenin blanc
-white-footed mouse
-Canada garlic
-salt-rising bread
-roomette
-mastodon
-bell founder
-long iron
-bi-fold door
-fig-bird
-European water shrew
-dyer's weed
-frog orchid
-allosaur
-Florida yew
-wild potato vine
-crape fern
-flat-topped white aster
-klebsiella
-oil heater
-waxmallow
-enjoyer
-mesocarp
-semidesert
-senior vice president
-coccidium
-burrawong
-syllabub
-jump suit
-harrier
-leaf roller
-cherrystone
-cinchona tree
-touring car
-eulogist
-air force officer
-red goosefoot
-cat thyme
-smoothbore
-slugger
-cardiac monitor
-cobber
-blister rust
-musicologist
-rolled biscuit
-Braun's holly fern
-hog plum
-nonpasserine bird
-pascal celery
-damson
-Jonathan
-Sheraton
-cohune palm
-egg white
-baton
-sixth-former
-Siberian pea tree
-choanocyte
-wineskin
-auditor
-detention home
-Leichtlin's camas
-Chartreuse
-clusia
-club car
-wattle and daub
-security blanket
-common American shad
-assistant professor
-marsh pea
-camomile tea
-gopher hole
-gravure
-Freudian
-spirillum
-maharani
-equilateral
-crow garlic
-mammee apple
-felwort
-hardtop
-dillenia
-curlycup gumweed
-pilot engine
-calcimine
-wooly lip fern
-bitter dock
-wineberry
-jumper
-monolingual
-spinning frame
-old-timer
-native cat
-diving petrel
-sodium-vapor lamp
-marchand de vin
-sexton
-matelote
-interior designer
-windfall
-mole salamander
-minder
-bodkin
-neutron bomb
-Caloscypha fulgens
-slinger ring
-mezzo-soprano
-aura
-Southern Baptist
-viscacha
-midfield
-tie
-prosthetist
-round-headed leek
-yellow mariposa tulip
-canary grass
-staddle
-Tokay
-Muenster
-brazil nut
-California black walnut
-applesauce
-penologist
-virgin's bower
-tenon
-steward
-Jerusalem oak
-red-bellied snake
-bindery
-scow
-fluid flywheel
-bullhead
-satinleaf
-clove
-double glazing
-matron
-wild parsnip
-winged elm
-shoot-'em-up
-musk deer
-white rust
-lock
-Cornishman
-Vidalia onion
-corn spurry
-freeloader
-justice of the peace
-inlay
-myxobacteria
-tiglon
-tangram
-German ivy
-scented fern
-woolly daisy
-caretaker
-gastroscope
-scuppernong
-spotted sunfish
-guilloche
-codling
-wormcast
-Eskimo curlew
-tayra
-European fly honeysuckle
-septuagenarian
-third gear
-coatee
-red alder
-water ice
-cubitiere
-frame buffer
-gamboge tree
-pernyi moth
-chicken Marengo
-Galliano
-Lincoln
-true sago palm
-hunter's sauce
-carpet beater
-alpine goldenrod
-arch support
-vehicle-borne transmission
-jilt
-paternoster
-redcap
-Siberian larch
-hoary plantain
-swan's down
-chicane
-reverse
-divan
-kneeler
-alexic
-mock turtle soup
-daffodil garlic
-mission bells
-squilla
-ursinia
-winter's bark
-trifoliate orange
-discina
-frijole
-Swiss steak
-maildrop
-knotgrass
-dog fennel
-drum sander
-heroin addict
-costume
-camber arch
-shining willow
-lutefisk
-red porgy
-microfossil
-good old boy
-angle bracket
-pitcher sage
-bordelaise
-heat exchanger
-carrion
-bush jacket
-fanjet
-coach
-blackface
-sicklepod
-Manhattan clam chowder
-daisywheel printer
-olive
-Sphacelotheca
-Spanish needles
-brown root rot fungus
-boudoir
-encyclopedist
-V-8 juice
-red haw
-brass buttons
-gym suit
-skywalk
-water wagon
-gas-turbine ship
-stoup
-lisle
-sailor suit
-box beam
-balm of gilead
-housemaster
-hayrack
-neutralist
-water elm
-brook thistle
-doyenne
-nark
-alpha-tocopheral
-WASP
-hydrilla
-water-shield
-footlocker
-variola major
-pargeting
-ion engine
-yellow globe lily
-Malecite
-bloodleaf
-yellow sand verbena
-whorled loosestrife
-packinghouse
-Carolina parakeet
-Virginia waterleaf
-armband
-red rockfish
-factory ship
-moon trefoil
-jump seat
-water gillyflower
-yerba mansa
-chamfer bit
-compass saw
-hopsacking
-Indian rhododendron
-sickbed
-treacle
-honey eater
-mailsorter
-seabeach sandwort
-sob sister
-primrose jasmine
-prince consort
-elocutionist
-wishing cap
-runner
-trestle
-sugar water
-half-and-half dressing
-fringed poppy mallow
-portiere
-bung
-swan orchid
-weather satellite
-beef broth
-marblewood
-sapper
-agitator
-wren-tit
-grade
-allspice tree
-spacewalker
-American hornbeam
-sieva bean
-dill seed
-potoroo
-love-in-winter
-alembic
-Cheshire cheese
-small white aster
-Oregonian
-flipper
-twill
-differential gear
-Prince Albert
-licorice
-foster-father
-Melkite
-portraitist
-Yosemite toad
-Cox's Orange Pippin
-slender wheatgrass
-knob
-silique
-Rocky Mountain bee plant
-stirrup pump
-chicken hawk
-sweetbrier
-Sierra lodgepole pine
-poulette
-biohazard suit
-striated muscle cell
-Geiger counter
-World Wide Web
-turmeric
-prairie wake-robin
-latchet
-pushball
-grill
-shooting lodge
-floating-moss
-refried beans
-boojum tree
-red poll
-toothbrush tree
-rabbiteye blueberry
-red haw
-sweet vetch
-delta
-upland cotton
-ballet mistress
-padrone
-complementary color
-great Solomon's-seal
-bud brush
-brandy sling
-spinster
-Andorran
-Mojave aster
-mackinaw
-golden calla
-bottom rot fungus
-segmental arch
-periwinkle
-hellion
-topknot
-copper
-Mexican hyssop
-weeping love grass
-point woman
-pathogen
-fall cankerworm
-common shiner
-silverspot
-corer
-atomic pile
-crystal detector
-yellow spot fungus
-truncated cone
-saprobe
-variegated horsetail
-Cro-magnon
-cercaria
-aglet
-pollster
-oyster bed
-pancake turner
-egg cream
-sporozoite
-quirk molding
-mutisia
-sound bow
-physic nut
-sugar-bush
-cow
-magnetron
-jungle hen
-brassie
-rock bit
-taco sauce
-seeded raisin
-desert selaginella
-folding door
-vinegarroon
-Pinot blanc
-rye
-ellipsoid
-betel nut
-tree of knowledge
-ambrosia
-long tom
-breechloader
-bicolor lespediza
-cosmetician
-monoblast
-American oil palm
-prancer
-farina
-caiman lizard
-hardball
-bullock's heart
-cotton rat
-whiting
-weather ship
-sharecropper
-creamcups
-gas bracket
-divinity
-ornithologist
-yellow twining snapdragon
-showy goldenrod
-end man
-heptagon
-sand dropseed
-round file
-guama
-blue elder
-sand spurry
-raccoon dog
-zigzag goldenrod
-fast reactor
-arctic willow
-cyclopean masonry
-punter
-sgraffito
-slattern
-storage ring
-clipper
-pulasan
-short-tailed shrew
-scammony
-daybook
-umbrella tree
-coloring
-element of a cone
-gesneriad
-cane
-burgoo
-western coral snake
-friendship plant
-Leydig cell
-scrutineer
-hairy golden aster
-inclined fault
-water milfoil
-bryozoan
-nardoo
-native pomegranate
-curly grass
-Florence fennel
-resurrection plant
-ice water
-crown
-ploughman's lunch
-clustered lady's slipper
-kitchenette
-sand sedge
-pouched mouse
-roadbed
-parsley haw
-predecessor
-super heavyweight
-seedless raisin
-mailbag
-sparling
-codling moth
-squama
-Bercy
-thermoelectric thermometer
-Jaculus jaculus
-saltpan
-firmer chisel
-round whitefish
-ramrod
-criollo
-pinch bar
-slash pocket
-thigh pad
-velvet plant
-intergalactic space
-brazilian ironwood
-whaleboat
-sirrah
-hanging fly
-aspirator
-Dominican
-dribbler
-yellow-eyed grass
-Cornish
-geophysicist
-tarmacadam
-marchioness
-rattlesnake orchid
-Alaska Native
-ilama
-myrrh tree
-zucchini
-licorice root
-nosebag
-lounger
-troposphere
-virginal
-spaghetti Western
-Virgin Mary
-waterwheel plant
-dry nurse
-enate
-carpet shark
-rijsttaffel
-stuffing nut
-caraway seed bread
-Leotia lubrica
-kaffiyeh
-Boston baked beans
-halophyte
-backscratcher
-instillator
-trefoil arch
-pip
-digitizer
-dosemeter
-Carolinian
-French sorrel
-boards
-historian
-rangpur
-clansman
-goral
-leatherjacket
-coiner
-fleece
-white globe lily
-storm cellar
-roundhouse
-mediatrix
-butterfly flower
-swamp gum
-prairie vole
-rhizomatous begonia
-common tobacco
-Marco Polo sheep
-subarachnoid space
-broomweed
-safety net
-silky wisteria
-swagger stick
-spectacled caiman
-derris root
-soap pad
-chop-suey greens
-summer hyacinth
-palo santo
-carbohydrate loading
-chinch bug
-roadman
-sheep plant
-messiah
-desk officer
-banquette
-drugget
-trumpet arch
-great duckweed
-purdah
-heartbreaker
-hasty pudding
-alligator weed
-dragee
-yellow bristlegrass
-Jacob's ladder
-campstool
-coffee fern
-sweet fern
-little chief hare
-cat-o'-nine-tails
-rep
-American red elder
-divorcee
-black salsify
-cambric
-sennit
-Canada ginger
-wonderer
-Formica
-cream-colored courser
-zooid
-European beggar-ticks
-sorrel tree
-piddock
-blolly
-red-flowered silky oak
-bay
-Hooker's onion
-dark horse
-cone clutch
-Roman hyacinth
-paintbox
-mestiza
-green alder
-bill
-panicled aster
-mammogram
-snuffbox fern
-Rediffusion
-swamp fly honeysuckle
-stoup
-psychiatrist
-nodding groundsel
-student union
-cold duck
-bee beetle
-playbox
-Psychopsis krameriana
-nosh-up
-earthnut
-narthex
-single-rotor helicopter
-revetment
-sweetleaf
-seasoned salt
-piculet
-speckled alder
-mackerel scad
-common yellowwood
-devisee
-static tube
-Spanish heath
-umbrella plant
-fucoid
-Chilean
-coral-root bittercress
-fanatic
-cachou
-agony aunt
-bird's-foot fern
-washwoman
-torchbearer
-placoderm
-frosted bat
-spicemill
-Cape lobster
-hard-shell crab
-colonizer
-camphor daisy
-friar's-cowl
-false tamarisk
-toggle joint
-tinsmith
-theorist
-hydrologist
-loganberry
-universal donor
-northern whiting
-tent-caterpillar moth
-russet
-kangaroo mouse
-African scented mahogany
-bastinado
-breast implant
-betel
-grade separation
-vox humana
-stodge
-Maryland chicken
-Anguillan
-oil pump
-governor's plum
-narcissist
-deadwood
-private citizen
-winker
-ropewalker
-gidgee
-Lothario
-ski resort
-major-domo
-von Neumann machine
-belaying pin
-water parsnip
-Fissipedia
-luggage carrier
-spring water
-oyster stew
-kohl
-celesta
-date-nut bread
-punchboard
-sunniness
-hospital train
-man
-rack and pinion
-mixer
-pousse-cafe
-narrow goldenrod
-Maxim gun
-stiff
-recruiting-sergeant
-watch glass
-white hellebore
-tung tree
-prairie white-fringed orchid
-beef Stroganoff
-scoffer
-grassy death camas
-Shawnee cake
-tapioca
-Short's aster
-banker
-laparoscope
-honeyflower
-Caterpillar
-electric clock
-baling wire
-huntress
-Surinam toad
-art school
-incurable
-Canton crepe
-apple juice
-hipline
-bronchoscope
-marshmallow fluff
-Texan
-wild fig
-sawed-off shotgun
-forestay
-red kauri
-fish slice
-Egyptian grass
-English walnut
-brown sauce
-ogee arch
-nectary
-chambray
-leather flower
-phloem
-Persian violet
-bomb calorimeter
-western narrow-mouthed toad
-soup du jour
-sickle alfalfa
-caracolito
-periscope
-coralberry
-sword bean
-sigmoidoscope
-water locust
-hygrodeik
-sycamore
-sheikdom
-ballistocardiograph
-clove
-akee
-fucoid
-jacquard
-cat's-ear
-puritan
-slender wild oat
-smooth softshell
-purchasing agent
-landing craft
-chartist
-lace bug
-sharksucker
-Virginia chain fern
-horseradish
-namer
-ripcord
-personage
-aspirin powder
-puku
-Wankel engine
-nightcap
-velvet bent
-roridula
-cytogeneticist
-olm
-almond extract
-common heath
-fringe-toed lizard
-Kentucky yellowwood
-lithosphere
-cramp
-bulgur
-scurvy grass
-officer's mess
-frigate
-electroscope
-giant chinkapin
-opah
-rutabaga
-wood hoopoe
-Farley maidenhair
-shingle tree
-argentine
-router
-palm nut
-quillwort
-hiba arborvitae
-runcible spoon
-hireling
-sickbay
-alpine totara
-white lupine
-Cotoneaster horizontalis
-desert plume
-staghound
-Sea Scout
-opalescence
-enophile
-Jersey elm
-coal house
-Helvella acetabulum
-selenium cell
-white camas
-creole-fish
-auger
-fragrant agrimony
-research center
-achromia
-shank
-cottonseed
-mod con
-extension
-sugar beet
-winter flounder
-silky dogwood
-strop
-tokamak
-rabbit ears
-baby farmer
-fireman's ax
-serration
-taproot
-socket wrench
-action officer
-Chilean jasmine
-Greek fire
-stem-winder
-body louse
-lumpsucker
-stink bomb
-American lady crab
-dicer
-lie detector
-maneuverer
-black-headed snake
-tiger moth
-shooting stick
-spermatid
-babushka
-deaconess
-home
-prior
-chanfron
-chickasaw plum
-big-eared bat
-rusty woodsia
-tertigravida
-miniver
-combretum
-habit
-bluehead
-angled loofah
-gipsywort
-fire-on-the-mountain
-purple milk vetch
-alpine gold
-merozoite
-loddon pondweed
-Uniat
-provost marshal
-Gyromitra fastigiata
-Coigue
-proconsul
-oarfish
-San Jose scale
-filature
-chimney plant
-spiny softshell
-bluecoat
-live axle
-river limpet
-clever Dick
-pink bollworm
-Japanese plum
-roarer
-caricature plant
-wardroom
-Texas chachalaca
-Bahia grass
-Moreton Bay tulipwood
-accessory fruit
-pearl barley
-ashcake
-bunt
-Polynesian tattler
-pine fern
-laughing owl
-potato fern
-speaking trumpet
-adjoining room
-bearing rein
-banana quit
-redbrick university
-Scleroderma bovista
-magdalen
-pressurized water reactor
-advisee
-NIMBY
-poorwill
-almond moth
-comedian
-star tulip
-cracked wheat
-water pump
-guest of honor
-yellow-breasted bunting
-hire
-pedate leaf
-augur
-purple locoweed
-Socinian
-upland white aster
-guesthouse
-double reed
-detention basin
-rollmops
-hitch
-bodega
-mayeng
-sparkplug wrench
-attack dog
-peach melba
-heliozoan
-tower mustard
-blue mold fungus
-lamplighter
-banded sand snake
-smooth crabgrass
-elsholtzia
-bodkin
-Aegean island
-bag lady
-alewife
-arcella
-electrical contact
-common ax
-animist
-concave polyhedron
-coalface
-climbing perch
-yellowtail
-hobble skirt
-marquee
-Russian dandelion
-snow mushroom
-polo ball
-NADA daiquiri
-cormous plant
-chaparral mallow
-inside caliper
-milking stool
-fallout shelter
-sea gooseberry
-Danish blue
-grissino
-chimney breast
-mosquito fern
-soundbox
-spring chicken
-epauliere
-cape forget-me-not
-japan
-saddle oyster
-white fritillary
-push-button radio
-bladder senna
-bladder stone
-macedoine
-moire
-Shawnee
-starnose mole
-douroucouli
-horseradish sauce
-electron gun
-cotter
-console
-park commissioner
-free press
-lump sugar
-western poison oak
-apple maggot
-keurboom
-lisper
-griffon
-burin
-horseshoe whipsnake
-Jacobean lily
-spinner
-cochineal insect
-emesis basin
-sowbane
-humanitarian
-uakari
-three-dimensional radar
-wild hollyhock
-heartseed
-swinger
-two-by-four
-mop handle
-common amsinckia
-traitress
-rush aster
-fibrous-rooted begonia
-violet-flowered petunia
-milliammeter
-alidade
-azure aster
-celery seed
-snorer
-scarlet plume
-obtuse leaf
-heathen
-rose chestnut
-headrace
-dwarf buckeye
-Pacific tripletail
-wiggler
-bounty hunter
-Lowlander
-slate pencil
-typist
-syconium
-vaquita
-skybox
-business lunch
-gusher
-curacao
-palometa
-Diapsida
-light diet
-sourdine
-thorny amaranth
-potato fern
-cartridge extractor
-peshmerga
-chaffweed
-tahoka daisy
-hematologist
-massage parlor
-diverging lens
-breadroot
-papyrus
-amarelle
-cover plate
-hubbard squash
-cryptomonad
-whitetail prairie dog
-rabbit burrow
-orthochromatic film
-goncalo alves
-Chile bonito
-tent-caterpillar moth
-Manila grass
-buck sergeant
-mustard seed
-crested wheatgrass
-wise guy
-asarabacca
-field pea
-bite plate
-barbasco
-heart-lung machine
-mouse-eared bat
-piping guan
-gun pendulum
-climbing onion
-fungus gnat
-Livonian
-one-hitter
-Chilean firebush
-Sonoran whipsnake
-round scad
-myelogram
-Rhodes grass
-vomitory
-roble beech
-South-African yellowwood
-molasses
-Velcro
-common calamint
-radiation pyrometer
-sketcher
-chaparral pea
-coffee stall
-Australian nettle
-bilimbi
-Khedive
-visionary
-field spaniel
-devilwood
-collimator
-Siberian spruce
-sling
-limestone salamander
-ribbon worm
-hazel
-petter
-coolant system
-artillery plant
-bailiff
-chameleon tree frog
-microsporophyll
-maiden blue-eyed Mary
-Drosophyllum lusitanicum
-cocozelle
-king post
-nailer
-knobkerrie
-tovarich
-Intelnet
-worm lizard
-drop forge
-wool grass
-brown bullhead
-anthropoid
-vitamin A2
-creche
-hickory nut
-whiffletree
-deipnosophist
-Muskhogean
-masochist
-hypsometer
-gliricidia
-complexifier
-wild licorice
-reconnaissance vehicle
-fives
-beefsteak plant
-eastern dasyure
-bookworm
-crested coral root
-wire recorder
-cinnamon vine
-bubble
-Newfoundland dwarf birch
-spruce bark beetle
-teetotaler
-fad diet
-ascus
-spicebush
-African coral snake
-soft-shell crab
-Postum
-packhorse
-sand cherry
-cricket-bat willow
-middlebrow
-Hungarian sauce
-buffalo clover
-jimsonweed
-latanier
-stablemate
-jumper
-zoospore
-smooth woodsia
-flowering ash
-unilateralist
-lomatia
-flapper
-wild cotton
-Siberian wall flower
-probe
-bankrupt
-blockade
-lemon geranium
-fig leaf
-basic point defense missile system
-clack valve
-buttinsky
-ingenue
-mountain everlasting
-zebra-tailed lizard
-shaving-brush tree
-evergreen huckleberry
-core drill
-lugworm
-Cashmere goat
-doorjamb
-minelayer
-student center
-horsehair
-European dewberry
-white broom
-arenavirus
-eastern poison oak
-rye ergot
-Tupi
-tensiometer
-fleawort
-coquille
-icing sugar
-junior lightweight
-Doppler radar
-mahuang
-candlepin
-chambermaid
-evergreen blueberry
-Eton jacket
-parvis
-solleret
-molded salad
-malvasia
-birth-control campaigner
-nonagon
-backswimmer
-ogee
-bowstring
-salt marsh mallow
-trapezohedron
-hoary willow
-speech therapist
-Zinjanthropus
-core
-red-backed mouse
-eptatretus
-mossy saxifrage
-Aristotelian
-Thessalonian
-searing iron
-bifocals
-falangist
-field pea
-packsaddle
-lay reader
-hoecake
-cuboid
-white maire
-iceman
-lobscouse
-neckcloth
-color-blind person
-Chinese holly
-assemblyman
-white-lipped peccary
-kava
-plastron
-crab louse
-hook wrench
-trailing four o'clock
-junior
-skilly
-internet
-tonguefish
-footman
-sub-assembly
-evangelist
-track
-bench lathe
-desk clerk
-scalded milk
-chamois cloth
-American marten
-chachka
-nondescript
-pellitory-of-the-wall
-swamp candles
-procurator
-cuddy
-farkleberry
-mountain male fern
-trawl
-dual scan display
-fish meal
-prospector
-convener
-guano bat
-ant shrike
-picture rail
-sand rat
-gynophore
-quilting
-sleeper
-summer savory
-Cotoneaster dammeri
-smooth sumac
-slumgullion
-suite
-catalufa
-spherule
-lean-to tent
-gryphon
-gas shell
-short iron
-sweet sultan
-dewberry
-Victoria plum
-American water shrew
-X-ray tube
-macebearer
-green arrow arum
-abbe
-poke milkweed
-atheist
-Fosbury flop
-Ord kangaroo rat
-moldboard
-wheat germ
-explosive trace detection
-whippoorwill
-examiner
-tallyman
-Crookes tube
-wild peach
-fringed grass of Parnassus
-Crookes radiometer
-Atlantic croaker
-lobster stew
-spring cress
-maggot
-pacer
-hydra
-Zionist
-pepper tree
-diamante
-baize
-Rhodesian man
-county agent
-respecter
-Anglican
-antimacassar
-materialist
-Swan River everlasting
-cloud grass
-toll line
-C battery
-chinese mustard
-grass poly
-warming pan
-seasonal worker
-common sickle pine
-bathysphere
-elegant Habenaria
-card table
-Chilean cedar
-brocket
-collimator
-malted milk
-avadavat
-fire marshall
-coloratura
-yellow spiny daisy
-fingerstall
-narrow-leaf penstemon
-indigo broom
-pillwort
-bearberry willow
-Etonian
-certified milk
-climbing bird's nest fern
-field coil
-wrist pad
-parr
-kaoliang
-engelmannia
-stocker
-satrap
-Nantua
-spearfish
-caper tree
-gold-tail moth
-mountain chinchilla
-sea milkwort
-westerner
-army cutworm
-leaf-nosed snake
-neurobiologist
-xeranthemum
-Eastern silvery aster
-ecclesiastical attire
-caper
-Ukranian
-bight
-button fern
-peach pit
-oligodendrocyte
-maar
-digitigrade mammal
-streptobacillus
-sensitometer
-preemptor
-oat
-bell foundry
-crown lens
-rock purslane
-Junior
-Brazilian guava
-kicksorter
-Ohio goldenrod
-red mulberry
-King's Counsel
-mountain four o'clock
-fairy shrimp
-fell
-oca
-sycophant
-chantry
-dermatoglyphic
-bomblet
-keyhole saw
-hangman's rope
-little barley
-lion-jaw forceps
-giant scrambling fern
-popper
-dulcimer
-Espagnole
-tardigrade
-smooth-haired fox terrier
-bullbrier
-rewa-rewa
-Japanese poinsettia
-trunk line
-cannery
-helminth
-American spikenard
-prince's-feather
-arthroscope
-ginger
-aphakic
-pilot bit
-angle of refraction
-low-sodium diet
-wall creeper
-growler
-praetorium
-Hall of Fame
-soupfin shark
-Molotov cocktail
-kaffir boom
-stitcher
-sawwort
-flagellant
-Atlantic herring
-Reticulitermes lucifugus
-voltaic pile
-snowy orchid
-southern flounder
-skysail
-osage orange
-white mullein
-lined snake
-tolu tree
-poliovirus
-foreman
-burette
-jackass bat
-invigilator
-electromyograph
-acarus
-presence chamber
-columbian mammoth
-hyacinth bean
-pilot
-meadow jumping mouse
-Maria
-outskirts
-aftershaft
-Queensland nut
-schlockmeister
-plainsman
-afropavo
-scarlet musk flower
-five spice powder
-gunboat
-multiplex
-Dutch uncle
-louvered window
-chimney corner
-cuscus
-psalmist
-Vichy water
-signer
-amphiuma
-harmonizer
-authorizer
-naiad
-control rod
-stentor
-mountain bladder fern
-gig
-read-only memory chip
-assenter
-vixen
-hermitage
-corn dab
-locksmith
-cockspur thorn
-variable-pitch propeller
-western red-backed salamander
-dolman sleeve
-cultist
-sweet buckeye
-pine vole
-Peking man
-mountain swamp gum
-nimblewill
-bethel
-aye-aye
-lancelet
-teff
-Alpine celery pine
-endive
-nipa palm
-center of curvature
-seeder
-Sabahan
-sea scallop
-social secretary
-gorgonzola
-western chokecherry
-misanthrope
-rabbitweed
-beggarman
-button fern
-white mallee
-doodia
-mastiff bat
-roper
-prima donna
-blanc
-holding pen
-fingerling
-skyhook
-flophouse
-steam chest
-crystallized ginger
-acrocarp
-horse pistol
-true mahogany
-costmary
-ballistic galvanometer
-jaunting car
-bartonia
-rep
-mandibular notch
-bubble and squeak
-umpire
-fringed loosestrife
-bear oak
-ski jump
-staggerbush
-plumcot
-thermal reactor
-field brome
-bodkin
-jackknife-fish
-malope
-writing arm
-gold fern
-Stayman Winesap
-merlon
-eclectic
-fluxmeter
-emeritus
-imam
-drum
-pop tent
-capital ship
-subalpine larch
-flail
-Lorenzo dressing
-tomboy
-eastern woodrat
-warrantee
-Pacific spiny dogfish
-sheepshead porgy
-farthingale
-Cryptoprocta
-power loom
-communicant
-howdah
-ectomorph
-false foxglove
-basset horn
-odd-pinnate leaf
-Wisconsin weeping willow
-Queensland bottletree
-dampener
-corbel arch
-silent butler
-Circe
-town clerk
-Japanese chestnut
-bloodwood tree
-switcher
-cup hook
-spreader
-rice rat
-straightedge
-traverser
-fluid drive
-Spanish paprika
-sour milk
-poison camas
-bean dip
-card table
-vinegar fly
-vizier
-electric-discharge lamp
-purple rock brake
-dynamo
-Japanese snowbell
-Grindelia robusta
-neuroglia
-safflower seed
-coronet
-frown line
-Renaissance man
-Steller's sea cow
-book scorpion
-isosceles triangle
-arthritic
-spherical triangle
-kangaroo mouse
-garden orache
-stemless hymenoxys
-titi
-out-basket
-gent
-columnea
-mint sauce
-mouthbreeder
-Liebig condenser
-cheerer
-assegai
-stickler
-Merostomata
-dimmer
-grey poplar
-common heath
-scorzonera
-glory hole
-Blackfoot
-oil slick
-musketeer
-apple geranium
-daisyleaf grape fern
-gas furnace
-bijugate leaf
-Arabist
-star-thistle
-hand throttle
-huckleberry oak
-lift pump
-maulstick
-Rome Beauty
-Newburg sauce
-pit
-volunteer
-Baldwin
-ark
-Asian horseshoe crab
-black calla
-marlinespike
-Gentianopsid procera
-guinea gold vine
-tucker-bag
-desk sergeant
-piezometer
-migrator
-keelson
-executrix
-sackcloth
-onion smut
-buckboard
-substitute
-pudge
-mess
-cinchona
-intervenor
-gravimeter
-pederast
-censor
-gastroenterologist
-cutlassfish
-launch
-demerara
-Diegueno
-bog bilberry
-aglet
-soda fountain
-crank call
-harpoon gun
-ribbon fern
-Gurkha
-output device
-epilating wax
-greasewood
-water horehound
-return key
-fairy swallow
-spatulate leaf
-culverin
-leptocephalus
-kleptomaniac
-barley water
-bleeding tooth
-Cheyenne
-maleberry
-limber
-tapenade
-whorled aster
-toe
-revenant
-lap joint
-vein
-truant
-florest's cineraria
-morning dress
-trichodesmium
-nightshirt
-element of a cylinder
-shopaholic
-section hand
-electrodynamometer
-Guadalupe cypress
-rosebud
-racist
-avaram
-keeled garlic
-Alaska rein orchid
-orange toast
-cunner
-dipstick
-Neolentinus ponderosus
-bulbil
-charlotte
-pull-through
-header
-Manduca quinquemaculata
-persona grata
-elegist
-cafe royale
-scup
-semanticist
-wood sage
-field magnet
-tundra
-bay myrtle
-alluvial flat
-arrowleaf groundsel
-celtuce
-baryon
-must
-entrant
-othonna
-pied-a-terre
-liza
-sticky aster
-grasshopper mouse
-prison guard
-tire iron
-bomb rack
-Spanish American
-sheltered workshop
-turfing daisy
-backbone
-tangle orchid
-creeping willow
-dumb bomb
-horse cassia
-barosaur
-Yavapai
-shrimp Newburg
-peanut worm
-dwarf chinkapin oak
-corchorus
-brick cheese
-by-catch
-stover
-Urnula craterium
-clasp
-Kekchi
-alpine coltsfoot
-soybean future
-altar wine
-ripping chisel
-encephalogram
-mountain spleenwort
-transferee
-remoulade sauce
-American rock brake
-stenographer
-read/write head
-loblolly
-ground
-powdered mustard
-brake band
-sea dahlia
-freak
-proconsul
-Coffey still
-Sivapithecus
-pellitory
-palm cat
-skew arch
-American angelica tree
-vigilante
-candelilla
-andryala
-amarelle
-swiftlet
-petcock
-associate professor
-sclerite
-open circuit
-Virginia crownbeard
-Last Supper
-button tree
-scyphozoan
-margate
-mercury cell
-horsewhip
-water scorpion
-companionway
-drop cloth
-Amhara
-miraculous food
-pro-lifer
-embryologist
-Creole
-bombazine
-Indian blackwood
-cubeb
-trace detector
-gros point
-main-topsail
-meringue kiss
-spree killer
-capstone
-specimen bottle
-woolly apple aphid
-silverweed
-American barberry
-gallfly
-European bog asphodel
-northern flying squirrel
-alliterator
-Old Catholic
-heliograph
-Pteris cretica
-tippler
-pump well
-allspice
-balancer
-scarlet bugler
-lantern fly
-white prairie aster
-krummhorn
-robin's plantain
-Pacific sardine
-patty-pan
-decaffeinated coffee
-western saxifrage
-warrantee
-colorimeter
-ball bearing
-makomako
-foot
-troika
-apricot sauce
-data multiplexer
-rose-root
-sound film
-Northern dewberry
-water hickory
-swing door
-spastic
-Oligoporus leucospongia
-botulinus
-tamale pie
-Sagittarius
-muff
-spicebush
-petiolule
-pump action
-Parry's pinyon
-split-pea
-rudder blade
-princess royal
-wormseed mustard
-honey guide
-pip-squeak
-fin keel
-foretop
-cyrilla
-Navaho
-melanocyte
-deist
-silver tree
-citrus whitefly
-Morrow's honeysuckle
-green peach aphid
-longanberry
-call-board
-wild yam
-novelist
-toothed spurge
-alienee
-pond apple
-allspice
-Carolina lupine
-Jack of all trades
-white false indigo
-boiled dinner
-princewood
-sailor's-choice
-false bracken
-microbrewery
-black grama
-tutee
-brickkiln
-sea raven
-guesser
-wirework
-European lemming
-thyrse
-plains lemon monarda
-milo
-shunt
-spotted cowbane
-anchovy sauce
-grande dame
-Maryland golden aster
-Chinese puzzle
-boarfish
-burweed marsh elder
-defense contractor
-nitric bacteria
-Belgian hare
-beach plum
-conformal projection
-sand fly
-steering linkage
-quickset
-Mahayanist
-Geiger tube
-loudmouth
-Lancastrian
-brownie mix
-ex-spouse
-deltoid leaf
-Shasta salamander
-rabbet joint
-purple anise
-garibaldi
-gebang palm
-bladderpod
-Host
-great bowerbird
-string cheese
-spinning jenny
-drift net
-matriarch
-guar
-bitter betch
-panda car
-mess
-plains pocket mouse
-scarlet wisteria tree
-deerberry
-reamer
-homing torpedo
-molehill
-stockyard
-reniform leaf
-rag
-symmetry
-Texas star
-lerot
-pickle relish
-three-seeded mercury
-cotter pin
-ice-cream bean
-farmyard
-bar magnet
-hansom
-prickle cell
-renal cortex
-pest
-Ultrasuede
-sailing master
-brougham
-wastrel
-amboina pine
-Canary Island hare's foot fern
-ninepin ball
-southwestern lip fern
-usherette
-lemon drop
-star begonia
-weeds
-saltworks
-Persian melon
-corbina
-medusa
-bucksaw
-Gibson girl
-diameter
-American twinflower
-kino
-clear liquid diet
-angiocardiogram
-wetter
-oyster cracker
-yellowfin mojarra
-wild parsley
-life tenant
-broom closet
-Corynebacterium diphtheriae
-square shooter
-bedwetter
-ball-and-socket joint
-nonsolid color
-Salmonella typhimurium
-buffel grass
-hip pad
-subaltern
-heliothis moth
-trail boss
-hayloft
-Francisella
-primordial dwarf
-cock-a-leekie
-sugarplum
-propulsion system
-tyrolean
-Carib
-salai
-ketembilla
-ironclad
-cornhusk
-heckler
-multistage rocket
-north island edelweiss
-Chaldean
-twenty-two pistol
-Francophobe
-scofflaw
-sickle feather
-screw bean
-sea squill
-Scopolia carniolica
-agglomerator
-western holly fern
-presenter
-straight pin
-Myxine glutinosa
-Colbert
-clover-leaf roll
-war paint
-bird's-eye bush
-longfin mako
-running suit
-arrow wood
-margrave
-blue fleabane
-dracontium
-plastron
-chimney swift
-child prodigy
-commissar
-turtle soup
-postulant
-archaebacteria
-snakefly
-Pitot tube
-chap
-smilo
-Malthusian
-French roof
-worm wheel
-gulag
-pointed-leaf maple
-pull-off
-Cathaya
-American green toad
-ball cartridge
-infiltrator
-snowfield
-crotchet
-auxiliary pump
-bearnaise
-galax
-chaenactis
-olympic salamander
-sundowner
-cows' milk
-beach plum
-moss-trooper
-Arabidopsis thaliana
-cat's-claw
-bog rosemary
-ribier
-book agent
-bumper jack
-beefwood
-monk's cloth
-alpine bearberry
-climbing fumitory
-cucking stool
-puka
-Piltdown man
-property man
-discharge lamp
-X chromosome
-knobble
-lobster Newburg
-herbalist
-sunray
-golden saxifrage
-leopard cat
-muffle
-stonewort
-blancmange
-intraocular lens
-trepan
-desert mariposa tulip
-plume poppy
-Dane
-martynia
-shaver
-white milkweed
-napu
-tansy-leaved rocket
-abortus
-telemeter
-tansy mustard
-harpy
-honeysuckle
-ironworks
-testacean
-Tartuffe
-silvervine
-Sihasapa
-surface gauge
-western blind snake
-paramyxovirus
-Icelander
-bird louse
-stockbroker belt
-test-tube baby
-ague root
-little golden zinnia
-dietician
-elephant's-foot
-dirty bomb
-sailing warship
-brier
-tinter
-Connemara heath
-potato fungus
-bait casting
-decagon
-rosefish
-die
-high-pass filter
-solitaire
-widow's walk
-goldthread
-Tudor
-trews
-orange pekoe
-ninon
-soda jerk
-sump
-flying carpet
-burial garment
-oblanceolate leaf
-press gallery
-Shintoist
-three-centered arch
-spreading pogonia
-Moro
-foxtail orchid
-Ghanian
-dry kiln
-thane
-naranjilla
-bitter pea
-American bugbane
-apron string
-oyster fish
-Port Jackson fig
-prize winner
-high-water mark
-Oneida
-smoking room
-potato skin
-charge d'affaires
-gantlet
-amyloid plaque
-barmbrack
-mate
-arrow leaved aster
-handbarrow
-horned screamer
-virago
-linoleum knife
-rattlesnake root
-K ration
-reset
-foot brake
-red coral
-good guy
-aberrant
-lavalava
-poleax
-garden webworm
-sneezer
-mountain heath
-American dog violet
-eolith
-chimneysweeper
-matriarch
-smalltooth sawfish
-sea mouse
-tubercle bacillus
-superconducting supercollider
-Abney level
-darnel
-gherkin
-celery salt
-Tungus
-pulasan
-oriflamme
-death camp
-redhorse
-apprehender
-scion
-selectwoman
-pentahedron
-principal
-old school tie
-slice bar
-chanar
-pimento butter
-wailer
-zero
-mescal
-rosebud orchid
-stone bramble
-Jarvik heart
-NOC
-pitchman
-rat cheese
-strawberry tomato
-dwarf golden chinkapin
-landau
-tocsin
-ampulla
-scratcher
-crab Louis
-ginseng
-ripcord
-polluter
-tensiometer
-eyewitness
-aalii
-Oregon crab apple
-conservator
-day jessamine
-hexahedron
-suture
-tippet
-linsey-woolsey
-vernal witch hazel
-stainer
-egocentric
-canistel
-nudger
-shipping agent
-shortleaf pine
-battle sight
-cheese spread
-weeder
-incendiary bomb
-honeyflower
-stovepipe iron
-stepper
-hellgrammiate
-votary
-aflatoxin
-arquebus
-impulse turbine
-pipewort
-garrote
-glow lamp
-pigsticking
-blood clam
-surface search radar
-Bolshevik
-platen
-chariot
-Gentianopsis thermalis
-water level
-quandong
-catalytic cracker
-giant foxtail
-nut butter
-drainplug
-holdover
-coastguardsman
-Secretary of Health and Human Services
-Seeing Eye dog
-American plaice
-coquilles Saint-Jacques
-christella
-medium
-clingfish
-lally
-light-o'-love
-Gentianopsis detonsa
-taper file
-signal detection
-trip wire
-lignosae
-receiver
-sedan
-mud puppy
-corn sugar
-Philippine mahogany
-magnetic pole
-jointed rush
-trapper's tea
-Dorking
-welcome wagon
-clammyweed
-guard
-false azalea
-convalescent
-babassu
-dedicated file server
-colossus
-air search radar
-marquess
-straight flute
-sand stargazer
-sea catfish
-rosilla
-ripsaw
-Bermuda onion
-peach sauce
-sagebrush mariposa tulip
-yashmak
-Virginia mallow
-erose leaf
-sand blackberry
-boulevardier
-forester
-choragus
-onion mildew
-threadfin
-winged pea
-sugar daddy
-rotary press
-styracosaur
-rathskeller
-Japanese millet
-anchorite
-coral drops
-false gavial
-eastern pipistrel
-cheese press
-Chinese primrose
-pamperer
-real estate broker
-power worker
-breeder reactor
-nutcracker
-piano wire
-cushaw
-Sinanthropus
-firebreak
-kelp greenling
-herba impia
-toll call
-yoke
-bird fancier
-evening-snow
-fever tree
-reed meadow grass
-flanker back
-toggle bolt
-Santa Cruz cypress
-carbonnade flamande
-northern dune tansy
-mikado
-millettia
-forty-five
-court
-icepick
-holm oak
-Japanese angelica tree
-Pacific cod
-cant hook
-urologist
-spelt
-lekvar
-enologist
-Mediterranean flour moth
-prickly-edged leaf
-Spanish grunt
-dune cycling
-frostweed
-whisperer
-tucker
-Roman wormwood
-counterterrorist
-woolly alder aphid
-Nuttall oak
-snail butter
-threshing floor
-motley
-forge
-water mold
-mummichog
-sulfur paintbrush
-head
-walking delegate
-jujube
-peachleaf willow
-Christmas bells
-valley pocket gopher
-bear's-paw fern
-Lanthanotus borneensis
-pearl hominy
-placeman
-swage block
-offerer
-stargazer
-jeweler's glass
-male chauvinist
-crossbar
-Oktoberfest
-tamarau
-micronutrient
-large-leaved aster
-tasset
-tepary bean
-sausage curl
-ivy
-snob
-roller towel
-wood meadowgrass
-archil
-padrone
-prairie rocket
-tongueflower
-kidney fern
-Carolina buckthorn
-sea island cotton
-landscape architect
-realist
-oyabun
-mother hen
-ostracoderm
-esker
-heliophila
-nympholept
-shining clubmoss
-press agent
-clam dip
-Djiboutian
-white currant
-codfish ball
-hand cheese
-kraal
-trident
-conventicle
-bacteroid
-Indian plantain
-quandong
-kola nut
-signor
-theater light
-musk clover
-canistel
-silent partner
-steel-wool pad
-diggings
-affluent
-sightreader
-John Doe
-arrowworm
-goatsfoot
-guardroom
-wild cinnamon
-kaffir boom
-ink eraser
-yardie
-industrialist
-sea lily
-polarimeter
-Polistes annularis
-western big-eared bat
-omnivore
-Ted
-horsecloth
-crab cocktail
-vacuum chamber
-flower-of-an-hour
-bilge
-poleax
-neolith
-Montezuma
-plum-yew
-welfare case
-trave
-pipe bomb
-shading
-Centigrade thermometer
-bangalore torpedo
-celery top pine
-nuclear rocket
-fowling piece
-anti-Semite
-landscape
-derris
-bush honeysuckle
-Mediterranean water shrew
-ticket collector
-masked shrew
-white dipladenia
-Savoyard
-bondman
-tempter
-pygmy cypress
-pentathlete
-thruster
-usurper
-Arminian
-yerba buena
-ice field
-ichthyosaurus
-sackcloth
-bean tostada
-Oxbridge
-Pteropus hypomelanus
-thinker
-bank robber
-ape-man
-thurifer
-knawel
-mule fat
-hot spot
-hairy-legged vampire bat
-night raven
-hook and eye
-crocodile bird
-skunkweed
-beaver rat
-cypress sedge
-florida selaginella
-April fool
-Jonah crab
-glass wool
-corkwood
-dwarf elder
-hinging post
-gentile
-Brazilian trumpeter
-witch doctor
-thermograph
-pink shower
-Mao jacket
-capelin
-parang
-bradawl
-stooper
-jewel orchid
-citrange
-oarswoman
-Macedonian
-particolored buckeye
-pachycephalosaur
-satinwood
-Chinese brown sauce
-peep sight
-straight man
-quandong
-chamois cress
-nonfat dry milk
-rosin bag
-Leiden jar
-Grimes' golden
-spirillum
-grass vetch
-carillonneur
-downy wood mint
-melon ball
-sweet calabash
-chlamydospore
-bombshell
-sidewall
-sprig
-Indian button fern
-globe pepper
-rough-stemmed goldenrod
-bocconia
-bubble chamber
-sand dab
-plum-fruited yew
-aecium
-marrowfat pea
-hobbyist
-whipper-in
-salad burnet
-neckband
-Tangier pea
-sauce Louis
-salad burnet
-artist's loft
-koumiss
-Nazarene
-cutter
-scrim
-drape
-crab-eating dog
-deckhand
-bedroll
-gaff
-stifler
-pink lady
-great plains paintbrush
-patternmaker
-yoke
-caryophyllaceous plant
-angrecum
-quadriplegic
-grid
-genlisea
-aspic
-water table
-junket
-signore
-Mutillidae
-proprioceptor
-pivoting window
-Indian poke
-synchroscope
-trichion
-tarahumara frog
-proctoscope
-abomination
-purslane speedwell
-breast drill
-Japanese barberry
-mandrake root
-breakable
-salon
-American watercress
-take-up
-entrenchment
-cocktail sauce
-Scotch asphodel
-borough
-matchmaker
-Seneca snakeroot
-pointsman
-psephologist
-clustered poppy mallow
-onion thrips
-nuclear-powered ship
-organizer
-deciduous holly
-balsam willow
-enzymologist
-caraway
-drip loop
-dog laurel
-Orangeman
-sapsago
-polymath
-backplate
-leathery grape fern
-modillion
-two-timer
-handhold
-consignee
-white stringybark
-nettle-leaved goosefoot
-bookmaker
-disk drive
-doliolum
-palmist
-packinghouse
-Spandau
-Whipple's penstemon
-sword grass
-ribbon development
-pearly-shelled mussel
-winter heliotrope
-rogue elephant
-deck tennis
-Venus's flower basket
-football
-shim
-boatswain
-blinks
-armored catfish
-hooded seal
-outdoorswoman
-water starwort
-upholstery needle
-pleurodont
-silky anteater
-cornmeal
-lead-in
-redfin pickerel
-horse balm
-Rydberg's penstemon
-cascade transformer
-fly poison
-Volvaria bombycina
-broad-leaved twayblade
-pastry cart
-body plethysmograph
-waverer
-hardware store
-Parry's penstemon
-European sanicle
-strawberry geranium
-cross-examiner
-head gate
-devil's tongue
-hemiepiphyte
-pine hyacinth
-machmeter
-spirit lamp
-field judge
-Rock Cornish
-mayhaw
-Sassenach
-bog pimpernel
-parallel interface
-crowberry
-roach
-Aegyptopithecus
-cajan pea
-lapboard
-cryostat
-magnetic storage medium
-white yam
-Lombard
-rhymer
-bed and breakfast
-bunya bunya
-rifle grenade
-caterer
-collared pika
-anti-submarine rocket
-bookkeeper
-Western mountain ash
-profit taker
-fruitlet
-Knowlton's cactus
-infernal
-beefsteak begonia
-lunula
-emulsion
-intermediate wheatgrass
-titfer
-European sea bream
-bigeye scad
-yak butter
-kola
-cone pepper
-plesiosaur
-ragwort
-penal colony
-black carpet beetle
-lubber's hole
-Stapelias asterias
-yard marker
-balloon bomb
-Scythian lamb
-armory
-selsyn
-marblewood
-spirula
-fatalist
-hash head
-armiger
-Dom Pedro
-white-chinned petrel
-ballast
-orthopter
-greater water parsnip
-clutch
-largeleaf holly
-Evangelist
-king whiting
-tuna fish salad
-Muscadet
-surpriser
-jumping bristletail
-proportional counter tube
-Hamburg parsley
-obstructionist
-pus-forming bacteria
-creep feed
-stepbrother
-janissary
-control freak
-trusty
-trepan
-King William pine
-orthicon
-geological horizon
-molecular biologist
-violator
-pariah dog
-Austrian
-conciliator
-Fauntleroy
-packing needle
-mazer
-Saturday night special
-leucocytozoan
-coastal rein orchid
-whirligig beetle
-capitalist
-breeches buoy
-clubroot fungus
-meadow spikemoss
-Kichai
-Spanish lime
-land office
-camera obscura
-strafer
-purple-stemmed aster
-lusterware
-valve
-Roman nettle
-isthmus
-breadstuff
-sealskin
-maleo
-bilge keel
-carissa plum
-fish fly
-kolkhoznik
-heath pea
-cowage
-hog sucker
-Sam Browne belt
-inductor
-wild licorice
-Socotra begonia
-supernumerary
-Angle
-red shrubby penstemon
-toilet kit
-tawse
-sweet bells
-kawaka
-brown soft scale
-lyssavirus
-betting shop
-double-crosser
-macrotus
-climbing hempweed
-poi
-strip mall
-deadhead
-petit juror
-tract housing
-American mistletoe
-lace-flower vine
-precipitator
-endoparasite
-hairy wood mint
-red snapper
-Victorian
-hog peanut
-line of heart
-opossum shrimp
-plumcot
-Bavarian blue
-slops
-light flyweight
-oregano
-sand myrtle
-pocket battleship
-curator
-narc
-hydraulic cement
-plains pocket gopher
-closed loop
-pluralist
-molter
-Christmas bush
-snuffers
-slender knapweed
-footwall
-plage
-caper tree
-red siskin
-tender
-boat train
-tipster
-low-pass filter
-student lamp
-morosoph
-japonica
-bellows
-herald
-oyster plant
-savory
-mail
-computational linguist
-blade
-winter crookneck squash
-zoomastigote
-blackmailer
-richweed
-dialectician
-genip
-plumed scorpionfish
-jet bridge
-thermopile
-billy buttons
-Brule
-millwright
-Arenaviridae
-Jones' penstemon
-monastic habit
-genipap fruit
-burnous
-dairyman
-top
-crab-eating raccoon
-quadrangular prism
-pilot burner
-weeder
-trireme
-boy wonder
-man of letters
-Catawba
-high-muck-a-muck
-light circuit
-bloodworm
-lappet caterpillar
-half-and-half
-office boy
-saddle stitch
-mistletoe cactus
-false chamomile
-Catalina cherry
-workhouse
-Jamaica quassia
-britches
-tooth shell
-reduction gear
-carrot pudding
-balsam woolly aphid
-handspike
-aioli
-silver hake
-flour bin
-wireman
-gas-cooled reactor
-aficionado
-plus fours
-gitano
-gene chip
-oilfish
-ingenue
-tulip orchid
-late purple aster
-pork and beans
-envoy
-lemon extract
-milk bar
-black huckleberry
-ground roller
-Connecticuter
-siderocyte
-Jacquard loom
-chub
-meat safe
-stock cube
-Australian sumac
-purple sanicle
-tailless tenrec
-dog wrench
-rainbow cactus
-castor bean
-scintillation counter
-eohippus
-pawnbroker
-gauge boson
-front man
-early warning radar
-bearing wall
-Bourbon
-sandwichman
-sild
-gravelweed
-perishable
-cembra nut
-riflebird
-quicksand
-slate
-sweeper
-ship-towed long-range acoustic detection system
-defamer
-president
-vitamin K3
-challis
-tanekaha
-bloodwort
-grenadier
-quietist
-Zairese
-fucker
-foremother
-gesneria
-print buffer
-salsilla
-fissiped mammal
-fender
-consulate
-acidophilus milk
-Southern dewberry
-snail darter
-Panama redwood tree
-dehydrated food
-bush willow
-coffee fungus
-Sinologist
-Mesoamerican
-hood
-large civet
-deck-house
-cyborg
-smuggler
-pepper sauce
-cyberpunk
-Grand Inquisitor
-persona non grata
-haggis
-weeping tree broom
-stop bath
-modifier
-coyol
-conodont
-yellow giant hyssop
-optical pyrometer
-Carolina moonseed
-marinade
-aspartame
-false wintergreen
-cityscape
-philter
-turnery
-hemiplegic
-chuck-will's-widow
-vower
-track star
-myrtaceous tree
-small civet
-intelligence analyst
-dogcart
-yardman
-cross bit
-holometabola
-platen
-sweet cassava
-Comstock mealybug
-acute angle
-Communist
-alcohol thermometer
-mountain hollyhock
-Mead's milkweed
-highjacker
-Townes
-congou
-Astrophyton muricatum
-lazybones
-roughcast
-pressure cabin
-clinch
-cinnamon
-smoke bomb
-quandong
-tout
-office-bearer
-punctum
-efficiency apartment
-Queensland hemp
-Ceylon bowstring hemp
-newswoman
-vermin
-fetid bugbane
-grantee
-sanitary landfill
-gluten-free diet
-clabber
-shillelagh
-white lettuce
-sweet coltsfoot
-beggar's lice
-samite
-loser
-flasher
-water star grass
-banana passion fruit
-translator
-artificial kidney
-Virginia creeper
-American crab apple
-cactus mouse
-nebbish
-Ligustrum obtusifolium
-vox angelica
-stringer
-hunter
-know-it-all
-scene painter
-invalidator
-jungle cock
-basilica
-coriander
-California single-leaf pinyon
-miles gloriosus
-pina cloth
-law agent
-scarlet fritillary
-keurboom
-bailor
-ramjet
-seedling
-rib joint pliers
-ways
-picket ship
-Surgeon General
-wasabi
-marquis
-clostridium perfringens
-Helvella sulcata
-furnace lining
-kingwood
-painted sandgrouse
-plain wanderer
-Indian madder
-silver screen
-bailey
-dwarf spurge
-Serbian
-ball-buster
-shaheed
-Platte River penstemon
-tensiometer
-mute
-nymphomaniac
-Yokuts
-arroyo willow
-whipping post
-class act
-load
-winged everlasting
-periodontist
-diarist
-robber frog
-diestock
-curry powder
-ratchet wheel
-store detective
-hog plum
-prune whip
-shortwave diathermy machine
-Anabaptist
-post chaise
-Kennan
-bean caper
-delegate
-orderly sergeant
-celtuce
-jumping bean
-gowen cypress
-puddingwife
-registered nurse
-West Saxon
-rosita
-gun room
-nasotracheal tube
-matchboard
-flagship
-Boswellia carteri
-Canadian pondweed
-wonder boy
-sewer rat
-dimetrodon
-pantograph
-marsh bellflower
-angoumois moth
-slippery dick
-woolly indris
-creme de cacao
-dulciana
-Jewess
-Macadamia integrifolia
-least shrew
-don
-diffuser
-black-stem spleenwort
-grouseberry
-goniometer
-annotator
-sticktight
-gossip columnist
-speechwriter
-capon
-rock hind
-Liederkranz
-chandler
-echocardiograph
-sidelight
-fisher
-brocket
-New Zealand daisybush
-northern sea robin
-roller bandage
-peachick
-pellet
-pichi
-plug fuse
-spark coil
-buckwheat
-brood bitch
-wedgie
-dwarf bilberry
-filigree
-bull
-queen
-dodo
-Salish
-denticulate leaf
-Western silvery aster
-Prima
-magnetic bottle
-fetterbush
-process-server
-nainsook
-mythologist
-Piedmont glacier
-hammerhead
-niggard
-Mound Builder
-Kui
-Nootka
-highbinder
-passenger pigeon
-oblong
-tickler coil
-agnostic
-succorer
-esophagogastric junction
-dressmaker's model
-bombshell
-social anthropologist
-gildhall
-orpine
-pterodactyl
-bristly sarsaparilla
-Lane's Prince Albert
-hognose bat
-salesgirl
-lubricating system
-electric catfish
-wrap
-Jacksonian
-chard
-cherry laurel
-foreground
-beadsman
-Kolam
-amniote
-frozen pudding
-acid head
-poor box
-depositor
-coattail
-pallas's sandgrouse
-mason's level
-English lady crab
-skeg
-cruel plant
-petrolatum gauze
-tuna
-swivel
-stock-in-trade
-perisperm
-civies
-Phyllostomus hastatus
-alienor
-Verdicchio
-guard's van
-onion butter
-moviegoer
-planter
-citrange
-box huckleberry
-iconoscope
-familiar
-helmsman
-baby boomer
-constructivist
-American bog asphodel
-whorled caraway
-simple pendulum
-viviparous eelpout
-Job's tears
-holdout
-sour salt
-poison bush
-dusky-footed woodrat
-golden algae
-granadilla tree
-telethermometer
-crossbar
-thrift
-African bowstring hemp
-dog in the manger
-hayrack
-gold-crowned kinglet
-prolonge
-doge
-pencil
-discount house
-mulligan stew
-Nonconformist
-virologist
-gregarine
-facula
-rocket scientist
-thin-shelled mussel
-oospore
-annual salt-marsh aster
-Afrikaner
-metallic
-julienne
-culverin
-cleavers
-Berliner
-mudhif
-thorny skate
-brown lemming
-yellow colicroot
-cooling system
-large-leaved magnolia
-free-reed
-canyonside
-preemptor
-stake
-Brucella
-anti-G suit
-pleximeter
-squire
-salsilla
-write-in candidate
-lowland burrowing treefrog
-flare star
-dwarf hulsea
-jobber
-mangel-wurzel
-quagga
-red-skinned onion
-positive pole
-Pteropus capestratus
-jug wine
-stomacher
-standee
-bladder worm
-hakim
-house of correction
-pelisse
-golden mole
-temporizer
-rose apple
-drove
-umbrellawort
-holy of holies
-lawyer cane
-smooth lip fern
-anode
-astatic coils
-zip gun
-feverroot
-self-heal
-expansion bit
-salt reed grass
-field pussytoes
-nutmeg hickory
-cryptic coloration
-Venus's girdle
-Hunkpapa
-Calostoma cinnabarina
-raft foundation
-May apple
-pygmy mouse
-prokaryote
-yellow-green algae
-Bermuda maidenhair
-withdrawer
-coelacanth
-Elliott's goldenrod
-driftfish
-epicyclic train
-bowl
-swamp dewberry
-corbel step
-sadist
-party line
-anti-American
-mining engineer
-Amur privet
-conidium
-Gastrocybe lateritia
-lithia water
-chaulmoogra
-Rough Rider
-Guinea pepper
-glade mallow
-pitcher sage
-whitecup
-shanghaier
-low St Andrew's cross
-phonologist
-cocobolo
-perfumery
-visor
-prison chaplain
-belt
-ingesta
-literary critic
-industrial watercourse
-reckoner
-pursuer
-Kinetoscope
-Kuiper belt
-hyperope
-raw recruit
-Galiella rufa
-Prince Albert yew
-slit trench
-usher
-tenderfoot
-white-rayed mule's ears
-browser
-piccalilli
-bran
-giant buttercup
-water lobelia
-arborescent plant
-echinus
-dryland blueberry
-struggler
-platyctenean
-Geordie
-domatium
-twenty-two rifle
-keteleeria
-sports editor
-chorus girl
-Hakham
-dry-bulb thermometer
-onomancer
-double-bitted ax
-Girondist
-bottle bank
-thyrsopteris
-bandwagon
-star anise
-armored car
-dhawa
-Bessemer converter
-mutineer
-paradise tree
-tupik
-centurion
-mending
-chowchow
-margrave
-International Grandmaster
-African hemp
-catafalque
-leptodactylid frog
-forcemeat
-tank shell
-pill
-barbecue pit
-worthy
-lady's maid
-evergreen
-Jesuit
-South American staghorn
-rigger
-suffragan
-imperialist
-spherical angle
-grey lemming
-kitchen police
-tree swift
-coliphage
-archaist
-Conservative
-rib
-exegete
-Mendelian
-tragedian
-steerage
-Paleo-American
-obeche
-garlic
-grapefruit peel
-accommodating lens implant
-half blood
-barrelfish
-catgut
-lanceolate spleenwort
-hardliner
-frieze
-name dropper
-carrack
-huckster
-onion bread
-magnetic head
-pease pudding
-raisin moth
-negative magnetic pole
-electroencephalograph
-bunji-bunji
-synchroflash
-Mornay sauce
-stencil
-winged pigweed
-Nesselrode
-MEDLINE
-licorice
-mainspring
-melilotus
-duke
-experimenter
-Napier's bones
-four-minute man
-pin-tailed sandgrouse
-toolmaker
-pogge
-rootstock
-baton
-pricket
-creeping snowberry
-anomalops
-nester
-devourer
-apolemia
-Maricopa
-pine-barren sandwort
-larvacean
-American dewberry
-escalope de veau Orloff
-gig
-myrtle
-pitsaw
-Lutheran
-fish house punch
-gnathostome
-intake valve
-molasses taffy
-clammy locust
-vandyke beard
-Atlantic tripletail
-planktonic algae
-estradiol patch
-flummery
-cytologist
-sectarian
-oil meal
-tomtate
-mediterranean anchovy
-aspersorium
-argonaut
-porkholt
-sheep ked
-algometer
-Adventist
-false goatsbeard
-snake polypody
-streetwalker
-shelver
-adoptee
-highflier
-pitch apple
-prairie rocket
-fish mousse
-viroid
-deckle
-manila tamarind
-observer's meridian
-pincurl clip
-hardstem bulrush
-gossamer
-brookweed
-Druze
-hug-me-tight
-accessory before the fact
-oilman
-Comanche
-Marine
-bedlamite
-Chinese cork oak
-squawbush
-false miterwort
-walk-on
-Cynopterus sphinx
-brandyball
-landlubber
-arrowroot
-cape forget-me-not
-galoot
-tabor pipe
-checker
-Levant cotton
-paddle box
-murderess
-smirker
-fuddy-duddy
-withdrawer
-newel
-shade
-pink disease fungus
-tipu
-sweet sultan
-aeronautical engineer
-tall gallberry holly
-acarid
-conqueror
-cucumber
-film director
-ordinary
-salon
-closet queen
-allegorizer
-tonka bean
-flax rust
-negative pole
-dagame
-dentist's drill
-mock privet
-micropyle
-contributor
-dark horse
-climbing corydalis
-cosmotron
-land agent
-Big Blue
-Cynic
-tassel flower
-lyrate leaf
-Minuteman
-Dutch-elm beetle
-Hessian fly
-flower girl
-West-sider
-window dresser
-skinny-dipper
-whitebait
-out-and-outer
-hooker
-amicus curiae
-jack
-camwood
-stockist
-black root rot fungus
-Jamaica dogwood
-diaphragm
-Holocentrus ascensionis
-roselle
-black maire
-Pygmy
-fumigator
-lame duck
-mudder
-hydraulic transmission
-conning tower
-phoronid
-batfish
-hearing dog
-monohybrid
-whaling gun
-Cockcroft and Walton accelerator
-allemande
-seasoner
-epileptic
-ammonia clock
-Young Turk
-lanseh tree
-urceole
-cafe noir
-poster girl
-Oglala
-deadeye
-manna lichen
-positive pole
-cinch
-lyricist
-hermaphrodite
-kidney stone
-dilator
-number one
-frotteur
-kaffir bread
-fish knife
-tarragon
-adjuster
-potato wart fungus
-Florida pompano
-conductor
-corbie gable
-rounders
-Catha edulis
-bender
-recruit
-Uruguayan
-subject
-bunghole
-day boarder
-pocketed bat
-Oxonian
-owner-occupier
-yellow-leaf sickle pine
-devisor
-exhibitor
-looking glass
-shipowner
-crooked-stemmed aster
-calico
-dash-pot
-defilade
-Confucian
-egg-and-dart
-irreligionist
-lepton
-self-rising flour
-diving bell
-Brahui
-shop girl
-maximum and minimum thermometer
-Dalmatian laburnum
-correspondent
-subduer
-nonperson
-Reaumur thermometer
-rough-leaved aster
-jacksmelt
-pinfold
-magneto
-ex-wife
-round-leaved rein orchid
-purloo
-American shrew mole
-sweet sand verbena
-polymastigote
-outfitter
-curled leaf pondweed
-Italian dressing
-borderer
-ambusher
-geebung
-four-stroke engine
-small ship
-homeopath
-gynostegium
-political prisoner
-Radiigera fuscogleba
-ensiform leaf
-rhizoctinia
-satyr orchid
-rue
-bouillon cube
-flip
-prophyll
-tilefish
-periselene
-prima donna
-choker
-laminar flow clean room
-Hooker's orchid
-fish joint
-mombin
-remover
-array
-coelostat
-autophyte
-consigner
-Damaraland mole rat
-gasman
-public works
-lye hominy
-pearlfish
-piassava palm
-Georgian
-uxoricide
-confessor
-community center
-epigone
-tagger
-abrading stone
-cryoscope
-nautch girl
-reliever
-Cartesian
-Indian beech
-protoplasmic astrocyte
-fundamentalist
-mustard sauce
-crank
-houselights
-five-point bishop's cap
-comedienne
-triangle
-presentist
-beaugregory
-dreamer
-Wave
-blue mockingbird
-Barbados gooseberry
-ten-spined stickleback
-papoose
-silky pocket mouse
-holdup man
-agent-in-place
-suspensory
-emigrant
-ropemaker
-bookbinder
-jumby bead
-undershrub
-Killarney fern
-sheep bell
-city slicker
-equerry
-pea crab
-down-and-out
-blackmouth bass
-shirtmaker
-lister
-UNIX guru
-snipefish
-gimbal
-maisonette
-haircloth
-Ranvier's nodes
-pigmy talinum
-tribute album
-msasa
-hydroxide ion
-madame
-four-pounder
-prophet
-sloganeer
-field-effect transistor
-nude mouse
-canteen
-Calostoma lutescens
-buteonine
-sunlamp
-Uruguay potato
-Spanish tamarind
-Prince-of-Wales'-heath
-kishke
-caprifig
-chincapin
-hegari
-alarmist
-bathtub gin
-astatic galvanometer
-Calostoma ravenelii
-marang
-tussah
-coin box
-bugleweed
-hacker
-frontal eminence
-timekeeper
-shunt
-bicycle clip
-mustang mint
-caesium clock
-hospice
-glenoid fossa
-archpriest
-ex-gambler
-incrustation
-salvager
-Donatist
-violator
-lamb succory
-hygroscope
-oilbird
-sharptail mola
-showplace
-corn syrup
-flashlight fish
-pulse timing circuit
-anchovy paste
-fascista
-chigoe
-divan
-Druid
-squad room
-Huntingdon elm
-buffalo carpet beetle
-carper
-corn lily
-goats' milk
-assault gun
-cockpit
-Lochaber ax
-Visigoth
-occupier
-Basotho
-criminologist
-spindle
-Rosicrucian
-Cornishwoman
-musk kangaroo
-artificial skin
-pandurate leaf
-Parkia javanica
-roundhead
-tea-like drink
-basidiolichen
-unguiculate
-stepmother
-Nauruan
-gutta-percha tree
-bloodberry
-scarlet haw
-marupa
-censor
-algebraist
-pelvimeter
-whaler
-cowhide
-paparazzo
-biochip
-internationalist
-Yukon white birch
-hangar queen
-chlamydia
-puttee
-Pipturus albidus
-pearly razorfish
-sea moss
-burglar
-hoary golden bush
-colter
-drey
-bushman's poison
-maxillaria
-gnetum
-deadeye
-shittah
-swamp oak
-damper block
-deepwater squirrelfish
-truffle
-cangue
-paleolith
-lawyerbush
-sorehead
-Texas snowbell
-Tremella reticulata
-quarter
-keelboat
-dimity
-whiner
-Wagnerian
-myrmecophyte
-frontierswoman
-pyrometric cone
-big-tree plum
-puppy
-galbulus
-hod
-winceyette
-carriage wrench
-dictostylium
-farmland
-infanticide
-Jacob's rod
-threadfish
-monocline
-inamorato
-leaf miner
-purple cress
-passer
-black-fronted bush shrike
-silverrod
-bootmaker
-segregate
-captive
-Edmontonia
-spherometer
-television transmitter
-bladder
-Saratoga spittlebug
-dynamometer
-lodge
-smooth darling pea
-Cossack
-wake-up call
-Olmec
-sutler
-molasses kiss
-corner post
-rattlesnake weed
-yardmaster
-adder
-rhinoscope
-referral
-ulster
-pantaloon
-counterspy
-gadgeteer
-heart cherry
-hospital chaplain
-Clydesdale terrier
-plank-bed
-Russian thistle
-actinometer
-dyspeptic
-common wolffia
-firewall
-seidel
-potato moth
-soapweed
-seif dune
-thill
-cosmographer
-absolver
-halberdier
-fire control system
-kai apple
-bastard pennyroyal
-Big Brother
-broadcast journalist
-Albatrellus dispansus
-citrophilous mealybug
-split end
-nickel-iron battery
-Newtonian
-gas maser
-thumbstall
-anaspid
-dusky-footed wood rat
-latitudinarian
-flatbrod
-schizocarp
-niqaabi
-flight surgeon
-gyrocompass
-Polyporus tenuiculus
-Utopian
-mailboat
-spellbinder
-undercoat
-cassareep
-typical jerboa
-photocathode
-katharometer
-bight
-fur-piece
-penetration bomb
-malik
-Siberian millet
-nanomia
-Wykehamist
-tosser
-gyrostabilizer
-microwave diathermy machine
-crystal set
-wall
-legatee
-alfalfa
-angwantibo
-charioteer
-piano maker
-African mahogany
-Morlett's crocodile
-taro
-parallel circuit
-cush-cush
-etymologist
-matriculate
-neem seed
-cornerback
-kingfisher daisy
-redoubt
-blastomycete
-peplos
-costumier
-publican
-tobogganist
-semolina
-myrmidon
-parricide
-gymslip
-whoremaster
-cryptocoryne
-header
-platitudinarian
-barleycorn
-spiral bandage
-reciter
-abecedarian
-dance
-wrymouth
-bilberry
-Liopelma hamiltoni
-streamliner
-Fordhooks
-fixed phagocyte
-radiobiologist
-neurologist
-Selkup
-dollarfish
-cascade everlasting
-acrodont
-boarhound
-midstream
-theatrical producer
-abhorrer
-goldsmith
-photometrist
-Anglo-Saxon
-rugel's plantain
-sable
-workmate
-ferule
-ankus
-earleaved umbrella tree
-Passamaquody
-timucu
-Mexican pocket mouse
-yerba santa
-Rochon prism
-apomict
-monocarp
-sweet unicorn plant
-common winterberry holly
-archivist
-drypis
-paretic
-fly-by-night
-white-berry yew
-Schoolman
-blue cheese dressing
-vintager
-squatter
-Euphausia pacifica
-corrugated fastener
-yellow henbane
-Croesus
-almoner
-analphabet
-acoustic delay line
-sheep frog
-workhouse
-horseleech
-venturer
-pond-scum parasite
-Pyrenees daisy
-plagiarist
-Truncocolumella citrina
-rerebrace
-group captain
-caddis fly
-hot-rock penstemon
-kanzu
-stylopodium
-slopseller
-rauli beech
-starter
-ootid
-statesman
-distributor cam
-ascot
-falcon-gentle
-Duplicidentata
-spotted antbird
-heliometer
-false buckthorn
-Allegheny spurge
-Cavalier
-dart
-photocoagulator
-master-at-arms
-kei apple
-baldachin
-crapshooter
-gametangium
-white hope
-chipotle
-spike heath
-Scotch woodcock
-Florentine
-differential analyzer
-Mitrula elegans
-wet cell
-basil balm
-Circassian
-corn cake
-bouncing betty
-vice-regent
-lagerphone
-ketembilla
-whoremaster
-fork
-tetrasporangium
-trifler
-pill head
-life-support system
-quartermaster general
-tobacco thrips
-officeholder
-teredo
-toyon
-Sundacarpus amara
-Phytophthora citrophthora
-naif
-lobbyist
-alligator wrench
-bully
-heavy
-toxicologist
-radio chassis
-waterdog
-drive line
-kaffir cat
-foster-brother
-breakax
-curette
-traditionalist
-pipe vise
-striped button quail
-gawker
-homeotherm
-schoolyard
-battue
-kalansuwa
-deviationist
-Bolshevik
-transponder
-pungapung
-iron
-Eyeish
-roccella
-manglietia
-Tory
-print seller
-Texas Ranger
-otter shrew
-seconder
-shellflower
-outlier
-party man
-wold
-hayfork
-oncologist
-framer
-co-beneficiary
-ocean pout
-Chinese angelica
-scrimshaw
-air attache
-false gromwell
-standing press
-fringepod
-specifier
-automatic choke
-durum
-yenta
-wassailer
-reeler
-signora
-beach pancake
-common booklouse
-pellicle
-backroom boy
-den mother
-associate
-Unitarian
-gambist
-brookweed
-clubroom
-cat's-tail
-playboy
-self-registering thermometer
-doorstop
-bennet
-yak's milk
-escapee
-quail bush
-sparge pipe
-coast boykinia
-screw key
-half gainer
-aggravator
-cotton mill
-tailor's chalk
-free agent
-cotton mouse
-deadhead
-bunny
-turpentine camphor weed
-amaranth
-ceratodus
-red lauan
-beam-ends
-thermograph
-wally
-Toda
-handrest
-commissary
-oak-leaved goosefoot
-manufacturer
-voicer
-Jafnea semitosta
-bench hook
-finder
-abyssal zone
-rabbitwood
-Hercules'-club
-epicarp
-declinometer
-camp follower
-signaler
-Australian pea
-putz
-qadi
-banded palm civet
-egg timer
-regnellidium
-calisaya
-harvestfish
-sound spectrograph
-side-wheeler
-glomerule
-woolly rhinoceros
-Black Muslim
-horticulturist
-ornithomimid
-cryometer
-battlefront
-gametophyte
-airmailer
-cuisse
-nakedwood
-baseball club
-slasher
-anise
-leatherleaf
-leatherjacket
-horned pondweed
-gofer
-Saigon cinnamon
-barong
-blazer
-twinkler
-skeleton shrimp
-dial
-floorwalker
-case shot
-flannelbush
-cultivated parsnip
-Jane Doe
-few-flowered leek
-nogging
-placer miner
-muzzler
-serge
-lion-hunter
-capulin
-Wandering Jew
-ascidian tadpole
-hispid pocket mouse
-southern spatterdock
-milk wagon
-junior middleweight
-duck sauce
-promycelium
-protozoologist
-cascade liquefier
-tout
-longheaded thimbleweed
-charcoal burner
-footage
-slop
-bridge agent
-miller's-thumb
-Job's comforter
-marocain
-tanker plane
-lancetfish
-knocker
-toque
-ordinand
-umbrella bird
-favorite son
-hare's-foot bristle fern
-business traveler
-plotter
-Asiatic shrew mole
-tallyman
-stump
-Paleacrita vernata
-index register
-mortgagee
-accuser
-codger
-sand rat
-seaside centaury
-chiropractor
-Florida smoothhound
-dwarf sperm whale
-T-man
-sannup
-dragonhead
-numdah
-alkali grass
-gynobase
-kymograph
-ascolichen
-steward
-waterline
-Nazarene
-filer
-lapidary
-muncher
-wincey
-scyphus
-question master
-besieger
-worldling
-docent
-facing
-atmometer
-quern
-puerpera
-three-decker
-calliope
-wild red oat
-bailee
-flame pea
-cattle cake
-theist
-yellowtail flounder
-cosmopolitan
-rocket engineer
-vouchee
-Turkoman
-hard sauce
-Thousand Island dressing
-assayer
-messmate
-mutilator
-oyster bar
-flame tokay
-countess
-prairie mimosa
-microsporangium
-cotter
-townsman
-paring
-fundraiser
-simperer
-Comrade
-orlop deck
-power takeoff
-cattleship
-prime meridian
-Javanthropus
-scriptorium
-curandera
-long-clawed prawn
-maestro
-paster
-potato tuberworm
-chachka
-junkyard
-cape yellowwood
-reentrant polygon
-Liberian coffee
-restaurateur
-Alsophila pometaria
-Jekyll and Hyde
-electrophorus
-Scomberomorus maculatus
-manipulator
-gromwell
-chicken provencale
-ashram
-mangel-wurzel
-shamrock pea
-dossal
-adducer
-erection
-Mysore thorn
-smoothie
-chufa
-brace wrench
-victualer
-litterer
-linstock
-Protium guianense
-palfrey
-banyan
-klieg light
-dangleberry
-trooper
-yaupon holly
-quitter
-tradescant's aster
-nullipara
-melter
-devil's urn
-ghostwriter
-mouth
-analogist
-Creek
-sonic depth finder
-fucker
-locus of infection
-mortician
-esophageal smear
-locum tenens
-conic projection
-aroeira blanca
-bellarmine
-night porter
-automobile mechanic
-codpiece
-Munro
-cottonweed
-scoinson arch
-tinderbox
-frozen food
-waterproofing
-Egyptian henbane
-lash
-transactor
-American smooth dogfish
-existentialist
-grabber
-Sonoran lyre snake
-Rufous rubber cup
-colors
-weekend warrior
-power user
-perennial salt marsh aster
-Puritan
-Apalachicola rosemary
-anecdotist
-tosser
-moth bean
-agnostic
-stretcher-bearer
-browntail
-optimist
-brewer's mole
-astronomy satellite
-flat file
-rust mite
-tuberous plant
-day laborer
-buster
-trapezoid
-bevatron
-nonresident
-Streptomyces griseus
-mangosteen
-customer agent
-hero worshiper
-suicide bomber
-procellariiform seabird
-archiannelid
-reaction turbine
-distortionist
-bulldog wrench
-grainy club
-scalp
-Aztec
-scow
-globigerina
-pedant
-heartleaf manzanita
-kanchil
-low gallberry holly
-containment
-scandalmonger
-rose-colored starling
-Powhatan
-addle-head
-Chilean rimu
-Atlantic sea bream
-arthrospore
-ramrod
-root climber
-Kalapooia
-roach clip
-Schreiber's aster
-horseradish
-albino
-Kshatriya
-trombidiid
-blasting cap
-body pad
-brachium
-shallu
-Wynnea americana
-slender centaury
-munj
-upset
-wind tunnel
-cottonwick
-airing cupboard
-pepper shrub
-ambrosia
-languisher
-chosen
-rose globe lily
-purple apricot
-costia
-sloop of war
-sultana
-frontlet
-booster
-sargassum fish
-broad-leaved montia
-rifleman bird
-stillroom
-amoralist
-enginery
-meter maid
-fitment
-southern bog lemming
-Athenian
-clincher
-cusk-eel
-mackintosh
-diaphone
-corozo
-Australian reed grass
-czar
-spongioblast
-Eurafrican
-airhead
-Shahaptian
-Roman
-pollinium
-tourist class
-halogeton
-stamper
-emperor
-malingerer
-tramp steamer
-Peziza domicilina
-pilot cloth
-stenopterygius
-cost accountant
-Queen's Counsel
-wine-maker's yeast
-poppet
-cage
-rowlock arch
-landgrave
-bearded wheatgrass
-stink bell
-quaker
-undesirable
-algarroba
-resistance pyrometer
-exorcist
-carib wood
-guvnor
-border patrolman
-bathhouse
-licenser
-headman
-rentier
-pine spittlebug
-nut-leaved screw tree
-paraduodenal smear
-apron
-necker
-smilax
-Alpine besseya
-creeper
-castle
-ground bait
-Queensland grass-cloth plant
-sclerotium
-great yellowcress
-fat farm
-Stoker
-hoop snake
-elixir of life
-Trotskyite
-home buyer
-wheat berry
-Tutelo
-semi-climber
-utahraptor
-wet-bulb thermometer
-packrat
-hygrophyte
-darter
-sketcher
-refiner
-camlet
-midgrass
-compound
-tarwood
-Colorado River hemp
-toiler
-abstractor
-override
-dwarf pipefish
-plodder
-briefcase computer
-trunk hose
-brown butter
-valve-in-head engine
-cymbalist
-explosive detection system
-horsewoman
-boutonniere
-chinchilla
-venerator
-scourer
-exarch
-cohune nut
-ayapana
-continental divide
-cosigner
-stalker
-pyxie
-Genet
-Macowanites americanus
-open-hearth furnace
-water chestnut
-American frogbit
-tarwood
-cutter
-scout
-burr
-upsetter
-grist
-tagasaste
-mouthpiece
-palette
-rattan
-letterman
-Exmoor
-Methodist
-eelblenny
-marasca
-slide valve
-ventilation
-saddle hackle
-Yakut
-flux applicator
-air traveler
-murder suspect
-Cynocephalus variegatus
-idolizer
-Surgeon General
-nutlet
-little-head snakeweed
-germ tube
-fellow traveler
-raceabout
-commodore
-czar
-anamorphosis
-treelet
-girlfriend
-groundnut
-sideline
-giant star grass
-goffer
-spark lever
-oubliette
-processor
-tare
-plodder
-extremist
-Kipp's apparatus
-gripsack
-S wrench
-viscountess
-bridgehead
-cascarilla
-Asiatic flying squirrel
-protoceratops
-equerry
-difflugia
-princeling
-moonlighter
-aspergill
-common flat pea
-Utahan
-imperial mammoth
-plantain-leaved pussytoes
-Boott's goldenrod
-bootlegger
-reed pipe
-runcinate leaf
-onion salt
-nitrite bacterium
-introvert
-duck
-New World opah
-goliath frog
-heterostracan
-disrupting explosive
-haggler
-candlenut
-false bugbane
-returning officer
-eudiometer
-ship-breaker
-metazoan
-mandarin
-patka
-gill net
-cavity wall
-armilla
-rainmaker
-dealfish
-orderly
-gleaner
-muffin man
-house sitter
-alto
-sand devil's claw
-vulcanizer
-appendicularia
-boron chamber
-chess
-bitok
-anchovy butter
-dropout
-flour mill
-bishop
-escapist
-scapegrace
-stanhope
-smooth winterberry holly
-upstager
-stalking-horse
-pony
-prairie gourd
-parabolic mirror
-Polaroid
-slasher
-lap
-garlic butter
-sendee
-German millet
-hairy honeysuckle
-Swiss canton
-Scleroderma flavidium
-red goatfish
-telegraph plant
-Jungian
-garment cutter
-mallee hen
-stranger
-driveway
-schooner
-Paiute
-cisco
-trestlework
-sipper
-shanny
-romanticist
-Molly Miller
-mountain rimu
-odd-leg caliper
-bitumastic
-Western Australia coral pea
-labor coach
-latchkey
-harpulla
-solitary pussytoes
-chop-suey greens
-coil
-guimpe
-diapir
-Osage
-gutta-percha tree
-giant eland
-reticulation
-garden huckleberry
-quick study
-Hudson bay collared lemming
-coreligionist
-Lancastrian
-stumblebum
-omnirange
-seersucker
-Potemkin village
-Rhea Silvia
-symphonist
-bolti
-jaw
-jaconet
-page
-visiting fireman
-haulm
-p-n junction
-landlubber
-yellow jack
-triclinium
-souari
-invader
-fire walker
-Luddite
-Plott hound
-hemming-stitch
-winker
-star-duckweed
-craniometer
-Arabidopsis lyrata
-loser
-cypripedia
-trimmer arch
-cookhouse
-pink fivecorner
-transfer
-ringleader
-northern pocket gopher
-moke
-blockade-runner
-cyclostome
-web-spinning mite
-Whig
-transcriber
-malahini
-sawyer
-patent log
-paca
-tragedian
-thermojunction
-soffit
-black buffalo
-foreigner
-applecart
-brit
-pole horse
-white mullet
-argentinosaur
-Homo soloensis
-bounty hunter
-decumary
-hand
-paperboy
-Smitane
-windowpane
-Java man
-Wynnea sparassoides
-prune
-middy
-lilliputian
-sorb
-pyrostat
-guest worker
-hold
-leaseholder
-vegan
-humanist
-salinometer
-piton
-zygospore
-means
-night rider
-tetraspore
-archipelago
-radiomicrometer
-nitpicker
-spot weld
-slicer
-girlfriend
-round-tailed muskrat
-cock's eggs
-Shavian
-bay
-nuclear chemist
-planetarium
-hiccup nut
-Marylander
-milling
-microsporidian
-brown cup
-Strophanthus kombe
-little skate
-emancipator
-paperhanger
-archaeopteryx
-maigre
-Mastotermes electrodominicus
-procurer
-seizure-alert dog
-homeboy
-cotton strain
-mute
-siren
-spearnose bat
-phenacomys
-gayal
-arsenal
-pitchfork
-Port Jackson heath
-cud
-magnetic core memory
-interferometer
-water jacket
-account executive
-hodoscope
-window oyster
-sudatorium
-syncopator
-loment
-hypertensive
-smoothbark
-Geogia holly
-nailhead
-African holly
-musette
-chafeweed
-microflora
-derrick
-strawworm
-shogun
-queen post
-jerboa kangaroo
-columbo
-royal
-sourball
-solenogaster
-cardsharp
-Homo habilis
-intaglio
-calf's-foot jelly
-flotsam
-skirret
-baronduki
-chyme
-shovel hat
-Welsh
-monoplane flying fish
-groundfish
-tablet-armed chair
-swan dive
-Indian club
-colonial
-cassiri
-pyramidal tent
-praya
-silk vine
-time clock
-button snakeroot
-clews
-Korean lespedeza
-diffuser
-ripping bar
-puttyroot
-nipple shield
-headpin
-juneberry holly
-hub-and-spoke
-laver
-weldment
-plain flour
-hoosegow
-dudeen
-grey skate
-line of life
-mung
-arariba
-Newtown Wonder
-rock candy
-side chapel
-castor sugar
-narrow-leaved white-topped aster
-babassu nut
-puka
-rings
-catchall
-heat shield
-caroche
-oxbow
-Australian coral snake
-tapper
-sporangiophore
-fenugreek
-spruce gall aphid
-gouache
-cutoff
-private line
-pod
-cargo hatch
-nailhead
-penile implant
-geophyte
-small-leaved linden
-deepwater pipefish
-paperhanger
-hairy spurge
-Persian lamb
-subtropics
-feed grain
-clarence
-nonparticipant
-scorpioid cyme
-hand brake
-tiller
-Geglossaceae
-albacore
-monochrome
-goa bean
-bur
-tongue worm
-psittacosaur
-frog's lettuce
-pectoral
-terreplein
-light filter
-fishpaste
-dry point
-grison
-feterita
-dolichocephalic
-oenomel
-stretcher
-swag
-cheval-de-frise
-mountain beaver
-scammony
-discus
-leatherleaf saxifrage
-wharf rat
-Dominique
-pelycosaur
-depth gauge
-bishop
-archespore
-true anomaly
-silver jenny
-mercy seat
-kelp
-oviraptorid
-acrylic
-Chinese pea tree
-meat house
-bilge well
-Temperate Zone
-whale louse
-balbriggan
-briefcase bomb
-pump-type pliers
-oil
-sour gourd
-Jewbush
-lunette
-Chinese paddlefish
-pyxidium
-beechnut
-calabar bean
-grugru nut
-gib
-blunt file
-cataphyll
-megasporangium
-blockbuster
-sliding seat
-hogchoker
-calceus
-Connarus guianensis
-honest woman
-survivor
-second balcony
-tempera
-Calvary clover
-murine
-outwork
-bogy
-elephant's-foot
-conning tower
-set square
-blackfly
-stirk
-Streptomyces erythreus
-blade
-goldfield
-snowball
-mortal enemy
-waltzer
-shoal
-galley
-hitchhiker
-lithophyte
-brisling
-scauper
-esophagoscope
-grab
-subtracter
-philosopher
-duplex apartment
-southeastern pocket gopher
-bonduc nut
-reverberatory furnace
-grader
-lamp house
-northern bog lemming
-brotula
-ornithopod
-ptyalith
-obturator
-perpetual motion machine
-range pole
-Africander
-curvet
-daisy print wheel
-floor
-collector
-mutant
-tuck
-fore-and-after
-senega
-buckler mustard
-louvar
-Tarsius glis
-culdoscope
-Spanish fly
-steering gear
-hatchet man
-museum
-saw set
-cambric tea
-comber
-thermohydrometer
-stationer
-chalcis fly
-bryanthus
-whipstitch
-harvest mite
-rock gunnel
-time bomb
-rariora
-pigfish
-apetalous flower
-head shop
-horned whiff
-sandpit
-tachistoscope
-sundries
-taffrail
-caller
-monofocal lens implant
-Dover's powder
-souari nut
-crowbait
-render
-Shakespearian
-hagberry
-megatherian
-magus
-hatchel
-mangabey
-garroter
-piedmont
-cope
-barrio
-psychodid
-rigout
-distributor
-croupier's rake
-sarcenet
-narrow-leaved water plantain
-treenail
-biped
-lanternfish
-overdrive
-barndoor skate
-picket boat
-amber lily
-sawpit
-sand lance
-bucket shop
-common beech
-laundry truck
-surtout
-grogram
-tampion
-escape hatch
-interstice
-shop bell
-snake mackerel
-nakedwood
-tumbrel
-mericarp
-mountain paca
-cab
-big board
-cringle
-eusporangium
-shipping room
-coal chute
-dumbwaiter
-Smiledon californicus
-man-at-arms
-cartridge
-deinonychus
-pigeon pea
-screw bean
-spectacle
-floorboard
-cutting room
-low-warp-loom
-proconsul
-sabicu
-genipap
-clapper
-aquifer
-archaeornis
-belly flop
-Protium heptaphyllum
-interrupter
-high-warp loom
-knight
-wiper
-impression
-poker
-Pithecanthropus
-sable
-guardroom
-tenter
-wellhead
-raja
-strickle
-sodomite
-mountebank
-sand leek
-Barbados gooseberry
-shuffler
-sensory fiber
-crab-eating opossum
-etching
-rare bird
-scup
-fagot
-negro vine
-hutment
-droshky
-nephoscope
-lady chapel
-cutty stool
-release
-vestiture
-buff
-standard
-Tabernacle
-vascular ray
-snakewood
-chlorobenzylidenemalononitrile
-limnologist
-pouched mole
-microwave linear accelerator
-Mastotermes darwiniensis
-wind tee
-orange bat
-open sight
-carpospore
-rampant arch
-sabbatia
-cursor
-post exchange
-bellpull
-center
-cyclostyle
-canonist
-pygmy sperm whale
-moa
-king
-pass-through
-angioscope
-marrow
-hookup
-revetment
-acanthocephalan
-good Samaritan
-apatosaur
-web spinner
-dixie
-ommastrephes
-crossbench
-candlewick
-jack
-light arm
-caisson
-kaki
-quandong nut
-Meuniere butter
-coquilla nut
-mast
-black
-twitterer
-bluethroat pikeblenny
-shielding
-water-shield
-urolith
-elephant bird
-clearway
-dark lantern
-schizopetalon
-press
-Nazi
-sugarberry
-Maltese
-stevedore
-hair shirt
-party wall
-gainer
-blackheart
-nothosaur
-cavetto
-evergreen bittersweet
-chemical bomb
-calpac
-shingle
-turnpike
-animator
-heaver
-isoclinic line
-death knell
-liner
-anathema
-aerie
-razorback
-Ichyostega
-pound net
-French dressing
-mottle
-yard
-string tie
-bell seat
-brattice
-battering ram
-sierra
-pompon
-vertex
-stomach pump
-electrolytic cell
-escolar
-telpher
-roadhouse
-cerecloth
-tartare sauce
-letter case
-whale sucker
-hob
-teg
-canvas
-strickle
-hectograph
-Cartagena bark
-mail car
-acinus
-freedom rider
-bread sauce
-picture window
-Rhizopogon idahoensis
-pinprick
-mass spectrograph
-ringer
-devil's cigar
-salad cream
-marlberry
-airbrake
-Clark cell
-yellow-throated marten
-wire gauge
-dinoceras
-aba
-harpoon log
-plate rail
-mustard plaster
-coelophysis
-journal box
-puce
-ballcock
-quartering
-izar
-clinid
-whirler
-turnspit
-deathbed
-pottle
-shot
-doubler
-Coryphaena equisetis
-English sole
-chicken feed
-borrow pit
-mylodontid
-Chilean nut
-Kundt's tube
-ling
-asthenosphere
-reseau
-death seat
-immovable bandage
-peppermint patty
-lecturer
-electron multiplier
-bear claw
-hyacinth
-beaked salmon
-toehold
-scull
-snowball
-gangsaw
-fiber
-oxeye
-lashing
-Beckman thermometer
-fence
-cantilever
-dinner theater
-Reynard
-jag
-umbrella plant
-camera lucida
-beaver
-slug
-yellowfin croaker
-Sibley tent
-rat-tail file
-anchovy pear
-soldier
-cackler
-chaise
-Pitot-static tube
-minniebush
-Episcopalian
-oleaster
-ejaculator
-wavy-leaved aster
-knight
-rack
-real storage
-magnetic mine
-cocoa plum
-vesiculovirus
-birch leaf miner
-water chevrotain
-rudapithecus
-torpedo tube
-itch mite
-warren
-loft
-washerman
-terrace
-nonstarter
-shit
-platform
-caudex
-ground control
-Ostariophysi
-slopshop
-Peruvian cotton
-crystal oscillator
-plastic bomb
-bar bit
-watering cart
-Asiatic sweetleaf
-artificial joint
-chariot
-casern
-charge-exchange accelerator
-display adapter
-hornpipe
-honey bell
-planula
-Nephthytis afzelii
-hame
-ranter
-trachodon
-synchrocyclotron
-splasher
-heterotroph
-Nicol prism
-Himalayan rhubarb
-headfast
-put-put
-bitter almond
-parr
-scantling
-power breakfast
-madder
-Catalpa bignioides
-rose of Jericho
-spark chamber
-rhizome
-beard worm
-supper club
-negro peach
-keratoscope
-wain
-apple aphid
-planking
-time-delay measuring instrument
-sternpost
-sicklepod
-lake bed
-gatherer
-monotype
-dead-man's float
-poison gas
-dicynodont
-organism
-cell
-person
-animal
-plant
-food
-artifact
-dressage
-contact sport
-outdoor sport
-gymnastics
-track and field
-jumping
-high jump
-skiing
-water sport
-swimming
-dive
-floating
-skin diving
-rowing
-boxing
-sledding
-tobogganing
-wrestling
-skating
-ice skating
-roller skating
-racing
-boat racing
-riding
-equestrian sport
-cycling
-blood sport
-hunt
-fishing
-angling
-casting
-athletic game
-outdoor game
-golf
-field game
-field hockey
-football
-American football
-ball game
-baseball
-court game
-badminton
-basketball
-tennis
-sport
-Seder
-scavenger
-bottom-feeder
-work animal
-beast of burden
-pack animal
-domestic animal
-marine animal
-female
-male
-young
-young mammal
-pup
-cub
-lion cub
-tiger cub
-microorganism
-arbovirus
-herpes
-herpes zoster
-reovirus
-moneran
-cyanobacteria
-enteric bacteria
-actinomycete
-streptomyces
-diplococcus
-parasite
-ectoparasite
-protoctist
-protozoan
-sarcodinian
-ameba
-ciliate
-alga
-brown algae
-green algae
-sporozoan
-cypriniform fish
-cyprinid
-carp
-domestic carp
-shiner
-catostomid
-buffalo fish
-cyprinodont
-killifish
-topminnow
-squirrelfish
-stickleback
-pipefish
-embryo
-fetus
-blastula
-chordate
-cephalochordate
-tunicate
-ascidian
-vertebrate
-aquatic vertebrate
-jawless vertebrate
-lamprey
-hagfish
-cartilaginous fish
-holocephalan
-chimaera
-elasmobranch
-shark
-mackerel shark
-mako
-requiem shark
-dogfish
-smooth dogfish
-spiny dogfish
-smooth hammerhead
-smalleye hammerhead
-shovelhead
-ray
-sawfish
-roughtail stingray
-butterfly ray
-eagle ray
-manta
-skate
-bird
-gamecock
-night bird
-ratite
-passerine
-oscine
-accentor
-lark
-pipit
-finch
-canary
-dark-eyed junco
-New World sparrow
-bunting
-honeycreeper
-sparrow
-grosbeak
-towhee
-weaver
-grassfinch
-tyrannid
-New World flycatcher
-kingbird
-pewee
-cotinga
-antbird
-Old World flycatcher
-thrush
-nightingale
-Old World chat
-warbler
-kinglet
-Old World warbler
-New World warbler
-flycatching warbler
-New World chat
-yellowthroat
-New World oriole
-northern oriole
-meadowlark
-New World blackbird
-grackle
-Old World oriole
-starling
-myna
-corvine bird
-crow
-Old World jay
-common European jay
-New World jay
-blue jay
-Canada jay
-Rocky Mountain jay
-nutcracker
-European magpie
-American magpie
-Australian magpie
-wren
-marsh wren
-thrasher
-New Zealand wren
-creeper
-titmouse
-black-capped chickadee
-Carolina chickadee
-swallow
-martin
-tanager
-shrike
-butcherbird
-bush shrike
-bowerbird
-European water ouzel
-American water ouzel
-vireo
-waxwing
-bird of prey
-hawk
-black kite
-swallow-tailed kite
-white-tailed kite
-harrier
-falcon
-peregrine
-caracara
-eagle
-young bird
-sea eagle
-Aegypiidae
-Old World vulture
-griffon vulture
-bearded vulture
-Egyptian vulture
-black vulture
-New World vulture
-buzzard
-condor
-Andean condor
-California condor
-black vulture
-king vulture
-owl
-horned owl
-scops owl
-amphibian
-salamander
-newt
-Pacific newt
-ambystomid
-climbing salamander
-web-toed salamander
-frog
-true frog
-true toad
-spadefoot
-tree toad
-cricket frog
-tongueless frog
-reptile
-anapsid
-diapsid
-chelonian
-turtle
-sea turtle
-ridley
-snapping turtle
-musk turtle
-diamondback terrapin
-Western box turtle
-tortoise
-soft-shelled turtle
-saurian
-lizard
-gecko
-iguanid
-spiny lizard
-fence lizard
-horned lizard
-skink
-teiid lizard
-racerunner
-plateau striped whiptail
-Chihuahuan spotted whiptail
-western whiptail
-checkered whiptail
-agamid
-moloch
-anguid lizard
-venomous lizard
-lacertid lizard
-chameleon
-monitor
-crocodilian reptile
-crocodile
-alligator
-caiman
-armored dinosaur
-ankylosaur
-bone-headed dinosaur
-ceratopsian
-hadrosaur
-saurischian
-sauropod
-theropod
-ceratosaur
-maniraptor
-synapsid
-pterosaur
-ichthyosaur
-snake
-colubrid snake
-smooth green snake
-rough green snake
-racer
-blacksnake
-whip-snake
-rat snake
-bull snake
-common kingsnake
-milk snake
-common garter snake
-ribbon snake
-Western ribbon snake
-common water snake
-water moccasin
-grass snake
-viperine grass snake
-sand snake
-lyre snake
-blind snake
-indigo snake
-constrictor
-boa
-python
-elapid
-coral snake
-coral snake
-cobra
-mamba
-black mamba
-krait
-viper
-pit viper
-rattlesnake
-timber rattlesnake
-arthropod
-arachnid
-false scorpion
-whip-scorpion
-spider
-European wolf spider
-acarine
-hard tick
-Ixodes dammini
-Ixodes neotomae
-Ixodes pacificus
-Ixodes scapularis
-sheep-tick
-Ixodes persulcatus
-Ixodes dentatus
-Ixodes spinipalpis
-wood tick
-soft tick
-mite
-trombiculid
-spider mite
-house centipede
-gallinaceous bird
-domestic fowl
-jungle fowl
-chicken
-cock
-hen
-turkey
-grouse
-European black grouse
-Asian black grouse
-blackcock
-greyhen
-red grouse
-moorhen
-greater prairie chicken
-lesser prairie chicken
-heath hen
-guan
-chachalaca
-megapode
-mallee fowl
-phasianid
-pheasant
-bobwhite
-northern bobwhite
-Old World quail
-migratory quail
-peafowl
-California quail
-Hungarian partridge
-red-legged partridge
-Greek partridge
-mountain quail
-guinea fowl
-columbiform bird
-pigeon
-dove
-turtledove
-domestic pigeon
-homing pigeon
-sandgrouse
-parrot
-cockatoo
-lory
-varied Lorikeet
-rainbow lorikeet
-parakeet
-cuculiform bird
-cuckoo
-crow pheasant
-coraciiform bird
-roller
-kingfisher
-hoopoe
-apodiform bird
-swift
-Archilochus colubris
-thornbill
-goatsucker
-piciform bird
-woodpecker
-flicker
-sapsucker
-toucanet
-trogon
-quetzal
-aquatic bird
-waterfowl
-anseriform bird
-duck
-teal
-widgeon
-sheldrake
-goldeneye
-scaup
-wood duck
-sea duck
-scoter
-merganser
-gosling
-gander
-Chinese goose
-greylag
-blue goose
-snow goose
-brant
-common brant goose
-honker
-barnacle goose
-swan
-tundra swan
-screamer
-crested screamer
-mammal
-prototherian
-monotreme
-marsupial
-opossum
-bandicoot
-kangaroo
-common wallaby
-hare wallaby
-nail-tailed wallaby
-rock wallaby
-pademelon
-tree wallaby
-rat kangaroo
-phalanger
-dasyurid marsupial
-dasyure
-placental
-calf
-buck
-insectivore
-mole
-shrew mole
-shrew
-water shrew
-tenrec
-invertebrate
-sponge
-glass sponge
-coelenterate
-Chrysaora quinquecirrha
-hydrozoan
-siphonophore
-anthozoan
-actinia
-coral
-gorgonian
-stony coral
-ctenophore
-worm
-planarian
-fluke
-liver fluke
-Fasciolopsis buski
-schistosome
-tapeworm
-echinococcus
-taenia
-common roundworm
-chicken roundworm
-pinworm
-eelworm
-vinegar eel
-trichina
-hookworm
-filaria
-Guinea worm
-annelid
-oligochaete
-polychaete
-leech
-mollusk
-scaphopod
-gastropod
-abalone
-scorpion shell
-giant conch
-edible snail
-garden snail
-brown snail
-Helix hortensis
-seasnail
-neritid
-limpet
-Hermissenda crassicornis
-cowrie
-bivalve
-clam
-quahog
-cockle
-oyster
-mussel
-marine mussel
-freshwater mussel
-scallop
-shipworm
-cephalopod
-octopod
-decapod
-squid
-crustacean
-malacostracan crustacean
-decapod crustacean
-crab
-swimming crab
-spider crab
-lobster
-true lobster
-Old World crayfish
-American crayfish
-shrimp
-prawn
-krill
-stomatopod
-mantis shrimp
-woodlouse
-pill bug
-sow bug
-sea louse
-amphipod
-copepod
-barnacle
-wading bird
-stork
-ibis
-common spoonbill
-roseate spoonbill
-heron
-egret
-night heron
-American bittern
-European bittern
-least bittern
-whooping crane
-rail
-crake
-gallinule
-purple gallinule
-coot
-great bustard
-plain turkey
-button quail
-trumpeter
-seabird
-shorebird
-plover
-turnstone
-sandpiper
-yellowlegs
-ruff
-tattler
-woodcock
-snipe
-greyback
-red-breasted snipe
-curlew
-godwit
-stilt
-stilt
-phalarope
-courser
-coastal diving bird
-larid
-gull
-tern
-jaeger
-skua
-auk
-guillemot
-murre
-puffin
-gaviiform seabird
-podicipitiform seabird
-grebe
-pelecaniform seabird
-white pelican
-Old world white pelican
-gannet
-snakebird
-sphenisciform seabird
-penguin
-pelagic bird
-wandering albatross
-black-footed albatross
-petrel
-shearwater
-storm petrel
-aquatic mammal
-cetacean
-whale
-baleen whale
-rorqual
-toothed whale
-beaked whale
-dolphin
-bottlenose dolphin
-porpoise
-sea cow
-carnivore
-pinniped mammal
-seal
-eared seal
-fur seal
-fur seal
-South American sea lion
-California sea lion
-Australian sea lion
-Steller sea lion
-earless seal
-walrus
-canine
-bitch
-dog
-cur
-toy dog
-toy spaniel
-English toy spaniel
-hunting dog
-hound
-coonhound
-dachshund
-foxhound
-wolfhound
-greyhound
-terrier
-bullterrier
-rat terrier
-Manchester terrier
-fox terrier
-wirehair
-Welsh terrier
-schnauzer
-Skye terrier
-sporting dog
-retriever
-pointer
-setter
-spaniel
-springer spaniel
-water spaniel
-working dog
-watchdog
-shepherd dog
-Belgian sheepdog
-pinscher
-Sennenhunde
-mastiff
-bulldog
-guide dog
-sled dog
-liver-spotted dalmatian
-spitz
-griffon
-corgi
-poodle
-wolf
-coydog
-wild dog
-striped hyena
-brown hyena
-spotted hyena
-aardwolf
-fox
-black fox
-silver fox
-blue fox
-feline
-cat
-domestic cat
-tom
-blue point Siamese
-wildcat
-common lynx
-Canada lynx
-bobcat
-spotted lynx
-caracal
-big cat
-leopardess
-panther
-lioness
-lionet
-Bengal tiger
-tigress
-saber-toothed tiger
-bear
-Syrian bear
-grizzly
-Alaskan brown bear
-cinnamon bear
-viverrine
-civet
-Indian mongoose
-ichneumon
-slender-tailed meerkat
-suricate
-bat
-fruit bat
-carnivorous bat
-leafnose bat
-false vampire
-vespertilian bat
-long-eared bat
-freetail
-vampire bat
-predator
-game
-game bird
-fossorial mammal
-tetrapod
-insect
-beetle
-two-spotted ladybug
-Mexican bean beetle
-Hippodamia convergens
-vedalia
-bombardier beetle
-calosoma
-searcher
-firefly
-sawyer
-pine sawyer
-flea beetle
-Colorado potato beetle
-carpet beetle
-clerid beetle
-lamellicorn beetle
-scarabaeid beetle
-scarab
-tumblebug
-dorbeetle
-June beetle
-melolonthid beetle
-elaterid beetle
-snout beetle
-boll weevil
-blister beetle
-bark beetle
-darkling beetle
-flour beetle
-seed beetle
-pea weevil
-bean weevil
-rice weevil
-louse
-flea
-dipterous insect
-gall midge
-housefly
-tsetse fly
-blowfly
-bluebottle
-greenbottle
-flesh fly
-tachina fly
-gadfly
-botfly
-human botfly
-sheep botfly
-warble fly
-horsefly
-bee fly
-fruit fly
-louse fly
-horn fly
-mosquito
-gnat
-fungus gnat
-hymenopterous insect
-drone
-worker
-honeybee
-Africanized bee
-black bee
-Carniolan bee
-Italian bee
-carpenter bee
-bumblebee
-cuckoo-bumblebee
-andrena
-Nomia melanderi
-leaf-cutting bee
-mason bee
-potter bee
-wasp
-vespid
-paper wasp
-hornet
-sphecoid wasp
-digger wasp
-chalcid fly
-sawfly
-pharaoh ant
-little black ant
-army ant
-carpenter ant
-fire ant
-wood ant
-slave ant
-Formica fusca
-slave-making ant
-sanguinary ant
-bulldog ant
-Amazon ant
-termite
-dry-wood termite
-orthopterous insect
-short-horned grasshopper
-locust
-migratory locust
-migratory grasshopper
-long-horned grasshopper
-katydid
-mormon cricket
-sand cricket
-mole cricket
-European house cricket
-field cricket
-tree cricket
-snowy tree cricket
-phasmid
-diapheromera
-oriental cockroach
-American cockroach
-Australian cockroach
-German cockroach
-giant cockroach
-praying mantis
-hemipterous insect
-leaf bug
-mirid bug
-lygus bug
-lygaeid
-coreid bug
-heteropterous insect
-water bug
-water strider
-assassin bug
-homopterous insect
-whitefly
-sweet-potato whitefly
-coccid insect
-scale insect
-soft scale
-armored scale
-mealybug
-plant louse
-aphid
-greenfly
-woolly aphid
-adelgid
-dog-day cicada
-seventeen-year locust
-spittle insect
-plant hopper
-psocopterous insect
-psocid
-booklouse
-ephemerid
-neuropteron
-green lacewing
-brown lacewing
-odonate
-trichopterous insect
-caseworm
-thysanuran insect
-bristletail
-thysanopter
-thrips
-earwig
-lepidopterous insect
-butterfly
-nymphalid
-fritillary
-emperor butterfly
-danaid
-pierid
-small white
-large white
-southern cabbage butterfly
-blue
-copper
-American copper
-hairstreak
-Strymon melinus
-moth
-tortricid
-lymantriid
-geometrid
-cankerworm
-pyralid
-tineoid
-tineid
-clothes moth
-gelechiid
-grain moth
-noctuid moth
-cutworm
-underwing
-hawkmoth
-bombycid
-saturniid
-giant silkworm moth
-silkworm
-arctiid
-lasiocampid
-tent caterpillar
-webworm
-webworm moth
-caterpillar
-bollworm
-woolly bear
-larva
-grub
-pupa
-queen
-echinoderm
-basket star
-edible sea urchin
-sand dollar
-heart urchin
-crinoid
-trepang
-lagomorph
-leporid
-rabbit
-eastern cottontail
-swamp rabbit
-marsh hare
-leveret
-European hare
-jackrabbit
-white-tailed jackrabbit
-blacktail jackrabbit
-polar hare
-snowshoe hare
-pika
-rodent
-mouse
-rat
-pocket rat
-field mouse
-brown rat
-jerboa rat
-water rat
-New World mouse
-wood mouse
-wood rat
-vole
-packrat
-Eurasian hamster
-golden hamster
-gerbil
-lemming
-pied lemming
-Old World porcupine
-brush-tailed porcupine
-long-tailed porcupine
-New World porcupine
-Canada porcupine
-pocket mouse
-kangaroo rat
-jumping mouse
-jerboa
-dormouse
-gopher
-squirrel
-tree squirrel
-ground squirrel
-prairie dog
-American flying squirrel
-groundhog
-hoary marmot
-yellowbelly marmot
-Old World beaver
-New World beaver
-cavy
-naked mole rat
-ungulate
-hyrax
-odd-toed ungulate
-equine
-horse
-foal
-colt
-male horse
-stallion
-mare
-saddle horse
-warhorse
-pony
-mustang
-bronco
-wild horse
-pony
-racehorse
-racer
-harness horse
-workhorse
-draft horse
-trotting horse
-ass
-domestic ass
-wild ass
-onager
-common zebra
-mountain zebra
-grevy's zebra
-rhinoceros
-tapir
-even-toed ungulate
-swine
-piglet
-porker
-peccary
-ruminant
-bovid
-bovine
-ox
-cattle
-bull
-cow
-beef
-Brahman
-dairy cattle
-Old World buffalo
-Indian buffalo
-carabao
-Asian wild ox
-American bison
-wisent
-sheep
-lamb
-domestic sheep
-wild sheep
-mountain sheep
-goat
-domestic goat
-wild goat
-goat antelope
-antelope
-Thomson's gazelle
-Gazella subgutturosa
-springbok
-kudu
-harnessed antelope
-eland
-waterbuck
-oryx
-deer
-stag
-red deer
-mule deer
-roe deer
-caribou
-chevrotain
-camel
-domestic llama
-guanaco
-alpaca
-giraffe
-musteline mammal
-ermine
-stoat
-New World least weasel
-Old World least weasel
-longtail weasel
-American mink
-ferret
-muishond
-snake muishond
-striped muishond
-river otter
-Eurasian otter
-striped skunk
-hooded skunk
-hog-nosed skunk
-spotted skunk
-American badger
-Eurasian badger
-ferret badger
-hog badger
-marten
-pachyderm
-edentate
-peba
-apar
-tatouay
-peludo
-giant armadillo
-pichiciago
-sloth
-anteater
-primate
-ape
-anthropoid ape
-hominoid
-hominid
-homo
-Homo erectus
-Homo sapiens
-australopithecine
-great ape
-western lowland gorilla
-eastern lowland gorilla
-mountain gorilla
-silverback
-western chimpanzee
-eastern chimpanzee
-central chimpanzee
-pygmy chimpanzee
-lesser ape
-monkey
-Old World monkey
-talapoin
-grivet
-vervet
-green monkey
-chacma
-mandrill
-drill
-rhesus
-bonnet macaque
-Barbary ape
-crab-eating macaque
-entellus
-guereza
-New World monkey
-true marmoset
-pygmy marmoset
-tamarin
-silky tamarin
-pinche
-lemur
-tarsier
-flying lemur
-proboscidean
-elephant
-mammoth
-procyonid
-raccoon
-fish
-food fish
-young fish
-crossopterygian
-lungfish
-catfish
-silurid
-bullhead
-channel catfish
-gadoid
-cod
-hake
-elver
-common eel
-tuna
-moray
-conger
-teleost fish
-clupeid fish
-shad
-herring
-sardine
-pilchard
-anchovy
-salmonid
-salmon
-Atlantic salmon
-trout
-brown trout
-char
-whitefish
-smelt
-tarpon
-ribbonfish
-toadfish
-needlefish
-flying fish
-spiny-finned fish
-percoid fish
-perch
-pike-perch
-walleye
-robalo
-pike
-pickerel
-sunfish
-crappie
-freshwater bream
-black bass
-bass
-serranid fish
-grouper
-hind
-surfperch
-cardinalfish
-remora
-carangid fish
-jack
-moonfish
-pompano
-scad
-dolphinfish
-characin
-cichlid
-snapper
-grunt
-sparid
-sea bream
-porgy
-sciaenid fish
-croaker
-whiting
-sea trout
-mullet
-goatfish
-mullet
-silversides
-barracuda
-sea chub
-butterfly fish
-damselfish
-clown anemone fish
-wrasse
-blenny
-pikeblenny
-gunnel
-goby
-gempylid
-scombroid
-mackerel
-Spanish mackerel
-tuna
-bonito
-sailfish
-billfish
-marlin
-tripletail
-mojarra
-ganoid
-Pacific sturgeon
-beluga
-scorpaenoid
-scorpaenid
-scorpionfish
-rockfish
-lumpfish
-greenling
-gurnard
-sea robin
-plectognath
-triggerfish
-filefish
-boxfish
-spiny puffer
-ocean sunfish
-flatfish
-righteye flounder
-lefteye flounder
-whiff
-sole
-abbey
-abbey
-abrader
-accelerator
-accessory
-accommodation
-acoustic device
-acoustic modem
-acrylic
-action
-actuator
-adhesive bandage
-adjustable wrench
-aeolian harp
-aerosol
-after-shave
-airbus
-aircraft
-airfield
-airfoil
-air gun
-airplane
-air pump
-air-to-air missile
-air-to-ground missile
-alarm
-alb
-alcazar
-Allen screw
-alms dish
-altimeter
-Amati
-ammeter
-ammunition
-amplifier
-analog computer
-analytical balance
-anchor
-anchor chain
-aneroid barometer
-angledozer
-anklet
-antenna
-anteroom
-antiaircraft
-antiballistic missile
-apartment
-apartment building
-aperture
-apparatus
-apparel
-appliance
-appliance
-applicator
-aquarium
-arbor
-arcade
-arch
-arc lamp
-area
-argyle
-arm
-armament
-armature
-armchair
-armoire
-armor
-armored vehicle
-armor plate
-armrest
-array
-arrow
-artificial heart
-artillery
-assembly
-assembly plant
-astrodome
-astronomical telescope
-athletic sock
-atom bomb
-atomic clock
-atomizer
-attachment
-attack submarine
-attire
-audiocassette
-audio system
-audiotape
-auditorium
-autoclave
-autoinjector
-autoloader
-automat
-automat
-automatic firearm
-automatic rifle
-automaton
-auxiliary research submarine
-awl
-ax
-axis
-axle
-axletree
-baby bed
-baby buggy
-baby grand
-back
-background
-backseat
-badminton equipment
-badminton racket
-bag
-bag
-bag
-baggage
-bagpipe
-bait
-balance
-balcony
-balcony
-bale
-ball
-ball gown
-ballistic missile
-ballistic pendulum
-ball-peen hammer
-ballroom
-band
-bandage
-bandanna
-banderilla
-bar
-bar
-barbed wire
-barge
-barge pole
-barn door
-barograph
-barrack
-barrage balloon
-barrel knot
-barrel vault
-barrier
-barroom
-base
-base
-baseball equipment
-basilica
-basin
-basket
-basketball equipment
-bass
-bass drum
-bass horn
-bastion
-bat
-bathhouse
-battery
-battle-ax
-battle dress
-battleship
-bay rum
-bay window
-beading plane
-beam
-beam balance
-bearing
-beater
-beating-reed instrument
-bed
-bed
-bedclothes
-bedroom
-bedroom furniture
-bedspread
-bedspring
-beehive
-beer barrel
-bell
-bell push
-bell tower
-belt
-belt buckle
-bench
-berlin
-berth
-besom
-bevel gear
-bicycle
-bicycle chain
-bier
-billiard ball
-bin
-binding
-bin liner
-binocular microscope
-bioscope
-birchbark canoe
-bird shot
-bistro
-bit
-bit
-black tie
-blade
-blade
-blanket
-blimp
-blind
-block
-block plane
-blouse
-blower
-blowtorch
-bludgeon
-boarding
-boarding house
-boardroom
-boat
-bobbin
-body
-body armor
-body lotion
-boiler
-bolt
-bolt
-bomb
-bomber
-bongo
-boom
-boom
-boomerang
-boot
-booth
-booth
-bore bit
-Boston rocker
-bota
-bottle
-bottle opener
-bow
-bow
-bowed stringed instrument
-bowl
-bowl
-bowline
-bowling equipment
-bowling pin
-bowsprit
-box
-box
-boxcar
-boxing equipment
-brace
-brace
-bracelet
-bracket
-brake
-brake system
-brass
-brasserie
-brazier
-breechcloth
-breeches
-brewpub
-brick
-bricklayer's hammer
-brickwork
-bridal gown
-bridge
-briefcase
-brigandine
-brilliant pebble
-brim
-broad arrow
-broadax
-broad hatchet
-broadsword
-brush
-bubble jet printer
-buffer
-buffet
-building
-building complex
-bulldozer
-bullet
-bullhorn
-bullnose
-bundle
-bunker
-burial chamber
-burner
-bus
-business suit
-butt joint
-button
-buttress
-butt shaft
-buzz bomb
-cabaret
-caber
-cabin
-cabin
-cabinet
-cabinet
-cabin liner
-cable
-cable
-cafe
-cafeteria
-cafeteria tray
-caff
-cage
-calculator
-caliper
-calorimeter
-camera
-camera lens
-camera tripod
-camp
-camp
-camp chair
-camper
-can
-canal
-candelabrum
-candlestick
-cane
-cannikin
-cannon
-cannon
-cannonball
-canopy
-canteen
-canteen
-canvas
-canvas tent
-cap
-cap
-cap
-capacitor
-caparison
-cape
-cap screw
-capsule
-car
-car
-carbine
-carbon arc lamp
-card index
-cardioid microphone
-car door
-cargo liner
-cargo ship
-carillon
-carpenter's hammer
-carpenter's level
-carpenter's mallet
-carpenter's rule
-carpet tack
-carriage
-carriage
-carriage bolt
-carrick bend
-carrier
-car seat
-cart
-cartridge
-cartridge belt
-cartridge holder
-case
-case
-cashbox
-casque
-casserole
-cassock
-catch
-catcher's mask
-cathedra
-cathedral
-cathedral
-catheter
-cathode
-cathode-ray tube
-cat's-paw
-cattle car
-cautery
-cavalry sword
-cedar chest
-cell
-cell
-cellblock
-center
-centrifuge
-ceramic
-ceramic ware
-chain tongs
-chair
-chair of state
-chalk
-chamfer plane
-chandlery
-chapel
-character printer
-chassis
-chasuble
-chatelaine
-checker
-cheeseboard
-chemical reactor
-chessman
-chest of drawers
-child's room
-china
-chip
-chip
-chisel
-choke
-chokey
-chordophone
-chronoscope
-chuck
-church key
-cigar lighter
-circle
-circuit
-circuit board
-circular plane
-circular saw
-cistern
-civilian clothing
-clamp
-clamshell
-clarinet
-classroom
-clavier
-cleaning implement
-cleaning pad
-clean room
-clinic
-clip
-cloak
-clock
-closed circuit
-closed-circuit television
-closet
-cloth covering
-clothes closet
-clothes dryer
-clothes hamper
-clothes tree
-clothing
-clothing store
-clout nail
-clove hitch
-clutch
-coach
-coal car
-coal shovel
-coat
-coat closet
-coating
-coating
-coat of paint
-coaxial cable
-cocked hat
-coffee cup
-coffee maker
-coffer
-coffin
-coil
-colander
-collider
-cologne
-colonnade
-color television
-Colt
-column
-column
-comb
-comb
-combination plane
-combine
-commissary
-commodity
-communication system
-commutator
-compact disk
-compartment
-compass
-compass card
-compound lens
-compound lever
-compressor
-computer
-computer circuit
-computer network
-computer screen
-computer system
-concentration camp
-concert grand
-concertina
-condenser
-condenser
-condenser microphone
-conductor
-connecting rod
-connection
-conservatory
-conservatory
-contact
-container
-contrabassoon
-control
-control panel
-control system
-convent
-converging lens
-converter
-convertible
-conveyance
-cooker
-cooking utensil
-cooler
-cooling system
-cord
-cord
-cordage
-corner
-correctional institution
-corset
-cosmetic
-costume
-costume
-cotter
-cotton
-counter
-counter
-counter
-counter tube
-country house
-coupling
-court
-court
-coverall
-covering
-cowbarn
-craft
-cravat
-crazy quilt
-cream
-cream pitcher
-crematory
-crepe
-crib
-cricket equipment
-croquet equipment
-crossbar
-crossbow
-crosspiece
-crown jewels
-cruiser
-cruiser
-cruise ship
-crystal microphone
-cudgel
-cuff
-cultivator
-cup
-cupboard
-cupola
-curb roof
-curtain
-cutout
-cutter
-cutting implement
-cybercafe
-cyclotron
-cylinder
-cymbal
-dado plane
-dagger
-damper
-dart
-data converter
-data input device
-davenport
-davenport
-davit
-dead axle
-deck
-deck
-deck chair
-deep-freeze
-defensive structure
-delay line
-delicatessen
-dental appliance
-denture
-depilatory
-depressor
-depth finder
-derrick
-destroyer
-detector
-detector
-detonating fuse
-detonator
-developer
-device
-dial
-dialyzer
-diathermy machine
-diesel locomotive
-digital camera
-digital computer
-digital display
-diner
-dinghy
-dining car
-dining-hall
-dining room
-dining-room furniture
-dining-room table
-dinner dress
-dinner pail
-dinner table
-diode
-dip
-diplomatic building
-dipper
-DIP switch
-directional antenna
-directional microphone
-direction finder
-disguise
-dish
-dish
-disk
-dispenser
-display
-display panel
-distillery
-ditch
-ditch spade
-dive bomber
-doll
-dolmen
-domino
-door
-doorbell
-doorlock
-doornail
-dormer window
-dormitory
-dot matrix printer
-double-breasted suit
-double-reed instrument
-douche
-dovecote
-dovetail plane
-downstage
-drafting instrument
-Dragunov
-drawstring bag
-dray
-dredging bucket
-dress
-dress blues
-dressing
-dress uniform
-drill
-electric drill
-drill rig
-drinking fountain
-drinking vessel
-drip mat
-drip pot
-drive
-drive
-drogue
-drogue parachute
-drop-leaf table
-dry battery
-dry dock
-dryer
-dry masonry
-dry wall
-dugout canoe
-dumdum
-dumpcart
-dune buggy
-dungeon
-duplicator
-dustmop
-dwelling
-earphone
-earthenware
-easel
-easy chair
-edge tool
-eiderdown
-elastic bandage
-electrical converter
-electrical device
-electric bell
-electric frying pan
-electric furnace
-electric heater
-electric lamp
-electric motor
-electric refrigerator
-electro-acoustic transducer
-electrode
-electromagnet
-electronic balance
-electronic device
-electronic equipment
-electronic instrument
-electronic voltmeter
-electron microscope
-electrostatic generator
-electrostatic printer
-elevator
-embankment
-embellishment
-enamel
-enamelware
-enclosure
-endoscope
-engine
-engine
-ensemble
-entrenching tool
-epidiascope
-equipment
-eraser
-escutcheon
-espadrille
-espresso shop
-establishment
-estaminet
-exercise device
-exhaust fan
-exhibition hall
-Exocet
-expansion bolt
-explosive device
-external-combustion engine
-extractor
-fabric
-face mask
-face veil
-facing
-factory
-fairlead
-false face
-fan
-farm building
-farm machine
-fastener
-fatigues
-faucet
-feedback circuit
-fence
-fencing sword
-fender
-ferry
-fetoscope
-field-sequential color television
-fife
-fifth wheel
-fighter
-figure eight
-file
-file server
-filling
-film
-film
-filter
-filter
-finery
-finisher
-fipple flute
-fire
-firearm
-fire iron
-fireplace
-firkin
-fisherman's bend
-fisherman's knot
-fisherman's lure
-fishing boat
-fishing rod
-fishnet
-flag
-flageolet
-flambeau
-flannelette
-flap
-flashlight
-flask
-flatcar
-flat tip screwdriver
-fleet ballistic missile submarine
-flight simulator
-flip-flop
-floating dock
-floor
-floor
-floor cover
-fly
-flywheel
-fob
-foghorn
-folder
-food hamper
-footbath
-footbridge
-foothold
-foot rule
-footwear
-footwear
-forceps
-fore-and-aft sail
-foremast
-fore plane
-fore-topmast
-fork
-formalwear
-fortification
-fortress
-foundation garment
-foundry
-fragmentation bomb
-framework
-free-reed instrument
-freight train
-French door
-friary
-friction clutch
-frigate
-frill
-frock coat
-front projector
-fruit machine
-full-dress uniform
-full metal jacket
-funny wagon
-fur hat
-furnace
-furnishing
-furniture
-fuse
-gable
-gable roof
-gaff
-galleon
-gallery
-galley
-galley
-gallows
-galvanometer
-gambling house
-game
-game equipment
-gamp
-garage
-Garand rifle
-garden
-garden spade
-garden tool
-garment
-gas burner
-gas-discharge tube
-gasket
-gasoline engine
-gate
-gatehouse
-gatepost
-gathered skirt
-gauge
-gauze
-gauze
-gavel
-gear
-gear
-gear
-gearing
-general-purpose bomb
-generator
-generator
-Geneva gown
-geodesic dome
-girder
-glass
-glider
-glove
-glyptic art
-goal
-golf club
-golf equipment
-Gordian knot
-Gothic arch
-government building
-government office
-gown
-gramophone
-granary
-granny knot
-grapnel
-grapnel
-grate
-graver
-greasy spoon
-greatcoat
-great hall
-greengrocery
-grenade
-grillroom
-groined vault
-Guarnerius
-guidance system
-guided missile
-guildhall
-guitar
-guitar pick
-gun
-gun carriage
-gunlock
-gunsight
-gun trigger
-gurney
-gymnastic apparatus
-gym shoe
-gypsy cab
-habergeon
-habit
-hairdressing
-hairpiece
-hairpin
-half hatchet
-half hitch
-hall
-hall
-hammer
-hand
-handbell
-handbow
-handcart
-hand glass
-handloom
-hand lotion
-hand mower
-handsaw
-hand shovel
-hand tool
-handwear
-handwheel
-hanger
-hank
-harpsichord
-harrow
-hash house
-hat
-hatch
-hauberk
-hawser bend
-hazard
-head
-head
-head covering
-headdress
-header
-headgear
-headlight
-headsail
-headscarf
-health spa
-heat engine
-heater
-heat lamp
-heat-seeking missile
-heavier-than-air craft
-heckelphone
-hedge
-helicopter
-helm
-helmet
-helmet
-heraldry
-high altar
-high-angle gun
-high gear
-high table
-hinge
-hip boot
-hitch
-hoe
-hogshead
-hoist
-holder
-holding device
-home appliance
-homespun
-hood
-hood
-hood
-hook
-Hoover
-hope chest
-horn
-horn button
-horse
-horsecar
-horse-drawn vehicle
-horsehair wig
-hosiery
-hospital
-hospital room
-hostel
-hot-air balloon
-hotel
-hotel room
-hot tub
-house
-house
-housing
-hovel
-huarache
-humeral veil
-hut
-hutch
-hydraulic brake
-hydraulic system
-hydroelectric turbine
-hydrofoil
-hydrometer
-hygrometer
-hypermarket
-hypodermic syringe
-ice machine
-ice rink
-ice skate
-icetray
-ignition switch
-impact printer
-implant
-implement
-imprint
-improvised explosive device
-inclined plane
-indicator
-induction coil
-ink-jet printer
-inkstand
-institution
-instrument
-instrument of punishment
-instrument of torture
-interceptor
-interchange
-intercommunication system
-intercontinental ballistic missile
-interface
-interior door
-internal-combustion engine
-ionization chamber
-video iPod
-iron
-jack
-jack
-jacket
-jacket
-jack plane
-jail
-jamb
-jar
-jeroboam
-jet
-jet engine
-jewelled headdress
-jib
-jibboom
-jiggermast
-joint
-jointer
-joist
-jolly boat
-jug
-jumper
-jumper cable
-junction
-junction
-jury mast
-kayak
-keel
-keg
-kerchief
-kettle
-key
-key
-keyboard
-keyboard instrument
-khakis
-kiln
-kinescope
-kingbolt
-kirk
-kit
-kit
-kitbag
-kitchen
-kitchen appliance
-kitchen utensil
-kite balloon
-knee-high
-knife
-knife
-knit
-knob
-lace
-lacquer
-ladder truck
-lag screw
-lamasery
-laminate
-lamination
-lamp
-lamp
-landing gear
-land mine
-lantern
-lapel
-lathe
-lattice
-launcher
-lead-acid battery
-leather strip
-Leclanche cell
-leg
-legging
-lens
-lens implant
-level
-lever
-Levi's
-lid
-life buoy
-life jacket
-life preserver
-lifting device
-ligament
-light
-light-emitting diode
-lighter-than-air craft
-lighting
-light microscope
-linear accelerator
-line printer
-lingerie
-lining
-liquid crystal display
-lister
-living quarters
-living room
-local area network
-lock
-locomotive
-lodge
-lodging house
-loft
-loft
-longbow
-lookout
-loom
-loop knot
-lota
-lounge
-loungewear
-love knot
-lunchroom
-luxury liner
-lyre
-machine
-machine
-machine bolt
-machine gun
-machinery
-machine screw
-machine tool
-magic lantern
-magnet
-magnetic disk
-magnetic recorder
-magnetic tape
-magnifier
-magnum
-magnus hitch
-mailer
-mainframe
-mainmast
-main-topmast
-main yard
-makeup
-mallet
-mallet
-mallet
-mandolin
-manger
-man-of-war
-manometer
-MANPAD
-mansard
-mansion
-marina
-marker
-marketplace
-maser
-mask
-masonry
-mass spectrometer
-mast
-mast
-mat
-mat
-match
-match
-match plane
-material
-materiel
-Matthew Walker
-maul
-measure
-measuring instrument
-measuring stick
-mechanical device
-mechanical system
-mechanism
-medical building
-medical instrument
-memorial
-memory
-memory chip
-memory device
-menhir
-man's clothing
-mercantile establishment
-mercury barometer
-mercury thermometer
-mercury-vapor lamp
-mess
-metal screw
-meteorological balloon
-meter
-meterstick
-microbalance
-microfilm
-microscope
-military hospital
-military quarters
-military vehicle
-mill
-milldam
-millinery
-mine
-minibike
-mink
-minster
-Minuteman
-mirror
-mixer
-mizzenmast
-module
-mold
-moldboard plow
-monitor
-monitor
-morgue
-mortise joint
-motion-picture camera
-motion-picture film
-motor
-motorboat
-motorcycle
-motor hotel
-motor vehicle
-mound
-mount
-mouse button
-movie projector
-moving-coil galvanometer
-mug
-multiplex
-multiplexer
-musette pipe
-mushroom anchor
-musical instrument
-musket
-musket ball
-muslin
-muzzle loader
-narrowbody aircraft
-nautilus
-navigational system
-naval equipment
-naval gun
-naval radar
-naval weaponry
-navigational instrument
-nebuchadnezzar
-neckline
-neckpiece
-necktie
-neckwear
-needle
-needlework
-negligee
-net
-net
-net
-net
-network
-network
-night bell
-nightwear
-noisemaker
-nonsmoker
-non-volatile storage
-nose flute
-nuclear reactor
-nuclear weapon
-nursery
-oar
-oblique bandage
-oboe da caccia
-oboe d'amore
-obstacle
-office
-office furniture
-oil lamp
-oil paint
-oil tanker
-olive drab
-omnidirectional antenna
-onion dome
-open-air market
-open-end wrench
-opener
-openside plane
-ophthalmoscope
-optical device
-optical disk
-optical instrument
-optical telescope
-organ pipe
-outbuilding
-outerwear
-outfit
-outrigger canoe
-outside mirror
-oven
-overgarment
-overhand knot
-overhang
-overhead projector
-overnighter
-overshoe
-oxford
-package
-packaging
-packing box
-paddle
-paddle steamer
-page printer
-paint
-pallium
-pan
-pan
-panic button
-panopticon
-panopticon
-pantechnicon
-pantry
-pants suit
-panzer
-paper chain
-paper fastener
-parabolic reflector
-parapet
-parasail
-parka
-parsonage
-particle detector
-partition
-passenger ship
-passenger train
-passenger van
-passive matrix display
-passkey
-patch
-patchouli
-patchwork
-patina
-patisserie
-pavis
-peavey
-pedal
-pedestal table
-pedestrian crossing
-pedicab
-peg
-pen
-penal institution
-pencil
-pendulum
-pendulum clock
-percolator
-percussion instrument
-perfumery
-peripheral
-periwig
-personal computer
-petticoat
-Phillips screw
-Phillips screwdriver
-phonograph record
-photographic equipment
-photographic paper
-photometer
-physical pendulum
-piano
-piccolo
-pick
-pick
-pickle barrel
-piece of cloth
-pile
-pillow lace
-pilothouse
-pin
-pincer
-pinstripe
-pipe
-pipet
-pipe wrench
-pistol
-pivot
-place of business
-place of worship
-planetarium
-planner
-plant
-planter
-plasterboard
-plastic laminate
-plastic wrap
-plastron
-plate
-platform
-platform
-platform rocker
-plating
-pleat
-plethysmograph
-plexor
-pliers
-plug
-plug
-pneumatic drill
-pocket
-pocket-handkerchief
-pocketknife
-pointed arch
-polyester
-polygraph
-pomade
-pontifical
-pool ball
-poorhouse
-porcelain
-porch
-portable computer
-portico
-post
-posthole digger
-pot
-potential divider
-potpourri
-pottery
-pouch
-poultice
-powder
-powder keg
-power brake
-power mower
-power saw
-power shovel
-power tool
-press
-press
-pressure dome
-pressure gauge
-pressure suit
-printed circuit
-printer
-prison camp
-prod
-prolonge knot
-prompter
-prong
-propeller
-propeller plane
-prosthesis
-protective covering
-protective garment
-pruning saw
-pruning shears
-public house
-public toilet
-public transport
-pull
-pull chain
-pulley
-Pullman
-pullover
-pulse counter
-pump
-pump
-pump house
-punch
-punch press
-purifier
-push broom
-push button
-pusher
-puzzle
-pyrometer
-pyx
-QWERTY keyboard
-racing boat
-rack
-rack
-radar
-radiogram
-radio interferometer
-radio link
-radiometer
-radio receiver
-radiotelegraph
-radiotelephone
-radio transmitter
-raft
-rail
-rail fence
-railing
-raincoat
-rake
-ramp
-rampart
-random-access memory
-rayon
-razor
-reaction-propulsion engine
-reactor
-reading lamp
-reading room
-read-only memory
-rearview mirror
-receiver
-receptacle
-reception room
-recess
-reconnaissance plane
-recorder
-recording
-record player
-recreation room
-recycling bin
-reed stop
-reef knot
-refectory table
-refinery
-reflecting telescope
-reflector
-reformatory
-refracting telescope
-refrigerator car
-refuge
-regalia
-regimentals
-regulator
-rein
-religious residence
-removable disk
-repair shop
-repeating firearm
-reproducer
-rescue equipment
-reservoir
-reset button
-residence
-resistor
-resonator
-respirator
-restraint
-retort
-rheostat
-rib
-ribbed vault
-riddle
-ride
-riding boot
-riding mower
-rifle ball
-rig
-rink
-river boat
-road
-roadway
-robe
-rocket
-rocket
-rod
-roller
-roller
-in-line skate
-roller blind
-roller coaster
-rolling hitch
-Rolodex
-Roman building
-roof
-roof
-room
-roost
-rope
-rose water
-rotary engine
-rotating mechanism
-rotating shaft
-rotisserie
-rotor
-round arch
-router plane
-row house
-royal mast
-rubber bullet
-rug
-rushlight
-sable
-sable coat
-sack
-sackbut
-sacking
-saddle
-safe
-safety belt
-safety curtain
-safety fuse
-safety match
-sail
-sailboat
-sailing vessel
-salver
-sandglass
-sash
-satellite
-satellite television
-saucepan
-savings bank
-saw
-sawhorse
-scale
-scarf
-school
-scientific instrument
-scissors
-scoop
-scratcher
-screen
-screen
-screen
-screw eye
-scrub plane
-scuffer
-sculpture
-sea boat
-sea chest
-seam
-seaplane
-seat
-seat
-second hand
-secretary
-security system
-seeker
-selector
-self-propelled vehicle
-semiautomatic firearm
-semiautomatic pistol
-semiconductor device
-serger
-serpent
-serving cart
-serving dish
-set
-setscrew
-setscrew
-sewing needle
-sextant
-shackle
-shade
-shaft
-shag rug
-shaker
-shaper
-shaping tool
-sharpener
-shaving cream
-shaving foam
-shawl
-shawm
-shears
-sheath
-shed
-sheepshank
-sheet bend
-shelf
-shell
-shell
-shell
-shellac
-shelter
-shelter
-shelter
-shield
-ship
-shipboard system
-shirt
-shirtfront
-shock absorber
-shoe
-shooting brake
-shop
-short pants
-shotgun
-shoulder holster
-shrine
-shutter
-shuttle
-sidewinder
-sieve
-sifter
-sights
-signaling device
-signboard
-silk
-simulator
-single bed
-single-breasted suit
-single-reed instrument
-sitz bath
-six-pack
-skate
-skein
-skeleton
-skewer
-skidder
-skid lid
-skiff
-ski pole
-skirt
-ski tow
-skullcap
-slack suit
-slat
-sled
-sleeper
-sleeping car
-sleeve
-sleeve
-slide projector
-slipknot
-slipper
-sloop
-slop pail
-slot machine
-small boat
-smart bomb
-smoker
-smooth plane
-snack bar
-snap-brim hat
-snare drum
-sniper rifle
-Sno-cat
-soapbox
-socle
-sofa
-sonograph
-sorter
-sound recording
-soup ladle
-source of illumination
-soutane
-spacecraft
-spade
-spar
-spatula
-spear
-spear
-spectacles
-spectrograph
-spectroscope
-speedometer
-spider
-spike
-spike
-spinet
-spinning machine
-spiral ratchet screwdriver
-spiral spring
-spit
-spokeshave
-sponge mop
-spoon
-sports equipment
-sports implement
-sportswear
-spot
-spring
-spring balance
-springboard
-sprit
-square
-square knot
-squash racket
-squawk box
-squeezer
-squinch
-stabilizer
-stabilizer
-stable gear
-stadium
-stall
-stamp mill
-stand
-standard cell
-staple
-starter
-state prison
-station
-statue
-stay
-steakhouse
-stealth aircraft
-stealth bomber
-stealth fighter
-steam bath
-steamboat
-steamer
-steam iron
-steam whistle
-steel mill
-steelyard
-steeple
-steering system
-step
-step-up transformer
-stereo
-stick
-stick
-still
-stilt
-Stinger
-stock
-stockcar
-stock car
-stocking
-stonework
-stool
-stopper knot
-storage battery
-storage space
-storeroom
-stove
-stove bolt
-Stradavarius
-straight chair
-strap
-strap
-stringed instrument
-strip
-strongbox
-stronghold
-strongroom
-structural member
-structure
-stylus
-submachine gun
-submersible
-submersible
-subwoofer
-suction pump
-suede cloth
-sunbonnet
-sunhat
-supermarket
-superstructure
-supply chamber
-support
-support
-support column
-supporting structure
-supporting tower
-surface lift
-surface-to-air missile
-surgeon's knot
-surgical instrument
-surgical knife
-surplice
-surveillance system
-surveying instrument
-surveyor's level
-swamp buggy
-sweater
-swimsuit
-sword
-synchrotron
-system
-tabi
-table
-table
-table knife
-tableware
-tabor
-tachometer
-tack
-tack hammer
-talaria
-tambour
-tambourine
-tampon
-tank
-tank car
-tannoy
-tape
-tape deck
-tape recorder
-target
-tavern
-tea chest
-teaching aid
-tea gown
-teashop
-teaspoon
-tea-strainer
-tea tray
-telecommunication system
-telephone
-telephone line
-telephone receiver
-telephone system
-telephone wire
-telescope
-television antenna
-television camera
-television equipment
-television monitor
-temple
-temple
-tender
-tennis racket
-tenor drum
-tenoroon
-tenpenny nail
-tent
-tenterhook
-terminal
-terminal
-test rocket
-tetraskelion
-textile machine
-textile mill
-theater
-theodolite
-thermometer
-thermostat
-three-piece suit
-three-way switch
-thumbscrew
-thumbtack
-tights
-tile
-timber
-timber hitch
-timbrel
-time-fuse
-timepiece
-timer
-time-switch
-tire chain
-tithe barn
-toecap
-toga
-toggle switch
-toilet
-toilet powder
-toiletry
-toilet water
-token
-tomograph
-toner
-tongs
-tool
-toolbox
-tooth
-toothbrush
-top
-top
-topgallant
-topmast
-topsail
-torpedo
-torpedo boat
-touch screen
-towel
-toweling
-tower
-toy box
-track
-tracked vehicle
-trailer
-trailer
-train
-trammel
-transdermal patch
-transformer
-transistor
-transmission
-transmitter
-transporter
-trap
-trapeze
-travel iron
-treasure chest
-trellis
-trench
-trial balloon
-triclinium
-troop carrier
-trough
-trouser
-trowel
-truck
-trunk
-try square
-tube
-tuck shop
-tun
-tunic
-turbine
-Turkish towel
-Turk's head
-turner
-turntable
-turtleneck
-tweed
-tweeter
-twenty-two
-two-piece
-typesetting machine
-typewriter
-ultraviolet lamp
-undercarriage
-undergarment
-underpants
-underwear
-uneven parallel bars
-uniform
-university
-uplift
-urn
-urn
-utensil
-vacuum flask
-valve
-van
-van
-varnish
-vehicle
-veranda
-vertical file
-vessel
-vessel
-vest
-vibrator
-vibrator
-videocassette
-video recording
-vigil light
-viol
-vise
-vivarium
-voltaic cell
-voltmeter
-wagon
-waist pack
-walking stick
-wall
-wall
-wall unit
-ward
-warehouse
-warship
-wash
-washer
-washtub
-watch
-watchtower
-water-base paint
-water butt
-water cart
-watercolor
-water-cooled reactor
-water gauge
-water ski
-waterwheel
-weapon
-weaponry
-weatherglass
-weathervane
-web
-wedge
-wedge
-weighbridge
-weight
-weir
-weld
-well
-whaler
-wheel
-wheelchair
-wheeled vehicle
-wheelwork
-whetstone
-whip
-whisk
-whispering gallery
-white goods
-whorehouse
-wicker basket
-widebody aircraft
-winch
-Winchester
-wind instrument
-window
-window
-window blind
-window envelope
-Windsor knot
-wine bucket
-wine cask
-wineglass
-wire
-wire
-wire matrix printer
-wiring
-woman's clothing
-wood
-woodenware
-woodscrew
-woodwind
-woofer
-workbasket
-workbench
-work-clothing
-worktable
-workwear
-wrapping
-wrench
-writing desk
-writing implement
-X-ray film
-X-ray machine
-yacht chair
-yard
-yard
-yardstick
-yoke
-zither
-zoot suit
-grain
-light
-colorlessness
-chromatic color
-black
-gray
-dark red
-orange
-yellow
-green
-blue
-purple
-reddish purple
-pink
-light brown
-reddish brown
-complexion
-skin
-epidermal cell
-columnar cell
-macule
-specimen
-milk
-embryonic cell
-leukocyte
-neutrophil
-astrocyte
-exoskeleton
-medium
-film
-press
-print media
-storage medium
-journalism
-photojournalism
-newspaper
-telecommunication
-telephone
-call
-long distance
-wireless
-broadcasting
-television
-reception
-chat room
-portal site
-wordbook
-album
-concept album
-magazine
-movie
-sign
-comestible
-course
-dainty
-dish
-fare
-diet
-dietary supplement
-liquid diet
-reducing diet
-vegetarianism
-ration
-field ration
-foodstuff
-starches
-concentrate
-meal
-roughage
-flour
-wheat flour
-nutriment
-commissariat
-canned food
-canned meat
-meal
-breakfast
-lunch
-dinner
-supper
-buffet
-picnic
-cookout
-bite
-entree
-side dish
-casserole
-chicken casserole
-appetizer
-cocktail
-hors d'oeuvre
-relish
-dip
-soup
-madrilene
-broth
-broth
-chowder
-clam chowder
-stew
-goulash
-fish stew
-fricassee
-ragout
-ready-mix
-powdered sugar
-granulated sugar
-brown sugar
-sweet
-confiture
-candy
-hard candy
-patty
-brittle
-chewing gum
-candied fruit
-candied citrus peel
-fudge
-gumdrop
-mint
-kiss
-lozenge
-taffy
-dessert
-dumpling
-frozen dessert
-mousse
-mousse
-whip
-pudding
-pudding
-tipsy cake
-ice
-chocolate ice cream
-Neapolitan ice cream
-peach ice cream
-strawberry ice cream
-tutti-frutti
-vanilla ice cream
-split
-pudding
-custard
-pastry
-turnover
-puff paste
-phyllo
-fish cake
-conserve
-jam
-jelly
-apple jelly
-marmalade
-gelatin
-gelatin dessert
-patty
-stuffing
-bread
-breadstick
-bun
-cracker
-dark bread
-flatbread
-loaf of bread
-quick bread
-rye bread
-toast
-white bread
-French bread
-cornbread
-johnnycake
-muffin
-scone
-onion roll
-sweet roll
-onion bagel
-biscuit
-baking-powder biscuit
-soft pretzel
-sandwich
-hamburger
-gruel
-edible fruit
-vegetable
-crudites
-legume
-greens
-solanaceous vegetable
-root vegetable
-potato
-baked potato
-sweet potato
-snack food
-corn chip
-tortilla chip
-cruciferous vegetable
-cabbage
-kale
-red cabbage
-savoy cabbage
-squash
-summer squash
-yellow squash
-winter squash
-turban squash
-gherkin
-sprout
-beet
-pepper
-sweet pepper
-hot pepper
-chili
-jalapeno
-onion
-Spanish onion
-salad green
-lettuce
-butterhead lettuce
-bean
-pea
-green pea
-common bean
-fresh bean
-green bean
-shell bean
-lima bean
-soy
-celery
-chicory
-coffee substitute
-chicory escarole
-corn
-hominy
-cress
-tomato
-cherry tomato
-salsify
-turnip
-edible nut
-apple
-eating apple
-Delicious
-McIntosh
-Pippin
-cooking apple
-berry
-currant
-citrus
-temple orange
-mandarin
-bitter orange
-sweet orange
-Jaffa orange
-navel orange
-Valencia orange
-lime
-almond
-plum
-dried fruit
-raisin
-passion fruit
-cocoa
-melon
-muskmelon
-winter melon
-cherry
-sweet cherry
-heart cherry
-sour cherry
-grape
-fox grape
-muscadine
-slipskin grape
-vinifera grape
-Tokay
-cherimoya
-soursop
-sweetsop
-ilama
-pond apple
-olive
-pear
-edible seed
-walnut
-feed
-fodder
-oil cake
-timothy
-grain
-barley
-wheat
-rice
-mash
-bird feed
-petfood
-salad
-tossed salad
-combination salad
-pasta salad
-fruit salad
-ingredient
-flavorer
-condiment
-herb
-spice
-cinnamon
-pepper
-garlic
-mustard
-sage
-savory
-curry
-paprika
-pickle
-sweet pickle
-vinegar
-sauce
-hot sauce
-dressing
-mayonnaise
-cheese sauce
-hot-fudge sauce
-white sauce
-spaghetti sauce
-boiled egg
-hard-boiled egg
-Easter egg
-omelet
-firm omelet
-souffle
-dairy product
-milk
-milk
-powdered milk
-cream
-butter
-clarified butter
-yogurt
-curd
-cheese
-cream cheese
-bleu
-cheddar
-Swiss cheese
-spread
-pate
-sweetening
-sugar
-syrup
-batter
-bread dough
-chicken and rice
-pasta
-Tetrazzini
-chili dog
-fondue
-fondue
-hash
-kabob
-seafood Newburg
-meatball
-pilaf
-sausage pizza
-pepperoni pizza
-cheese pizza
-anchovy pizza
-Sicilian pizza
-porridge
-fish loaf
-salmon loaf
-scallopine
-taco
-beef burrito
-quesadilla
-tostada
-beverage
-concoction
-mix
-filling
-potion
-elixir
-alcohol
-brew
-beer
-lager
-Weissbier
-malt
-ale
-stout
-mead
-wine
-white wine
-sparkling wine
-Burgundy
-Beaujolais
-Medoc
-Pinot noir
-Bordeaux
-claret
-Chianti
-Cabernet
-Merlot
-dessert wine
-Rhine wine
-Rioja
-Saint Emilion
-zinfandel
-table wine
-vermouth
-fortified wine
-Madeira
-liquor
-brandy
-gin
-rum
-whiskey
-corn whiskey
-Irish
-Scotch
-liqueur
-coffee liqueur
-orange liqueur
-mixed drink
-cocktail
-highball
-Bloody Mary
-daiquiri
-manhattan
-martini
-sling
-sour
-caffe latte
-cider
-sweet cider
-juice
-fruit juice
-grape juice
-orange juice
-fruit drink
-mulled wine
-soft drink
-cola
-coffee
-punch
-champagne cup
-claret cup
-rickey
-tea
-tea
-herb tea
-tisane
-black tea
-green tea
-water
-drinking water
-mineral water
-vitamin pill
-collection
-suburb
-residence
-littoral
-grassland
-pasture
-resort
-field
-air bubble
-arroyo
-ascent
-atoll
-bank
-bank
-bar
-barrier reef
-basin
-beach
-burrow
-canyon
-cave
-continental glacier
-crag
-crater
-dale
-descent
-draw
-dune
-geological formation
-glacier
-glen
-gorge
-gulch
-gully
-highland
-hill
-hillside
-hole
-hollow
-iceberg
-ice mass
-ion
-knoll
-landfall
-landfill
-lather
-ledge
-lowland
-meteorite
-mountain
-mull
-natural depression
-natural elevation
-nullah
-ocean floor
-outcrop
-plain
-point
-precipice
-ravine
-reef
-ridge
-ridge
-rift valley
-rock
-sandbank
-seaside
-shiner
-shore
-slope
-soapsuds
-spume
-tableland
-tideland
-volcanic crater
-wadi
-spiritual leader
-adventurer
-anomaly
-benefactor
-commoner
-contestant
-discussant
-entertainer
-female
-finisher
-inhabitant
-native
-juvenile
-lover
-male
-mediator
-national
-peer
-recipient
-sensualist
-traveler
-unwelcome person
-unskilled person
-worker
-wrongdoer
-Black
-White
-Semite
-white man
-Mongol
-Nahuatl
-Caddo
-Penutian
-Teton
-Taracahitian
-Slav
-Catholic
-Altaic
-Bornean
-Canadian
-Central American
-Britisher
-English person
-Englishwoman
-Ethiopian
-Parisian
-Greek
-Italian
-Japanese
-Mexican
-Nigerian
-North American
-Pakistani
-South American Indian
-Filipino
-Polynesian
-Scandinavian
-South African
-South American
-Turki
-American
-New Yorker
-abbess
-abstainer
-academic administrator
-accomplice
-acquaintance
-acquirer
-aerialist
-actor
-actor
-addict
-adjutant
-admirer
-adulterer
-advertiser
-advocate
-analyst
-ancestor
-announcer
-announcer
-appointee
-appreciator
-appropriator
-archbishop
-architect
-army engineer
-army officer
-arrival
-articulator
-asserter
-assistant
-associate
-astronaut
-athlete
-attendant
-aunt
-authoritarian
-authority
-aviator
-back
-bad person
-ballet dancer
-bullfighter
-baron
-bartender
-baseball coach
-base runner
-basketball player
-believer
-betrothed
-bigot
-big shot
-biochemist
-bisexual
-boatman
-bond servant
-botanist
-Boy Scout
-buddy
-campaigner
-captain
-card player
-careerist
-caretaker
-cavalryman
-celebrity
-charmer
-child
-child
-cipher
-citizen
-civil rights leader
-cleaner
-clergyman
-cleric
-clerk
-climber
-closer
-clown
-coach
-cobbler
-collaborator
-college student
-collegian
-commanding officer
-commissioned officer
-commissioned military officer
-commissioner
-committee member
-communist
-compulsive
-computer scientist
-computer user
-contractor
-convict
-copycat
-counselor
-craftsman
-creditor
-critic
-curate
-dancer
-dancer
-darling
-date
-daughter
-dawdler
-deacon
-deaf person
-debtor
-deliveryman
-descender
-designated hitter
-detective
-detractor
-director
-disbeliever
-dispatcher
-distributor
-doctor
-domestic partner
-draftsman
-drinker
-drinker
-drug addict
-drug user
-drummer
-drunkard
-eager beaver
-earner
-eavesdropper
-economist
-editor
-egotist
-elder
-elected official
-emissary
-employee
-employer
-endomorph
-enemy
-entrant
-examiner
-exhibitionist
-fan
-fancier
-farmer
-farmhand
-fascist
-father
-female aristocrat
-female offspring
-female child
-fielder
-fireman
-first baseman
-first sergeant
-flag officer
-flatterer
-foe
-folk dancer
-follower
-football player
-forefather
-forger
-founder
-free agent
-friar
-monk
-gambler
-generator
-geneticist
-genitor
-geologist
-girl
-godchild
-godparent
-golfer
-grandma
-grandmaster
-grandparent
-granter
-great grandchild
-great grandparent
-grouch
-guard
-guest
-guide
-gymnast
-Gypsy
-hack
-hairdresser
-hater
-headmaster
-hearer
-hedonist
-heir
-herder
-homeless
-horseman
-host
-host
-hypocrite
-important person
-incumbent
-infielder
-informer
-in-law
-insurgent
-investigator
-investor
-journalist
-judge
-juror
-Counsel to the Crown
-kinswoman
-laborer
-lama
-landowner
-lawgiver
-lawman
-lawyer
-liberator
-lieutenant
-lineman
-literate
-litigant
-Lord
-failure
-lowerclassman
-lumberman
-maid
-maker
-malcontent
-martinet
-master of ceremonies
-masturbator
-medical officer
-medical practitioner
-medical scientist
-mender
-meteorologist
-middle-aged man
-miler
-military attache
-military officer
-military policeman
-minister
-minor leaguer
-misfit
-mixed-blood
-model
-moneymaker
-mother
-mourner
-mover
-musician
-Muslimah
-mystic
-nanny
-neonate
-nephew
-neutral
-newcomer
-newcomer
-newspaper editor
-niece
-noncommissioned officer
-nurse
-observer
-occultist
-oldster
-old woman
-opportunist
-orator
-originator
-outfielder
-right fielder
-right-handed pitcher
-painter
-panelist
-pardoner
-parodist
-party
-passenger
-patient
-patron
-payer
-peddler
-percussionist
-personal representative
-personification
-pervert
-petitioner
-Pharaoh
-phonetician
-physical therapist
-physicist
-pimp
-pisser
-pitcher
-planner
-player
-poet
-politician
-practitioner
-prayer
-preserver
-president
-priest
-princess
-principal
-proctor
-programmer
-promiser
-propagandist
-prosecutor
-psychic
-pusher
-queen
-queen
-ranch hand
-reader
-recruit
-recruiter
-religious leader
-repairman
-reporter
-representative
-reprobate
-rescuer
-reservist
-restrainer
-retailer
-retiree
-revolutionist
-rich person
-civil authority
-runner
-running back
-rustic
-saboteur
-sailor
-salesman
-salesperson
-scalper
-schemer
-scholar
-schoolchild
-scientist
-second baseman
-secretary
-seeker
-selfish person
-seller
-serf
-serviceman
-settler
-shrew
-sibling
-sick person
-singer
-sister
-skeptic
-skier
-sleeper
-slob
-smith
-snoop
-social climber
-socialist
-social scientist
-sociologist
-soldier
-son
-songster
-sorcerer
-sovereign
-speaker
-specialist
-spectator
-stand-in
-star
-stepparent
-stock trader
-stranger
-strategist
-student
-subordinate
-suitor
-superior
-surgeon
-sweetheart
-sympathizer
-tax assessor
-taxonomist
-teacher
-television reporter
-tenant
-tenant
-tennis player
-testator
-testee
-theologian
-therapist
-thinker
-thrower
-toastmaster
-trader
-traffic cop
-trainer
-traitor
-traveling salesman
-tyrant
-upstart
-upstart
-utility man
-vacationer
-vegetarian
-vice president
-victim
-volunteer
-votary
-waiter
-waitress
-wanderer
-wanton
-washer
-white supremacist
-wife
-winner
-winner
-woman
-workman
-worshiper
-wright
-writer
-wilding
-bryophyte
-liverwort
-pteridophyte
-fern
-fern ally
-spore
-spermatophyte
-perennial
-gymnosperm
-ephedra
-cycad
-sago palm
-zamia
-pine
-pinon
-nut pine
-white pine
-yellow pine
-larch
-fir
-silver fir
-cedar
-spruce
-hemlock
-douglas fir
-cedar
-cypress
-arborvitae
-araucaria
-kauri pine
-celery pine
-yellowwood
-gymnospermous yellowwood
-yew
-angiosperm
-dicot
-flower
-wildflower
-inflorescence
-pistil
-pericarp
-oilseed
-custard apple
-barberry
-allspice
-laurel
-anise tree
-magnolia
-moonseed
-buttercup
-aconite
-baneberry
-anemone
-thimbleweed
-columbine
-clematis
-delphinium
-nigella
-wax myrtle
-zebrawood
-legume
-legume
-darling pea
-clover
-acacia
-wattle
-albizzia
-nitta tree
-dogbane
-allamanda
-carissa
-frangipani
-rauwolfia
-arum
-alocasia
-anthurium
-caladium
-monstera
-nephthytis
-arrow arum
-calla lily
-duckweed
-watermeal
-birthwort
-sandwort
-mouse-ear chickweed
-pink
-china pink
-lychnis
-silene
-chickweed
-fig marigold
-amaranth
-orach
-saltbush
-beet
-sand verbena
-four o'clock
-echinocactus
-prickly pear
-pokeweed
-portulaca
-flame flower
-caper
-spiderflower
-crucifer
-cress
-watercress
-rock cress
-cabbage
-head cabbage
-turnip plant
-mustard
-wallflower
-woad
-stock
-radish plant
-pennycress
-poppy
-prickly poppy
-composite
-compass plant
-everlasting
-achillea
-ageratum
-ragweed
-ammobium
-burdock
-artemisia
-mugwort
-aster
-wood aster
-common daisy
-bur marigold
-calendula
-thistle
-carline thistle
-catananche
-centaury
-knapweed
-chrysanthemum
-golden aster
-goldenbush
-plume thistle
-woolly thistle
-coreopsis
-fleabane
-woolly sunflower
-cotton rose
-gazania
-African daisy
-cudweed
-gumweed
-goldenbush
-sneezeweed
-sunflower
-hawkweed
-marsh elder
-krigia
-hawkbit
-blazing star
-rattlesnake root
-daisybush
-coneflower
-coneflower
-cutleaved coneflower
-golden thistle
-white-topped aster
-goldenrod
-sow thistle
-marigold
-dandelion
-crownbeard
-zinnia
-achene
-campanula
-orchid
-orchis
-arethusa
-helleborine
-coral root
-lady's slipper
-large yellow lady's slipper
-helleborine
-fringed orchis
-rein orchid
-spider orchid
-moth orchid
-butterfly orchid
-ladies' tresses
-vanda
-vanilla
-yam
-primrose
-pimpernel
-featherfoil
-loosestrife
-water pimpernel
-gramineous plant
-grass
-wheatgrass
-foxtail
-broom grass
-oat
-brome
-grama
-reed grass
-burgrass
-crabgrass
-lyme grass
-wild rye
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-rye grass
-ricegrass
-meadowgrass
-millet
-reed
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-grain sorghum
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-mealie
-zoysia
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-cotton grass
-spike rush
-pandanus
-cattail
-grain
-kernel
-gourd
-gourd
-squash
-summer squash
-marrow
-winter squash
-turban squash
-bryony
-sweet melon
-luffa
-lobelia
-mallow
-hollyhock
-althea
-poppy mallow
-seashore mallow
-globe mallow
-tulipwood tree
-sterculia
-bottle-tree
-screw tree
-cacao
-linden
-herb
-protea
-banksia
-grevillea
-macadamia
-casuarina
-beefwood
-heath
-bearberry
-huckleberry
-kalmia
-rhododendron
-cranberry
-blueberry
-shortia
-Australian heath
-epacris
-wintergreen
-pipsissewa
-beech
-chestnut
-tanbark oak
-southern beech
-New Zealand beech
-oak
-live oak
-white oak
-red oak
-scrub oak
-chestnut oak
-birch
-alder
-hornbeam
-hop hornbeam
-hazelnut
-centaury
-gentian
-fringed gentian
-olive tree
-fringe tree
-ash
-red ash
-jasmine
-privet
-lilac
-liquidambar
-walnut
-hickory
-wing nut
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-enchanter's nightshade
-willowherb
-fuchsia
-evening primrose
-daphne
-canna
-banana
-ginger
-begonia
-tuberous begonia
-poon
-St John's wort
-rockrose
-dipterocarp
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-reseda
-viola
-violet
-nettle
-cannabis
-mulberry
-fig tree
-fig
-elm
-hackberry
-iridaceous plant
-bearded iris
-beardless iris
-crocus
-amaryllis
-blood lily
-narcissus
-daffodil
-liliaceous plant
-colicroot
-alliaceous plant
-kniphofia
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-asphodel
-mariposa
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-camas
-dogtooth violet
-fritillary
-tulip
-star-of-Bethlehem
-grape hyacinth
-scilla
-false asphodel
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-hellebore
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-bellwort
-agave
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-cassia
-locust tree
-senna
-angelim
-milk vetch
-wild indigo
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-rosewood
-blackwood
-tick trefoil
-coral tree
-vetchling
-wild pea
-lupine
-medic
-mucuna
-locoweed
-pole bean
-pea
-edible-pod pea
-quira
-hoary pea
-bush pea
-vetch
-palm
-sago palm
-feather palm
-fan palm
-palmetto
-areca
-calamus
-oil palm
-raffia palm
-lady palm
-eriogonum
-rhubarb
-water plantain
-waterweed
-pondweed
-rose
-agrimonia
-flowering quince
-cotoneaster
-avens
-apple tree
-wild apple
-crab apple
-Iowa crab
-cinquefoil
-plum
-wild plum
-bullace
-apricot
-cherry
-wild cherry
-sweet cherry
-sour cherry
-almond tree
-almond
-bird cherry
-flowering cherry
-chokecherry
-fruit tree
-bramble bush
-raspberry
-mountain ash
-service tree
-spirea
-madderwort
-coffee
-cinchona
-bedstraw
-genipa
-hamelia
-honeysuckle
-American fly honeysuckle
-teasel
-scabious
-geranium
-cranesbill
-storksbill
-incense tree
-mahogany
-silver ash
-milkwort
-citrus
-orange
-mandarin
-lemon
-kumquat
-prickly ash
-bitterwood tree
-ailanthus
-nasturtium
-willow
-osier
-sallow
-poplar
-black poplar
-cottonwood
-aspen
-soapberry
-soapberry vine
-harpullia
-pachysandra
-spindle tree
-maple
-box elder
-holly
-sumac
-horse chestnut
-persimmon
-buckthorn
-styrax
-carnivorous plant
-pitcher plant
-sedum
-philadelphus
-saxifrage
-astilbe
-alumroot
-miterwort
-parnassia
-currant
-plane tree
-phlox
-acanthus
-catalpa
-anchusa
-comfrey
-convolvulus
-bindweed
-gloxinia
-streptocarpus
-waterleaf
-nemophila
-scorpionweed
-giant hyssop
-bugle
-wood mint
-calamint
-coleus
-dead nettle
-origanum
-horehound
-monarda
-savory
-germander
-thyme
-blue curls
-snapdragon
-kitten-tails
-Indian paintbrush
-foxglove
-toadflax
-veronica
-nightshade
-thorn apple
-matrimony vine
-cupflower
-petunia
-salpiglossis
-spurge
-croton
-cassava
-slipper spurge
-camellia
-umbellifer
-angelica
-astrantia
-caraway
-fennel
-parsnip
-parsley
-sanicle
-dogwood
-valerian
-bristle fern
-flowering fern
-climbing fern
-clover fern
-adder's tongue
-grape fern
-ergot
-sclerotinia
-earthball
-Podaxaceae
-false truffle
-rhizopus
-slime mold
-cellular slime mold
-downy mildew
-pythium
-Sarcosomataceae
-club fungus
-lichen
-lecanora
-fungus
-basidiomycete
-mushroom
-mushroom
-mushroom
-toadstool
-horse mushroom
-meadow mushroom
-royal agaric
-false deathcap
-fly agaric
-death cap
-blushing mushroom
-destroying angel
-chanterelle
-floccose chanterelle
-pig's ears
-cinnabar chanterelle
-jack-o-lantern fungus
-inky cap
-shaggymane
-milkcap
-fairy-ring mushroom
-oyster mushroom
-olive-tree agaric
-Pholiota astragalina
-Pholiota aurea
-Pholiota destruens
-Pholiota flammans
-Pholiota flavida
-nameko
-Pholiota squarrosa-adiposa
-Pholiota squarrosa
-Pholiota squarrosoides
-Stropharia ambigua
-Stropharia hornemannii
-Stropharia rugoso-annulata
-Entoloma lividum
-Entoloma aprile
-Chlorophyllum molybdites
-lepiota
-parasol mushroom
-poisonous parasol
-Lepiota naucina
-Lepiota rhacodes
-American parasol
-Lepiota rubrotincta
-Lepiota clypeolaria
-onion stem
-blewits
-sandy mushroom
-Tricholoma pessundatum
-Tricholoma sejunctum
-man-on-a-horse
-Tricholoma venenata
-Tricholoma pardinum
-Tricholoma vaccinum
-Tricholoma aurantium
-Pluteus aurantiorugosus
-Pluteus magnus
-deer mushroom
-straw mushroom
-Volvariella bombycina
-Clitocybe clavipes
-Clitocybe dealbata
-Clitocybe inornata
-Clitocybe robusta
-Clitocybe irina
-Clitocybe subconnexa
-winter mushroom
-mycelium
-ascomycete
-Clavicipitaceae
-yeast
-discomycete
-morel
-Verpa
-false morel
-lorchel
-helvella
-Gyromitra californica
-Gyromitra sphaerospora
-Gyromitra esculenta
-Gyromitra infula
-Gyromitra gigas
-gasteromycete
-common stinkhorn
-Phallus ravenelii
-dog stinkhorn
-stinky squid
-puffball
-Geastrum coronatum
-Astreus pteridis
-Astreus hygrometricus
-polypore
-Boletus chrysenteron
-Boletus edulis
-Frost's bolete
-Boletus luridus
-Boletus mirabilis
-Boletus pallidus
-Boletus pulcherrimus
-Boletus pulverulentus
-Boletus roxanae
-Boletus subvelutipes
-Boletus variipes
-Boletus zelleri
-Fuscoboletinus paluster
-Fuscoboletinus serotinus
-Leccinum fibrillosum
-Suillus albivelatus
-old-man-of-the-woods
-Boletellus russellii
-jelly fungus
-rust
-smut
-cornsmut
-flag smut fungus
-waxycap
-Hygrocybe acutoconica
-Hygrophorus borealis
-Hygrophorus caeruleus
-Hygrophorus inocybiformis
-Hygrophorus kauffmanii
-Hygrophorus marzuolus
-Hygrophorus purpurascens
-Hygrophorus russula
-Hygrophorus sordidus
-Hygrophorus tennesseensis
-Hygrophorus turundus
-Neohygrophorus angelesianus
-Cortinarius armillatus
-Cortinarius atkinsonianus
-Cortinarius corrugatus
-Cortinarius gentilis
-Cortinarius mutabilis
-Cortinarius semisanguineus
-Cortinarius subfoetidus
-Cortinarius violaceus
-Gymnopilus spectabilis
-Gymnopilus validipes
-Gymnopilus ventricosus
-mold
-mildew
-candida
-houseplant
-succulent
-weed
-sporophyll
-sporangium
-poisonous plant
-vine
-tree
-bean tree
-gymnospermous tree
-conifer
-angiospermous tree
-nut tree
-spice tree
-bonsai
-subshrub
-bramble
-liana
-desert plant
-marsh plant
-strangler
-root
-receptacle
-scape
-peduncle
-flower cluster
-raceme
-cyme
-bulbous plant
-fruit
-seed
-bean
-nut
-berry
-aggregate fruit
-drupe
-drupelet
-pome
-pod
-husk
-buckthorn
-vinifera
-true pepper
-peperomia
-bract
-palmate leaf
-pinnate leaf
-dentate leaf
-branchlet
-polypody
-strap fern
-staghorn fern
-spleenwort
-chain fern
-davallia
-hare's-foot fern
-shield fern
-wood fern
-lady fern
-bladder fern
-holly fern
-woodsia
-maidenhair
-brittle maidenhair
-lip fern
-cliff brake
-horsetail
-club moss
-spikemoss
-beech fern
-shoestring fungus
-Armillaria caligata
-Armillaria ponderosa
-Armillaria zelleri
-honey mushroom
-milkweed
-stapelia
-stephanotis
-orangery
-figure
-plane figure
-solid figure
-line
-convex shape
-concave shape
-cylinder
-round shape
-polygon
-concave polygon
-amorphous shape
-closed curve
-simple closed curve
-cone
-circle
-ring
-loop
-ellipse
-triangle
-spherical polygon
-angular distance
-groove
-bulge
-bow
-balance
-toroid
-boundary
-incisure
-notch
-wrinkle
-tree
-regular polyhedron
-carbon
-rock
-soil
-high explosive
-culture medium
-agar
-paper
-paving
-plaster
-stucco
-tear gas
-vitamin
-fat-soluble vitamin
-water-soluble vitamin
-vitamin A
-B-complex vitamin
-vitamin E
-vitamin K
diff --git a/build/darknet/x64/data/inet9k.map b/build/darknet/x64/data/inet9k.map
deleted file mode 100644
index c91b3c15acc..00000000000
--- a/build/darknet/x64/data/inet9k.map
+++ /dev/null
@@ -1,200 +0,0 @@
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diff --git a/build/darknet/x64/data/labels/100_0.png b/build/darknet/x64/data/labels/100_0.png
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diff --git a/build/darknet/x64/data/labels/make_labels.py b/build/darknet/x64/data/labels/make_labels.py
deleted file mode 100644
index c8146f6db02..00000000000
--- a/build/darknet/x64/data/labels/make_labels.py
+++ /dev/null
@@ -1,23 +0,0 @@
-import os
-import string
-import pipes
-
-font = 'futura-normal'
-
-def make_labels(s):
-    l = string.printable
-    for word in l:
-        if word == ' ':
-            os.system('convert -fill black -background white -bordercolor white -font %s -pointsize %d label:"\ " 32_%d.png'%(font,s,s/12-1))
-        if word == '@':
-            os.system('convert -fill black -background white -bordercolor white -font %s -pointsize %d label:"\@" 64_%d.png'%(font,s,s/12-1))
-        elif word == '\\':
-            os.system('convert -fill black -background white -bordercolor white -font %s -pointsize %d label:"\\\\\\\\" 92_%d.png'%(font,s,s/12-1))
-        elif ord(word) in [9,10,11,12,13,14]:
-            pass
-        else:
-            os.system("convert -fill black -background white -bordercolor white -font %s -pointsize %d label:%s \"%d_%d.png\""%(font,s,pipes.quote(word), ord(word),s/12-1))
-
-for i in [12,24,36,48,60,72,84,96]:
-    make_labels(i)
-
diff --git a/build/darknet/x64/data/openimages.data b/build/darknet/x64/data/openimages.data
deleted file mode 100644
index fa80e5ab7d8..00000000000
--- a/build/darknet/x64/data/openimages.data
+++ /dev/null
@@ -1,8 +0,0 @@
-classes= 601
-train  = /home/pjreddie/data/openimsv4/openimages.train.list
-#valid  = coco_testdev
-valid = data/coco_val_5k.list
-names = data/openimages.names
-backup = /home/pjreddie/backup/
-eval=coco
-
diff --git a/build/darknet/x64/data/openimages.names b/build/darknet/x64/data/openimages.names
deleted file mode 100644
index ddfd8f22649..00000000000
--- a/build/darknet/x64/data/openimages.names
+++ /dev/null
@@ -1,601 +0,0 @@
-Tortoise
-Container
-Magpie
-Sea turtle
-Football
-Ambulance
-Ladder
-Toothbrush
-Syringe
-Sink
-Toy
-Organ
-Cassette deck
-Apple
-Human eye
-Cosmetics
-Paddle
-Snowman
-Beer
-Chopsticks
-Human beard
-Bird
-Parking meter
-Traffic light
-Croissant
-Cucumber
-Radish
-Towel
-Doll
-Skull
-Washing machine
-Glove
-Tick
-Belt
-Sunglasses
-Banjo
-Cart
-Ball
-Backpack
-Bicycle
-Home appliance
-Centipede
-Boat
-Surfboard
-Boot
-Headphones
-Hot dog
-Shorts
-Fast food
-Bus
-Boy
-Screwdriver
-Bicycle wheel
-Barge
-Laptop
-Miniskirt
-Drill
-Dress
-Bear
-Waffle
-Pancake
-Brown bear
-Woodpecker
-Blue jay
-Pretzel
-Bagel
-Tower
-Teapot
-Person
-Bow and arrow
-Swimwear
-Beehive
-Brassiere
-Bee
-Bat
-Starfish
-Popcorn
-Burrito
-Chainsaw
-Balloon
-Wrench
-Tent
-Vehicle registration plate
-Lantern
-Toaster
-Flashlight
-Billboard
-Tiara
-Limousine
-Necklace
-Carnivore
-Scissors
-Stairs
-Computer keyboard
-Printer
-Traffic sign
-Chair
-Shirt
-Poster
-Cheese
-Sock
-Fire hydrant
-Land vehicle
-Earrings
-Tie
-Watercraft
-Cabinetry
-Suitcase
-Muffin
-Bidet
-Snack
-Snowmobile
-Clock
-Medical equipment
-Cattle
-Cello
-Jet ski
-Camel
-Coat
-Suit
-Desk
-Cat
-Bronze sculpture
-Juice
-Gondola
-Beetle
-Cannon
-Computer mouse
-Cookie
-Office building
-Fountain
-Coin
-Calculator
-Cocktail
-Computer monitor
-Box
-Stapler
-Christmas tree
-Cowboy hat
-Hiking equipment
-Studio couch
-Drum
-Dessert
-Wine rack
-Drink
-Zucchini
-Ladle
-Human mouth
-Dairy
-Dice
-Oven
-Dinosaur
-Ratchet
-Couch
-Cricket ball
-Winter melon
-Spatula
-Whiteboard
-Pencil sharpener
-Door
-Hat
-Shower
-Eraser
-Fedora
-Guacamole
-Dagger
-Scarf
-Dolphin
-Sombrero
-Tin can
-Mug
-Tap
-Harbor seal
-Stretcher
-Can opener
-Goggles
-Human body
-Roller skates
-Coffee cup
-Cutting board
-Blender
-Plumbing fixture
-Stop sign
-Office supplies
-Volleyball
-Vase
-Slow cooker
-Wardrobe
-Coffee
-Whisk
-Paper towel
-Personal care
-Food
-Sun hat
-Tree house
-Flying disc
-Skirt
-Gas stove
-Salt and pepper shakers
-Mechanical fan
-Face powder
-Fax
-Fruit
-French fries
-Nightstand
-Barrel
-Kite
-Tart
-Treadmill
-Fox
-Flag
-Horn
-Window blind
-Human foot
-Golf cart
-Jacket
-Egg
-Street light
-Guitar
-Pillow
-Human leg
-Isopod
-Grape
-Human ear
-Power plugs and sockets
-Panda
-Giraffe
-Woman
-Door handle
-Rhinoceros
-Bathtub
-Goldfish
-Houseplant
-Goat
-Baseball bat
-Baseball glove
-Mixing bowl
-Marine invertebrates
-Kitchen utensil
-Light switch
-House
-Horse
-Stationary bicycle
-Hammer
-Ceiling fan
-Sofa bed
-Adhesive tape
-Harp
-Sandal
-Bicycle helmet
-Saucer
-Harpsichord
-Human hair
-Heater
-Harmonica
-Hamster
-Curtain
-Bed
-Kettle
-Fireplace
-Scale
-Drinking straw
-Insect
-Hair dryer
-Kitchenware
-Indoor rower
-Invertebrate
-Food processor
-Bookcase
-Refrigerator
-Wood-burning stove
-Punching bag
-Common fig
-Cocktail shaker
-Jaguar
-Golf ball
-Fashion accessory
-Alarm clock
-Filing cabinet
-Artichoke
-Table
-Tableware
-Kangaroo
-Koala
-Knife
-Bottle
-Bottle opener
-Lynx
-Lavender
-Lighthouse
-Dumbbell
-Human head
-Bowl
-Humidifier
-Porch
-Lizard
-Billiard table
-Mammal
-Mouse
-Motorcycle
-Musical instrument
-Swim cap
-Frying pan
-Snowplow
-Bathroom cabinet
-Missile
-Bust
-Man
-Waffle iron
-Milk
-Ring binder
-Plate
-Mobile phone
-Baked goods
-Mushroom
-Crutch
-Pitcher
-Mirror
-Lifejacket
-Table tennis racket
-Pencil case
-Musical keyboard
-Scoreboard
-Briefcase
-Kitchen knife
-Nail
-Tennis ball
-Plastic bag
-Oboe
-Chest of drawers
-Ostrich
-Piano
-Girl
-Plant
-Potato
-Hair spray
-Sports equipment
-Pasta
-Penguin
-Pumpkin
-Pear
-Infant bed
-Polar bear
-Mixer
-Cupboard
-Jacuzzi
-Pizza
-Digital clock
-Pig
-Reptile
-Rifle
-Lipstick
-Skateboard
-Raven
-High heels
-Red panda
-Rose
-Rabbit
-Sculpture
-Saxophone
-Shotgun
-Seafood
-Submarine sandwich
-Snowboard
-Sword
-Picture frame
-Sushi
-Loveseat
-Ski
-Squirrel
-Tripod
-Stethoscope
-Submarine
-Scorpion
-Segway
-Training bench
-Snake
-Coffee table
-Skyscraper
-Sheep
-Television
-Trombone
-Tea
-Tank
-Taco
-Telephone
-Torch
-Tiger
-Strawberry
-Trumpet
-Tree
-Tomato
-Train
-Tool
-Picnic basket
-Cooking spray
-Trousers
-Bowling equipment
-Football helmet
-Truck
-Measuring cup
-Coffeemaker
-Violin
-Vehicle
-Handbag
-Paper cutter
-Wine
-Weapon
-Wheel
-Worm
-Wok
-Whale
-Zebra
-Auto part
-Jug
-Pizza cutter
-Cream
-Monkey
-Lion
-Bread
-Platter
-Chicken
-Eagle
-Helicopter
-Owl
-Duck
-Turtle
-Hippopotamus
-Crocodile
-Toilet
-Toilet paper
-Squid
-Clothing
-Footwear
-Lemon
-Spider
-Deer
-Frog
-Banana
-Rocket
-Wine glass
-Countertop
-Tablet computer
-Waste container
-Swimming pool
-Dog
-Book
-Elephant
-Shark
-Candle
-Leopard
-Axe
-Hand dryer
-Soap dispenser
-Porcupine
-Flower
-Canary
-Cheetah
-Palm tree
-Hamburger
-Maple
-Building
-Fish
-Lobster
-Asparagus
-Furniture
-Hedgehog
-Airplane
-Spoon
-Otter
-Bull
-Oyster
-Horizontal bar
-Convenience store
-Bomb
-Bench
-Ice cream
-Caterpillar
-Butterfly
-Parachute
-Orange
-Antelope
-Beaker
-Moths and butterflies
-Window
-Closet
-Castle
-Jellyfish
-Goose
-Mule
-Swan
-Peach
-Coconut
-Seat belt
-Raccoon
-Chisel
-Fork
-Lamp
-Camera
-Squash
-Racket
-Human face
-Human arm
-Vegetable
-Diaper
-Unicycle
-Falcon
-Chime
-Snail
-Shellfish
-Cabbage
-Carrot
-Mango
-Jeans
-Flowerpot
-Pineapple
-Drawer
-Stool
-Envelope
-Cake
-Dragonfly
-Sunflower
-Microwave oven
-Honeycomb
-Marine mammal
-Sea lion
-Ladybug
-Shelf
-Watch
-Candy
-Salad
-Parrot
-Handgun
-Sparrow
-Van
-Grinder
-Spice rack
-Light bulb
-Corded phone
-Sports uniform
-Tennis racket
-Wall clock
-Serving tray
-Kitchen & dining room table
-Dog bed
-Cake stand
-Cat furniture
-Bathroom accessory
-Facial tissue holder
-Pressure cooker
-Kitchen appliance
-Tire
-Ruler
-Luggage and bags
-Microphone
-Broccoli
-Umbrella
-Pastry
-Grapefruit
-Band-aid
-Animal
-Bell pepper
-Turkey
-Lily
-Pomegranate
-Doughnut
-Glasses
-Human nose
-Pen
-Ant
-Car
-Aircraft
-Human hand
-Skunk
-Teddy bear
-Watermelon
-Cantaloupe
-Dishwasher
-Flute
-Balance beam
-Sandwich
-Shrimp
-Sewing machine
-Binoculars
-Rays and skates
-Ipod
-Accordion
-Willow
-Crab
-Crown
-Seahorse
-Perfume
-Alpaca
-Taxi
-Canoe
-Remote control
-Wheelchair
-Rugby ball
-Armadillo
-Maracas
-Helmet
diff --git a/build/darknet/x64/data/person.jpg b/build/darknet/x64/data/person.jpg
deleted file mode 100644
index 61d377fff94..00000000000
Binary files a/build/darknet/x64/data/person.jpg and /dev/null differ
diff --git a/build/darknet/x64/data/scream.jpg b/build/darknet/x64/data/scream.jpg
deleted file mode 100644
index 43f2c36a8d4..00000000000
Binary files a/build/darknet/x64/data/scream.jpg and /dev/null differ
diff --git a/build/darknet/x64/data/voc.data b/build/darknet/x64/data/voc.data
deleted file mode 100644
index d6775870f65..00000000000
--- a/build/darknet/x64/data/voc.data
+++ /dev/null
@@ -1,7 +0,0 @@
-classes= 20
-train  = data/train_voc.txt
-valid  = data/2007_test.txt
-#difficult = data/difficult_2007_test.txt
-names = data/voc.names
-backup = backup/
-
diff --git a/build/darknet/x64/data/voc.names b/build/darknet/x64/data/voc.names
deleted file mode 100644
index 8420ab35ede..00000000000
--- a/build/darknet/x64/data/voc.names
+++ /dev/null
@@ -1,20 +0,0 @@
-aeroplane
-bicycle
-bird
-boat
-bottle
-bus
-car
-cat
-chair
-cow
-diningtable
-dog
-horse
-motorbike
-person
-pottedplant
-sheep
-sofa
-train
-tvmonitor
diff --git a/build/darknet/x64/data/voc/voc_label.py b/build/darknet/x64/data/voc/voc_label.py
deleted file mode 100644
index d1e88236f2c..00000000000
--- a/build/darknet/x64/data/voc/voc_label.py
+++ /dev/null
@@ -1,56 +0,0 @@
-import xml.etree.ElementTree as ET
-import pickle
-import os
-from os import listdir, getcwd
-from os.path import join
-
-sets=[('2012', 'train'), ('2012', 'val'), ('2007', 'train'), ('2007', 'val'), ('2007', 'test')]
-
-classes = ["aeroplane", "bicycle", "bird", "boat", "bottle", "bus", "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike", "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"]
-
-
-def convert(size, box):
-    dw = 1./size[0]
-    dh = 1./size[1]
-    x = (box[0] + box[1])/2.0
-    y = (box[2] + box[3])/2.0
-    w = box[1] - box[0]
-    h = box[3] - box[2]
-    x = x*dw
-    w = w*dw
-    y = y*dh
-    h = h*dh
-    return (x,y,w,h)
-
-def convert_annotation(year, image_id):
-    in_file = open('VOCdevkit/VOC%s/Annotations/%s.xml'%(year, image_id))
-    out_file = open('VOCdevkit/VOC%s/labels/%s.txt'%(year, image_id), 'w')
-    tree=ET.parse(in_file)
-    root = tree.getroot()
-    size = root.find('size')
-    w = int(size.find('width').text)
-    h = int(size.find('height').text)
-
-    for obj in root.iter('object'):
-        difficult = obj.find('difficult').text
-        cls = obj.find('name').text
-        if cls not in classes or int(difficult) == 1:
-            continue
-        cls_id = classes.index(cls)
-        xmlbox = obj.find('bndbox')
-        b = (float(xmlbox.find('xmin').text), float(xmlbox.find('xmax').text), float(xmlbox.find('ymin').text), float(xmlbox.find('ymax').text))
-        bb = convert((w,h), b)
-        out_file.write(str(cls_id) + " " + " ".join([str(a) for a in bb]) + '\n')
-
-wd = getcwd()
-
-for year, image_set in sets:
-    if not os.path.exists('VOCdevkit/VOC%s/labels/'%(year)):
-        os.makedirs('VOCdevkit/VOC%s/labels/'%(year))
-    image_ids = open('VOCdevkit/VOC%s/ImageSets/Main/%s.txt'%(year, image_set)).read().strip().split()
-    list_file = open('%s_%s.txt'%(year, image_set), 'w')
-    for image_id in image_ids:
-        list_file.write('%s/VOCdevkit/VOC%s/JPEGImages/%s.jpg\n'%(wd, year, image_id))
-        convert_annotation(year, image_id)
-    list_file.close()
-
diff --git a/build/darknet/x64/densenet201_yolo.cfg b/build/darknet/x64/densenet201_yolo.cfg
deleted file mode 100644
index 2c78ec5d2a4..00000000000
--- a/build/darknet/x64/densenet201_yolo.cfg
+++ /dev/null
@@ -1,1978 +0,0 @@
-[net]
-batch=64
-subdivisions=8
-height=416
-width=416
-channels=3
-momentum=0.9
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-learning_rate=0.0001
-max_batches = 45000
-policy=steps
-steps=100,25000,35000
-scales=10,.1,.1
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=7
-stride=2
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
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-size=1
-stride=1
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-activation=leaky
-
-[convolutional]
-batch_normalize=1
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-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
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-size=1
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-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
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-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
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-[convolutional]
-batch_normalize=1
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-
-[convolutional]
-batch_normalize=1
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-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
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-
-[convolutional]
-batch_normalize=1
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-[route]
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-[convolutional]
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-[convolutional]
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-[route]
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-
-[convolutional]
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-[convolutional]
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-[route]
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-[convolutional]
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-[convolutional]
-batch_normalize=1
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-[route]
-layers=-1,-3
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-[convolutional]
-batch_normalize=1
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-
-[convolutional]
-batch_normalize=1
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-[route]
-layers=-1,-3
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-[convolutional]
-batch_normalize=1
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-[convolutional]
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-[convolutional]
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-[route]
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-[convolutional]
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-[convolutional]
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-[route]
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-[convolutional]
-batch_normalize=1
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-[convolutional]
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-[route]
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-[convolutional]
-batch_normalize=1
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-[convolutional]
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-[route]
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-[convolutional]
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-[convolutional]
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-[route]
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-[convolutional]
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-[convolutional]
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-[route]
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-[convolutional]
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-[route]
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-[route]
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-[route]
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-[route]
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-[convolutional]
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-[route]
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-[route]
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-[convolutional]
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-[route]
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-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[route]
-layers=-1,-3
-#stopbackward=1
-
-
-[convolutional]
-batch_normalize=1
-size=1
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-
-
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=125
-activation=linear
-
-
-[region]
-anchors =  1.3221, 1.73145, 3.19275, 4.00944, 5.05587, 8.09892, 9.47112, 4.84053, 11.2364, 10.0071
-bias_match=1
-classes=20
-coords=4
-num=5
-softmax=1
-jitter=.3
-rescore=1
-
-object_scale=5
-noobject_scale=1
-class_scale=1
-coord_scale=1
-
-absolute=1
-thresh = .6
-random=0
-
diff --git a/build/darknet/x64/dog.jpg b/build/darknet/x64/dog.jpg
deleted file mode 100644
index 77b0381222e..00000000000
Binary files a/build/darknet/x64/dog.jpg and /dev/null differ
diff --git a/build/darknet/x64/dogr.jpg b/build/darknet/x64/dogr.jpg
deleted file mode 100644
index e9201231bdb..00000000000
Binary files a/build/darknet/x64/dogr.jpg and /dev/null differ
diff --git a/build/darknet/x64/gen_anchors.py b/build/darknet/x64/gen_anchors.py
deleted file mode 100644
index 709cb88adab..00000000000
--- a/build/darknet/x64/gen_anchors.py
+++ /dev/null
@@ -1,165 +0,0 @@
-'''
-Created on Feb 20, 2017
-
-@author: jumabek
-'''
-from os import listdir
-from os.path import isfile, join
-import argparse
-#import cv2
-import numpy as np
-import sys
-import os
-import shutil
-import random 
-import math
-
-width_in_cfg_file = 416.
-height_in_cfg_file = 416.
-
-def IOU(x,centroids):
-    similarities = []
-    k = len(centroids)
-    for centroid in centroids:
-        c_w,c_h = centroid
-        w,h = x
-        if c_w>=w and c_h>=h:
-            similarity = w*h/(c_w*c_h)
-        elif c_w>=w and c_h<=h:
-            similarity = w*c_h/(w*h + (c_w-w)*c_h)
-        elif c_w<=w and c_h>=h:
-            similarity = c_w*h/(w*h + c_w*(c_h-h))
-        else: #means both w,h are bigger than c_w and c_h respectively
-            similarity = (c_w*c_h)/(w*h)
-        similarities.append(similarity) # will become (k,) shape
-    return np.array(similarities) 
-
-def avg_IOU(X,centroids):
-    n,d = X.shape
-    sum = 0.
-    for i in range(X.shape[0]):
-        #note IOU() will return array which contains IoU for each centroid and X[i] // slightly ineffective, but I am too lazy
-        sum+= max(IOU(X[i],centroids)) 
-    return sum/n
-
-def write_anchors_to_file(centroids,X,anchor_file):
-    f = open(anchor_file,'w')
-    
-    anchors = centroids.copy()
-    print(anchors.shape)
-
-    for i in range(anchors.shape[0]):
-        anchors[i][0]*=width_in_cfg_file/32.
-        anchors[i][1]*=height_in_cfg_file/32.
-         
-
-    widths = anchors[:,0]
-    sorted_indices = np.argsort(widths)
-
-    print('Anchors = ', anchors[sorted_indices])
-        
-    for i in sorted_indices[:-1]:
-        f.write('%0.2f,%0.2f, '%(anchors[i,0],anchors[i,1]))
-
-    #there should not be comma after last anchor, that's why
-    f.write('%0.2f,%0.2f\n'%(anchors[sorted_indices[-1:],0],anchors[sorted_indices[-1:],1]))
-    
-    f.write('%f\n'%(avg_IOU(X,centroids)))
-    print()
-
-def kmeans(X,centroids,eps,anchor_file):
-    
-    N = X.shape[0]
-    iterations = 0
-    k,dim = centroids.shape
-    prev_assignments = np.ones(N)*(-1)    
-    iter = 0
-    old_D = np.zeros((N,k))
-
-    while True:
-        D = [] 
-        iter+=1           
-        for i in range(N):
-            d = 1 - IOU(X[i],centroids)
-            D.append(d)
-        D = np.array(D) # D.shape = (N,k)
-        
-        print("iter {}: dists = {}".format(iter,np.sum(np.abs(old_D-D))))
-            
-        #assign samples to centroids 
-        assignments = np.argmin(D,axis=1)
-        
-        if (assignments == prev_assignments).all() :
-            print("Centroids = ",centroids)
-            write_anchors_to_file(centroids,X,anchor_file)
-            return
-
-        #calculate new centroids
-        centroid_sums=np.zeros((k,dim),np.float)
-        for i in range(N):
-            centroid_sums[assignments[i]]+=X[i]        
-        for j in range(k):            
-            centroids[j] = centroid_sums[j]/(np.sum(assignments==j))
-        
-        prev_assignments = assignments.copy()     
-        old_D = D.copy()  
-
-def main(argv):
-    parser = argparse.ArgumentParser()
-    parser.add_argument('-filelist', default = '\\path\\to\\voc\\filelist\\train.txt', 
-                        help='path to filelist\n' )
-    parser.add_argument('-output_dir', default = 'generated_anchors/anchors', type = str, 
-                        help='Output anchor directory\n' )  
-    parser.add_argument('-num_clusters', default = 0, type = int, 
-                        help='number of clusters\n' )  
-
-   
-    args = parser.parse_args()
-    
-    if not os.path.exists(args.output_dir):
-        os.mkdir(args.output_dir)
-
-    f = open(args.filelist)
-  
-    lines = [line.rstrip('\n') for line in f.readlines()]
-    
-    annotation_dims = []
-
-    size = np.zeros((1,1,3))
-    for line in lines:
-                    
-        #line = line.replace('images','labels')
-        #line = line.replace('img1','labels')
-        line = line.replace('JPEGImages','labels')        
-        
-
-        line = line.replace('.jpg','.txt')
-        line = line.replace('.png','.txt')
-        print(line)
-        f2 = open(line)
-        for line in f2.readlines():
-            line = line.rstrip('\n')
-            w,h = line.split(' ')[3:]            
-            #print(w,h)
-            annotation_dims.append(tuple(map(float,(w,h))))
-    annotation_dims = np.array(annotation_dims)
-  
-    eps = 0.005
-    
-    if args.num_clusters == 0:
-        for num_clusters in range(1,11): #we make 1 through 10 clusters 
-            anchor_file = join( args.output_dir,'anchors%d.txt'%(num_clusters))
-
-            indices = [ random.randrange(annotation_dims.shape[0]) for i in range(num_clusters)]
-            centroids = annotation_dims[indices]
-            kmeans(annotation_dims,centroids,eps,anchor_file)
-            print('centroids.shape', centroids.shape)
-    else:
-        anchor_file = join( args.output_dir,'anchors%d.txt'%(args.num_clusters))
-        indices = [ random.randrange(annotation_dims.shape[0]) for i in range(args.num_clusters)]
-        centroids = annotation_dims[indices]
-        kmeans(annotation_dims,centroids,eps,anchor_file)
-        print('centroids.shape', centroids.shape)
-
-if __name__=="__main__":
-    main(sys.argv)
diff --git a/build/darknet/x64/pthreadGC2.dll b/build/darknet/x64/pthreadGC2.dll
deleted file mode 100644
index 841d4a21699..00000000000
Binary files a/build/darknet/x64/pthreadGC2.dll and /dev/null differ
diff --git a/build/darknet/x64/pthreadVC2.dll b/build/darknet/x64/pthreadVC2.dll
deleted file mode 100644
index 165b4d26ec4..00000000000
Binary files a/build/darknet/x64/pthreadVC2.dll and /dev/null differ
diff --git a/build/darknet/x64/resnet152_yolo.cfg b/build/darknet/x64/resnet152_yolo.cfg
deleted file mode 100644
index d766084d1ec..00000000000
--- a/build/darknet/x64/resnet152_yolo.cfg
+++ /dev/null
@@ -1,1473 +0,0 @@
-[net]
-batch=64
-subdivisions=32
-height=416
-width=416
-channels=3
-momentum=0.9
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-learning_rate=0.0001
-max_batches = 45000
-policy=steps
-steps=100,25000,35000
-scales=10,.1,.1
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=7
-stride=2
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-
-# Conv 4
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-#Conv 5
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-#stopbackward=1
-
-
-
-[convolutional]
-batch_normalize=1
-size=1
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=125
-activation=linear
-
-
-[region]
-anchors =  1.3221, 1.73145, 3.19275, 4.00944, 5.05587, 8.09892, 9.47112, 4.84053, 11.2364, 10.0071
-bias_match=1
-classes=20
-coords=4
-num=5
-softmax=1
-jitter=.3
-rescore=1
-#focal_loss=1
-
-object_scale=5
-noobject_scale=1
-class_scale=1
-coord_scale=1
-
-absolute=1
-thresh = .6
-random=1
diff --git a/build/darknet/x64/resnet50_yolo.cfg b/build/darknet/x64/resnet50_yolo.cfg
deleted file mode 100644
index 44dc68e90d7..00000000000
--- a/build/darknet/x64/resnet50_yolo.cfg
+++ /dev/null
@@ -1,520 +0,0 @@
-[net]
-batch=64
-subdivisions=32
-height=416
-width=416
-channels=3
-momentum=0.9
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-learning_rate=0.0001
-max_batches = 45000
-policy=steps
-steps=100,25000,35000
-scales=10,.1,.1
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=7
-stride=2
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-
-# Conv 4
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-#Conv 5
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=2048
-size=1
-stride=1
-pad=1
-activation=linear
-
-[shortcut]
-from=-4
-activation=leaky
-#stopbackward=1
-
-
-
-[convolutional]
-batch_normalize=1
-size=1
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=125
-activation=linear
-
-
-[region]
-anchors =  1.3221, 1.73145, 3.19275, 4.00944, 5.05587, 8.09892, 9.47112, 4.84053, 11.2364, 10.0071
-bias_match=1
-classes=20
-coords=4
-num=5
-softmax=1
-jitter=.3
-rescore=1
-
-object_scale=5
-noobject_scale=1
-class_scale=1
-coord_scale=1
-
-absolute=1
-thresh = .6
-random=1
\ No newline at end of file
diff --git a/build/darknet/x64/results/tmp.txt b/build/darknet/x64/results/tmp.txt
deleted file mode 100644
index e69de29bb2d..00000000000
diff --git a/build/darknet/x64/reval_voc_py3.py b/build/darknet/x64/reval_voc_py3.py
deleted file mode 100644
index 23f9ce3fd06..00000000000
--- a/build/darknet/x64/reval_voc_py3.py
+++ /dev/null
@@ -1,104 +0,0 @@
-#!/usr/bin/env python
-
-# Adapt from ->
-# --------------------------------------------------------
-# Fast R-CNN
-# Copyright (c) 2015 Microsoft
-# Licensed under The MIT License [see LICENSE for details]
-# Written by Ross Girshick
-# --------------------------------------------------------
-# <- Written by Yaping Sun
-
-"""Reval = re-eval. Re-evaluate saved detections."""
-
-import os, sys, argparse
-import numpy as np
-import _pickle as cPickle
-#import cPickle
-
-from voc_eval_py3 import voc_eval
-
-def parse_args():
-    """
-    Parse input arguments
-    """
-    parser = argparse.ArgumentParser(description='Re-evaluate results')
-    parser.add_argument('output_dir', nargs=1, help='results directory',
-                        type=str)
-    parser.add_argument('--voc_dir', dest='voc_dir', default='data/VOCdevkit', type=str)
-    parser.add_argument('--year', dest='year', default='2017', type=str)
-    parser.add_argument('--image_set', dest='image_set', default='test', type=str)
-
-    parser.add_argument('--classes', dest='class_file', default='data/voc.names', type=str)
-
-    if len(sys.argv) == 1:
-        parser.print_help()
-        sys.exit(1)
-
-    args = parser.parse_args()
-    return args
-
-def get_voc_results_file_template(image_set, out_dir = 'results'):
-    filename = 'comp4_det_' + image_set + '_{:s}.txt'
-    path = os.path.join(out_dir, filename)
-    return path
-
-def do_python_eval(devkit_path, year, image_set, classes, output_dir = 'results'):
-    annopath = os.path.join(
-        devkit_path,
-        'VOC' + year,
-        'Annotations',
-        '{}.xml')
-    imagesetfile = os.path.join(
-        devkit_path,
-        'VOC' + year,
-        'ImageSets',
-        'Main',
-        image_set + '.txt')
-    cachedir = os.path.join(devkit_path, 'annotations_cache')
-    aps = []
-    # The PASCAL VOC metric changed in 2010
-    use_07_metric = True if int(year) < 2010 else False
-    print('VOC07 metric? ' + ('Yes' if use_07_metric else 'No'))
-    print('devkit_path=',devkit_path,', year = ',year)
-
-    if not os.path.isdir(output_dir):
-        os.mkdir(output_dir)
-    for i, cls in enumerate(classes):
-        if cls == '__background__':
-            continue
-        filename = get_voc_results_file_template(image_set).format(cls)
-        rec, prec, ap = voc_eval(
-            filename, annopath, imagesetfile, cls, cachedir, ovthresh=0.5,
-            use_07_metric=use_07_metric)
-        aps += [ap]
-        print('AP for {} = {:.4f}'.format(cls, ap))
-        with open(os.path.join(output_dir, cls + '_pr.pkl'), 'wb') as f:
-            cPickle.dump({'rec': rec, 'prec': prec, 'ap': ap}, f)
-    print('Mean AP = {:.4f}'.format(np.mean(aps)))
-    print('~~~~~~~~')
-    print('Results:')
-    for ap in aps:
-        print('{:.3f}'.format(ap))
-    print('{:.3f}'.format(np.mean(aps)))
-    print('~~~~~~~~')
-    print('')
-    print('--------------------------------------------------------------')
-    print('Results computed with the **unofficial** Python eval code.')
-    print('Results should be very close to the official MATLAB eval code.')
-    print('-- Thanks, The Management')
-    print('--------------------------------------------------------------')
-
-
-
-if __name__ == '__main__':
-    args = parse_args()
-
-    output_dir = os.path.abspath(args.output_dir[0])
-    with open(args.class_file, 'r') as f:
-        lines = f.readlines()
-
-    classes = [t.strip('\n') for t in lines]
-
-    print('Evaluating detections')
-    do_python_eval(args.voc_dir, args.year, args.image_set, classes, output_dir)
diff --git a/build/darknet/x64/tiny-yolo-voc.cfg b/build/darknet/x64/tiny-yolo-voc.cfg
deleted file mode 100644
index ab2c066a216..00000000000
--- a/build/darknet/x64/tiny-yolo-voc.cfg
+++ /dev/null
@@ -1,134 +0,0 @@
-[net]
-batch=64
-subdivisions=8
-width=416
-height=416
-channels=3
-momentum=0.9
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-learning_rate=0.001
-max_batches = 40200
-policy=steps
-steps=-1,100,20000,30000
-scales=.1,10,.1,.1
-
-[convolutional]
-batch_normalize=1
-filters=16
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=1
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-###########
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=125
-activation=linear
-
-[region]
-anchors = 1.08,1.19,  3.42,4.41,  6.63,11.38,  9.42,5.11,  16.62,10.52
-bias_match=1
-classes=20
-coords=4
-num=5
-softmax=1
-jitter=.2
-rescore=1
-
-object_scale=5
-noobject_scale=1
-class_scale=1
-coord_scale=1
-
-absolute=1
-thresh = .6
-random=1
diff --git a/build/darknet/x64/tiny-yolo.cfg b/build/darknet/x64/tiny-yolo.cfg
deleted file mode 100644
index 5580098b45f..00000000000
--- a/build/darknet/x64/tiny-yolo.cfg
+++ /dev/null
@@ -1,134 +0,0 @@
-[net]
-batch=64
-subdivisions=8
-width=416
-height=416
-channels=3
-momentum=0.9
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-learning_rate=0.001
-max_batches = 120000
-policy=steps
-steps=-1,100,80000,100000
-scales=.1,10,.1,.1
-
-[convolutional]
-batch_normalize=1
-filters=16
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=1
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-###########
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=425
-activation=linear
-
-[region]
-anchors = 0.738768,0.874946,  2.42204,2.65704,  4.30971,7.04493,  10.246,4.59428,  12.6868,11.8741
-bias_match=1
-classes=80
-coords=4
-num=5
-softmax=1
-jitter=.2
-rescore=1
-
-object_scale=5
-noobject_scale=1
-class_scale=1
-coord_scale=1
-
-absolute=1
-thresh = .6
-random=1
diff --git a/build/darknet/x64/voc_eval_py3.py b/build/darknet/x64/voc_eval_py3.py
deleted file mode 100644
index 13d07a9a7e7..00000000000
--- a/build/darknet/x64/voc_eval_py3.py
+++ /dev/null
@@ -1,201 +0,0 @@
-# --------------------------------------------------------
-# Fast/er R-CNN
-# Licensed under The MIT License [see LICENSE for details]
-# Written by Bharath Hariharan
-# --------------------------------------------------------
-
-import xml.etree.ElementTree as ET
-import os
-#import cPickle
-import _pickle as cPickle
-import numpy as np
-
-def parse_rec(filename):
-    """ Parse a PASCAL VOC xml file """
-    tree = ET.parse(filename)
-    objects = []
-    for obj in tree.findall('object'):
-        obj_struct = {}
-        obj_struct['name'] = obj.find('name').text
-        #obj_struct['pose'] = obj.find('pose').text
-        #obj_struct['truncated'] = int(obj.find('truncated').text)
-        obj_struct['difficult'] = int(obj.find('difficult').text)
-        bbox = obj.find('bndbox')
-        obj_struct['bbox'] = [int(bbox.find('xmin').text),
-                              int(bbox.find('ymin').text),
-                              int(bbox.find('xmax').text),
-                              int(bbox.find('ymax').text)]
-        objects.append(obj_struct)
-
-    return objects
-
-def voc_ap(rec, prec, use_07_metric=False):
-    """ ap = voc_ap(rec, prec, [use_07_metric])
-    Compute VOC AP given precision and recall.
-    If use_07_metric is true, uses the
-    VOC 07 11 point method (default:False).
-    """
-    if use_07_metric:
-        # 11 point metric
-        ap = 0.
-        for t in np.arange(0., 1.1, 0.1):
-            if np.sum(rec >= t) == 0:
-                p = 0
-            else:
-                p = np.max(prec[rec >= t])
-            ap = ap + p / 11.
-    else:
-        # correct AP calculation
-        # first append sentinel values at the end
-        mrec = np.concatenate(([0.], rec, [1.]))
-        mpre = np.concatenate(([0.], prec, [0.]))
-
-        # compute the precision envelope
-        for i in range(mpre.size - 1, 0, -1):
-            mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])
-
-        # to calculate area under PR curve, look for points
-        # where X axis (recall) changes value
-        i = np.where(mrec[1:] != mrec[:-1])[0]
-
-        # and sum (\Delta recall) * prec
-        ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])
-    return ap
-
-def voc_eval(detpath,
-             annopath,
-             imagesetfile,
-             classname,
-             cachedir,
-             ovthresh=0.5,
-             use_07_metric=False):
-    """rec, prec, ap = voc_eval(detpath,
-                                annopath,
-                                imagesetfile,
-                                classname,
-                                [ovthresh],
-                                [use_07_metric])
-
-    Top level function that does the PASCAL VOC evaluation.
-
-    detpath: Path to detections
-        detpath.format(classname) should produce the detection results file.
-    annopath: Path to annotations
-        annopath.format(imagename) should be the xml annotations file.
-    imagesetfile: Text file containing the list of images, one image per line.
-    classname: Category name (duh)
-    cachedir: Directory for caching the annotations
-    [ovthresh]: Overlap threshold (default = 0.5)
-    [use_07_metric]: Whether to use VOC07's 11 point AP computation
-        (default False)
-    """
-    # assumes detections are in detpath.format(classname)
-    # assumes annotations are in annopath.format(imagename)
-    # assumes imagesetfile is a text file with each line an image name
-    # cachedir caches the annotations in a pickle file
-
-    # first load gt
-    if not os.path.isdir(cachedir):
-        os.mkdir(cachedir)
-    cachefile = os.path.join(cachedir, 'annots.pkl')
-    # read list of images
-    with open(imagesetfile, 'r') as f:
-        lines = f.readlines()
-    imagenames = [x.strip() for x in lines]
-
-    if not os.path.isfile(cachefile):
-        # load annots
-        recs = {}
-        for i, imagename in enumerate(imagenames):
-            recs[imagename] = parse_rec(annopath.format(imagename))
-            #if i % 100 == 0:
-                #print('Reading annotation for {:d}/{:d}').format(i + 1, len(imagenames))
-        # save
-        #print('Saving cached annotations to {:s}').format(cachefile)
-        with open(cachefile, 'wb') as f:
-            cPickle.dump(recs, f)
-    else:
-        # load
-        print('!!! cachefile = ',cachefile)
-        with open(cachefile, 'rb') as f:
-            recs = cPickle.load(f)
-
-    # extract gt objects for this class
-    class_recs = {}
-    npos = 0
-    for imagename in imagenames:
-        R = [obj for obj in recs[imagename] if obj['name'] == classname]
-        bbox = np.array([x['bbox'] for x in R])
-        difficult = np.array([x['difficult'] for x in R]).astype(np.bool)
-        det = [False] * len(R)
-        npos = npos + sum(~difficult)
-        class_recs[imagename] = {'bbox': bbox,
-                                 'difficult': difficult,
-                                 'det': det}
-
-    # read dets
-    detfile = detpath.format(classname)
-    with open(detfile, 'r') as f:
-        lines = f.readlines()
-
-    splitlines = [x.strip().split(' ') for x in lines]
-    image_ids = [x[0] for x in splitlines]
-    confidence = np.array([float(x[1]) for x in splitlines])
-    BB = np.array([[float(z) for z in x[2:]] for x in splitlines])
-
-    # sort by confidence
-    sorted_ind = np.argsort(-confidence)
-    sorted_scores = np.sort(-confidence)
-    BB = BB[sorted_ind, :]
-    image_ids = [image_ids[x] for x in sorted_ind]
-
-    # go down dets and mark TPs and FPs
-    nd = len(image_ids)
-    tp = np.zeros(nd)
-    fp = np.zeros(nd)
-    for d in range(nd):
-        R = class_recs[image_ids[d]]
-        bb = BB[d, :].astype(float)
-        ovmax = -np.inf
-        BBGT = R['bbox'].astype(float)
-
-        if BBGT.size > 0:
-            # compute overlaps
-            # intersection
-            ixmin = np.maximum(BBGT[:, 0], bb[0])
-            iymin = np.maximum(BBGT[:, 1], bb[1])
-            ixmax = np.minimum(BBGT[:, 2], bb[2])
-            iymax = np.minimum(BBGT[:, 3], bb[3])
-            iw = np.maximum(ixmax - ixmin + 1., 0.)
-            ih = np.maximum(iymax - iymin + 1., 0.)
-            inters = iw * ih
-
-            # union
-            uni = ((bb[2] - bb[0] + 1.) * (bb[3] - bb[1] + 1.) +
-                   (BBGT[:, 2] - BBGT[:, 0] + 1.) *
-                   (BBGT[:, 3] - BBGT[:, 1] + 1.) - inters)
-
-            overlaps = inters / uni
-            ovmax = np.max(overlaps)
-            jmax = np.argmax(overlaps)
-
-        if ovmax > ovthresh:
-            if not R['difficult'][jmax]:
-                if not R['det'][jmax]:
-                    tp[d] = 1.
-                    R['det'][jmax] = 1
-                else:
-                    fp[d] = 1.
-        else:
-            fp[d] = 1.
-
-    # compute precision recall
-    fp = np.cumsum(fp)
-    tp = np.cumsum(tp)
-    rec = tp / float(npos)
-    # avoid divide by zero in case the first detection matches a difficult
-    # ground truth
-    prec = tp / np.maximum(tp + fp, np.finfo(np.float64).eps)
-    ap = voc_ap(rec, prec, use_07_metric)
-
-    return rec, prec, ap
diff --git a/build/darknet/x64/yolo-voc.2.0.cfg b/build/darknet/x64/yolo-voc.2.0.cfg
deleted file mode 100644
index ceb3f2acf0b..00000000000
--- a/build/darknet/x64/yolo-voc.2.0.cfg
+++ /dev/null
@@ -1,244 +0,0 @@
-[net]
-batch=64
-subdivisions=8
-height=416
-width=416
-channels=3
-momentum=0.9
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-learning_rate=0.0001
-max_batches = 45000
-policy=steps
-steps=100,25000,35000
-scales=10,.1,.1
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-
-#######
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[route]
-layers=-9
-
-[reorg]
-stride=2
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=125
-activation=linear
-
-[region]
-anchors = 1.08,1.19,  3.42,4.41,  6.63,11.38,  9.42,5.11,  16.62,10.52
-bias_match=1
-classes=20
-coords=4
-num=5
-softmax=1
-jitter=.2
-rescore=1
-
-object_scale=5
-noobject_scale=1
-class_scale=1
-coord_scale=1
-
-absolute=1
-thresh = .6
-random=0
diff --git a/build/darknet/x64/yolo-voc.cfg b/build/darknet/x64/yolo-voc.cfg
deleted file mode 100644
index 5650b4c461e..00000000000
--- a/build/darknet/x64/yolo-voc.cfg
+++ /dev/null
@@ -1,259 +0,0 @@
-[net]
-# Testing
-batch=1
-subdivisions=1
-# Training
-# batch=64
-# subdivisions=8
-height=416
-width=416
-channels=3
-momentum=0.9
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-learning_rate=0.001
-burn_in=1000
-max_batches = 80200
-policy=steps
-steps=40000,60000
-scales=.1,.1
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-
-#######
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[route]
-layers=-9
-
-[convolutional]
-batch_normalize=1
-size=1
-stride=1
-pad=1
-filters=64
-activation=leaky
-
-[reorg]
-stride=2
-
-[route]
-layers=-1,-4
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=125
-activation=linear
-
-
-[region]
-anchors =  1.3221, 1.73145, 3.19275, 4.00944, 5.05587, 8.09892, 9.47112, 4.84053, 11.2364, 10.0071
-bias_match=1
-classes=20
-coords=4
-num=5
-softmax=1
-jitter=.3
-rescore=1
-
-object_scale=5
-noobject_scale=1
-class_scale=1
-coord_scale=1
-
-absolute=1
-thresh = .6
-random=1
-
diff --git a/build/darknet/x64/yolo.2.0.cfg b/build/darknet/x64/yolo.2.0.cfg
deleted file mode 100644
index fda339a2b00..00000000000
--- a/build/darknet/x64/yolo.2.0.cfg
+++ /dev/null
@@ -1,244 +0,0 @@
-[net]
-batch=1
-subdivisions=1
-width=416
-height=416
-channels=3
-momentum=0.9
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-learning_rate=0.001
-max_batches = 120000
-policy=steps
-steps=-1,100,80000,100000
-scales=.1,10,.1,.1
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-
-#######
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[route]
-layers=-9
-
-[reorg]
-stride=2
-
-[route]
-layers=-1,-3
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=425
-activation=linear
-
-[region]
-anchors = 0.738768,0.874946,  2.42204,2.65704,  4.30971,7.04493,  10.246,4.59428,  12.6868,11.8741
-bias_match=1
-classes=80
-coords=4
-num=5
-softmax=1
-jitter=.2
-rescore=1
-
-object_scale=5
-noobject_scale=1
-class_scale=1
-coord_scale=1
-
-absolute=1
-thresh = .6
-random=0
diff --git a/build/darknet/x64/yolo.cfg b/build/darknet/x64/yolo.cfg
deleted file mode 100644
index e4d93e522d7..00000000000
--- a/build/darknet/x64/yolo.cfg
+++ /dev/null
@@ -1,259 +0,0 @@
-[net]
-# Testing
-batch=1
-subdivisions=1
-# Training
-# batch=64
-# subdivisions=8
-height=416
-width=416
-channels=3
-momentum=0.9
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-learning_rate=0.001
-burn_in=1000
-max_batches = 500200
-policy=steps
-steps=400000,450000
-scales=.1,.1
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-
-#######
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[route]
-layers=-9
-
-[convolutional]
-batch_normalize=1
-size=1
-stride=1
-pad=1
-filters=64
-activation=leaky
-
-[reorg]
-stride=2
-
-[route]
-layers=-1,-4
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=425
-activation=linear
-
-
-[region]
-anchors =  0.57273, 0.677385, 1.87446, 2.06253, 3.33843, 5.47434, 7.88282, 3.52778, 9.77052, 9.16828
-bias_match=1
-classes=80
-coords=4
-num=5
-softmax=1
-jitter=.3
-rescore=1
-
-object_scale=5
-noobject_scale=1
-class_scale=1
-coord_scale=1
-
-absolute=1
-thresh = .6
-random=1
-
diff --git a/build/darknet/x64/yolo9000.cfg b/build/darknet/x64/yolo9000.cfg
deleted file mode 100644
index e745f78a6e3..00000000000
--- a/build/darknet/x64/yolo9000.cfg
+++ /dev/null
@@ -1,218 +0,0 @@
-[net]
-# Testing
-batch=1
-subdivisions=1
-# Training
-# batch=64
-# subdivisions=8
-batch=1
-subdivisions=1
-height=544
-width=544
-channels=3
-momentum=0.9
-decay=0.0005
-
-learning_rate=0.001
-burn_in=1000
-max_batches = 500200
-policy=steps
-steps=400000,450000
-scales=.1,.1
-
-hue=.1
-saturation=.75
-exposure=.75
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[maxpool]
-size=2
-stride=2
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-filters=28269
-size=1
-stride=1
-pad=1
-activation=linear
-
-[region]
-anchors = 0.77871, 1.14074, 3.00525, 4.31277, 9.22725, 9.61974
-bias_match=1
-classes=9418
-coords=4
-num=3
-softmax=1
-jitter=.2
-rescore=1
-
-object_scale=5
-noobject_scale=1
-class_scale=1
-coord_scale=1
-
-thresh = .6
-absolute=1
-random=1
-
-tree=data/9k.tree
-map = data/coco9k.map
diff --git a/build/darknet/x64/yolov3-voc.cfg b/build/darknet/x64/yolov3-voc.cfg
deleted file mode 100644
index 8f7ba54d0e3..00000000000
--- a/build/darknet/x64/yolov3-voc.cfg
+++ /dev/null
@@ -1,785 +0,0 @@
-[net]
-# Testing
-# batch=1
-# subdivisions=1
-# Training
-batch=64
-subdivisions=32
-width=416
-height=416
-channels=3
-momentum=0.9
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-learning_rate=0.001
-burn_in=1000
-max_batches = 50200
-policy=steps
-steps=40000,45000
-scales=.1,.1
-
-
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=1024
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-######################
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=512
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=1024
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=75
-activation=linear
-
-[yolo]
-mask = 6,7,8
-anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
-classes=20
-num=9
-jitter=.3
-ignore_thresh = .5
-truth_thresh = 1
-random=1
-
-[route]
-layers = -4
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[upsample]
-stride=2
-
-[route]
-layers = -1, 61
-
-
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=512
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=75
-activation=linear
-
-[yolo]
-mask = 3,4,5
-anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
-classes=20
-num=9
-jitter=.3
-ignore_thresh = .5
-truth_thresh = 1
-random=1
-
-[route]
-layers = -4
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[upsample]
-stride=2
-
-[route]
-layers = -1, 36
-
-
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-size=3
-stride=1
-pad=1
-filters=256
-activation=leaky
-
-[convolutional]
-size=1
-stride=1
-pad=1
-filters=75
-activation=linear
-
-[yolo]
-mask = 0,1,2
-anchors = 10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
-classes=20
-num=9
-jitter=.3
-ignore_thresh = .5
-truth_thresh = 1
-random=1
-
diff --git a/build/darknet/x64/yolov3.cfg b/build/darknet/x64/yolov3.cfg
deleted file mode 100644
index 1d94e666918..00000000000
--- a/build/darknet/x64/yolov3.cfg
+++ /dev/null
@@ -1,791 +0,0 @@
-[net]
-# Testing
-#batch=1
-#subdivisions=1
-# Training
-batch=64
-subdivisions=16
-width=416
-height=416
-channels=3
-momentum=0.9
-decay=0.0005
-angle=0
-saturation = 1.5
-exposure = 1.5
-hue=.1
-
-learning_rate=0.001
-burn_in=1000
-max_batches = 500200
-policy=steps
-steps=400000,450000
-scales=.1,.1
-
-show_receptive_field=1
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=3
-stride=1
-pad=1
-activation=leaky
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=32
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=64
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-# Downsample
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=2
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
-from=-3
-activation=linear
-
-[convolutional]
-batch_normalize=1
-filters=128
-size=1
-stride=1
-pad=1
-activation=leaky
-
-[convolutional]
-batch_normalize=1
-filters=256
-size=3
-stride=1
-pad=1
-activation=leaky
-
-[shortcut]
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-[convolutional]
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-[shortcut]
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-[convolutional]
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-[shortcut]
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-[convolutional]
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-[convolutional]
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-[shortcut]
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-[convolutional]
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-[shortcut]
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-[convolutional]
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-[convolutional]
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-[shortcut]
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-[convolutional]
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-[convolutional]
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-[convolutional]
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-[shortcut]
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-[convolutional]
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-[convolutional]
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-[shortcut]
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-[convolutional]
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-[convolutional]
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-[shortcut]
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-[convolutional]
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-[shortcut]
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-######################
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-[convolutional]
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-[convolutional]
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-
-[convolutional]
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-
-[convolutional]
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-[convolutional]
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-[convolutional]
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-[convolutional]
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-[yolo]
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-[route]
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-[convolutional]
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-[yolo]
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-[convolutional]
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-[convolutional]
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-[convolutional]
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-[convolutional]
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-[convolutional]
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-[yolo]
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