Unsupervised cross-domain object detection based on SSD in PyTorch 1.0

This repository implements SSD (Single Shot MultiBox Detector). The basecode is heavely influenced by this implementation and the Domain Transfer rely on AdaIN

Example SSD output (vgg_ssd300_voc0712).

Example AdaIN online translation (gg_ssd300_voc0712_variation).

Highlights

• PyTorch 1.0: Support PyTorch 1.0 or higher.
• Multi-GPU training and inference: We use DistributedDataParallel, you can train or test with arbitrary GPU(s), the training schema will change accordingly.
• Modular: Add your own modules without pain. We abstract backbone,Detector, BoxHead, BoxPredictor, etc. You can replace every component with your own code without change the code base. For example, You can add EfficientNet as backbone, just add efficient_net.py (ALREADY ADDED) and register it, specific it in the config file, It's done!
• CPU support for inference: runs on CPU in inference time.
• Smooth and enjoyable training procedure: we save the state of model, optimizer, scheduler, training iter, you can stop your training and resume training exactly from the save point without change your training CMD.
• Batched inference: can perform inference using multiple images per batch per GPU.
• Evaluating during training: eval you model every eval_step to check performance improving or not.
• Metrics Visualization: visualize metrics details in tensorboard, like AP, APl, APm and APs for COCO dataset or mAP and 20 categories' AP for VOC dataset.
• Auto download: load pre-trained weights from URL and cache it.

Installation

Requirements

1. Python3
2. PyTorch 1.0 or higher
3. yacs
4. Vizer
5. GCC >= 4.9
6. OpenCV

Step-by-step installation

git clone https://github.com/lufficc/SSD.git
cd SSD
# Required packages: torch torchvision yacs tqdm opencv-python vizer
pip install -r requirements.txt

# Done! That's ALL! No BUILD! No bothering SETUP!

# It's recommended to install the latest release of torch and torchvision.

Train

Setting Up Datasets

For Pascal VOC source dataset and Clipart1k target dataset, make the folder structure like this:

datasets
|__ VOC2007
    |_ JPEGImages
    |_ Annotations
    |_ ImageSets
    |_ SegmentationClass
|__ VOC2012
    |_ JPEGImages
    |_ Annotations
    |_ ImageSets
    |_ SegmentationClass
|__ ...
|
|__ clipart
    |_ JPEGImages
    |_ Annotations
    |_ ImageSets

Training with AdaIN online translation

See vgg_ssd300_voc0712_variationVx examples

You can find an example code on this Colab project

Single GPU training

# for example, train SSD300:
python train.py --config-file configs/vgg_ssd300_voc0712.yaml

Multi-GPU training

# for example, train SSD300 with 4 GPUs:
export NGPUS=4
python -m torch.distributed.launch --nproc_per_node=$NGPUS train.py --config-file configs/vgg_ssd300_voc0712.yaml SOLVER.WARMUP_FACTOR 0.03333 SOLVER.WARMUP_ITERS 1000

The configuration files that I provide assume that we are running on single GPU. When changing number of GPUs, hyper-parameter (lr, max_iter, ...) will also changed according to this paper: Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour.

Evaluate

Single GPU evaluating

# for example, evaluate SSD300:
python test.py --config-file configs/vgg_ssd300_voc0712.yaml

Multi-GPU evaluating

# for example, evaluate SSD300 with 4 GPUs:
export NGPUS=4
python -m torch.distributed.launch --nproc_per_node=$NGPUS test.py --config-file configs/vgg_ssd300_voc0712.yaml