v0.2

Features & Improvements:

• Support constructing objects with either configs or explicit arguments. As an example, the entire Mask R-CNN can be built without using configs
• Rename TransformGen to Augmentation and keep TransformGen as an alias. Design the interface of Augmentation so that it can access arbitrary custom data types. See augmentation tutorial for details.
• Improve speed of COCOEvaluator by about 3x
• Support LVIS v1 dataset
• Support GIoU loss in RPN and R-CNN
• Support auto-scaling of batch size and learning rate in DefaultTrainer. See cfg.SOLVER.REFERENCE_WORLD_SIZE

Pre-built Linux binaries are provided for the following environment:

CUDA	torch 1.5	torch 1.4
10.2	install python -m pip install detectron2==0.2 -f \ https://dl.fbaipublicfiles.com/detectron2/wheels/cu102/torch1.5/index.html
10.1	install python -m pip install detectron2==0.2 -f \ https://dl.fbaipublicfiles.com/detectron2/wheels/cu101/torch1.5/index.html	install python -m pip install detectron2==0.2 -f \ https://dl.fbaipublicfiles.com/detectron2/wheels/cu101/torch1.4/index.html
10.0		install python -m pip install detectron2==0.2 -f \ https://dl.fbaipublicfiles.com/detectron2/wheels/cu100/torch1.4/index.html
9.2	install python -m pip install detectron2==0.2 -f \ https://dl.fbaipublicfiles.com/detectron2/wheels/cu92/torch1.5/index.html	install python -m pip install detectron2==0.2 -f \ https://dl.fbaipublicfiles.com/detectron2/wheels/cu92/torch1.4/index.html
cpu	install python -m pip install detectron2==0.2 -f \ https://dl.fbaipublicfiles.com/detectron2/wheels/cpu/torch1.5/index.html	install python -m pip install detectron2==0.2 -f \ https://dl.fbaipublicfiles.com/detectron2/wheels/cpu/torch1.4/index.html