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AutoAssign: Differentiable Label Assignment for Dense Object Detection

pipeline

This is a PyTorch implementation of the AutoAssign paper:

@article{zhu2020autoassign,
  title={AutoAssign: Differentiable Label Assignment for Dense Object Detection},
  author={Zhu, Benjin and Wang, Jianfeng and Jiang, Zhengkai and Zong, Fuhang and Liu, Songtao and Li, Zeming and Sun, Jian},
  journal={arXiv preprint arXiv:2007.03496},
  year={2020}
}

Get Started

  1. install cvpods following the instructions
# Install cvpods
git clone https://github.com/Megvii-BaseDetection/cvpods
cd cvpods 
## build cvpods (requires GPU)
pip install -r requirements.txt
python setup.py build develop
## preprare data path
mkdir datasets
ln -s /path/to/your/coco/dataset datasets/coco
  1. run the project
cd auto_assign.res50.fpn.coco.800size.1x

# train
pods_train --num-gpus 8

# test
pods_test --num-gpus 8
# test with provided weights
pods_test --num-gpus 8 MODEL.WEIGHTS /path/to/your/model.pth

Results

Model Multi-scale training Multi-scale testing Testing time / im AP (minival) Link
AutoAssign_Res50_FPN_1x No No 53ms 40.5 download

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Pytorch implementation of "AutoAssign: Differentiable Label Assignment for Dense Object Detection"

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