AutoAssign: Differentiable Label Assignment for Dense Object Detection
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
- 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- 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.pthResults
| Model | Multi-scale training | Multi-scale testing | Testing time / im | AP (minival) | Link |
|---|---|---|---|---|---|
| AutoAssign_Res50_FPN_1x | No | No | 53ms | 40.5 | download |