Official codes for our NeurIPS 2021 paper "Bootstrap Your Object Detector via Mixed Training" (paper).
| Model | mAP | AP50 | AP75 | APs | APm | APl | Link |
|---|---|---|---|---|---|---|---|
| mixed_faster_rcnn_swin_small | 0.503 | 0.716 | 0.552 | 0.347 | 0.540 | 0.659 | |
| mixed_cascade_rcnn_swin_small | 0.528 | 0.721 | 0.580 | 0.366 | 0.568 | 0.686 |
torch==1.6.0
torchvision==0.7.0
wandb==0.10.26
apex==0.1
mmdet==2.11.0
mmcv-full==1.3.0
Install required packages with
cd ${your_code_dir}
mkdir -p thirdparty
git clone https://github.com/open-mmlab/mmdetection.git thirdparty/mmdetection
cd thirdparty/mmdetection && git checkout v2.11.0 && python -m pip install -e .
python -m pip install -e .
mkdir -p data
ln -s ${your_coco_path} data/coco
bash tools/dist_test.sh ${selected_config} 8where selected_config is one of provided script under the config/bvr folder.
bash tools/dist_train.sh ${selected_config} 8where selected_config is one of provided script under the config/bvr folder.
We have not trained or tested on other dataset. If you would like to use it on other data, please refer to mmdetection.
@inproceedings{xu2021bootstrap,
title={Bootstrap Your Object Detector via Mixed Training},
author={Xu, Mengde and Zhang, Zheng and Wei, Fangyun and Lin, Yutong and Cao, Yue and Lin, Stephen and Hu, Han and Bai, Xiang},
journal={Advances in Neural Information Processing Systems},
volume={34},
year={2021}
}
