MixTraining

Official codes for our NeurIPS 2021 paper "Bootstrap Your Object Detector via Mixed Training" (paper).

Main Results:

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	Google
mixed_cascade_rcnn_swin_small	0.528	0.721	0.580	0.366	0.568	0.686	Google

Implementation

• Enviroment

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

• For testing

bash tools/dist_test.sh ${selected_config} 8

where selected_config is one of provided script under the config/bvr folder.

• For training

bash tools/dist_train.sh ${selected_config} 8

where selected_config is one of provided script under the config/bvr folder.

• For more dataset

We have not trained or tested on other dataset. If you would like to use it on other data, please refer to mmdetection.

Citing us

@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}
}