Co-mining: Self-Supervised Learning for Sparsely Annotated Object Detection

This repository is an official implementation of the AAAI 2021 paper Co-mining: Self-Supervised Learning for Sparsely Annotated Object Detection.


TL; DR. Co-ming is a self-supervised learning framework for sparsely annotated object detection.


Get Started

  1. Install cvpods following the instructions

# Install cvpods
git clone
cd cvpods 
## build cvpods (requires GPU)
python3 build develop
## preprare data path
mkdir datasets
ln -s /path/to/your/coco/dataset datasets/coco
  1. Download the sparse-annotations from here of four cases and put them into /coco/annotations/. Note that the annotation of “missing_50p” is from the authors of BRL paper.

  2. For fast evaluation, please download trained model from here.

  3. Run the project

git clone

# for example(e.g. miss50p)
cd co-mining/retinanet.res101.comining.score.06.miss50p/

# 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


Model Multi-scale training AP (minival) Link
Comining_RetinaNet_Res50_Full No 36.8 download
Comining_RetinaNet_Res50_Easy No 35.4 download
Comining_RetinaNet_Res50_Hard No 31.8 download
Comining_RetinaNet_Res50_Extreme No 23.0 download
Comining_RetinaNet_Res101_Miss50p No 33.9 download

Citing Co-mining

If you find Co-mining useful to your research, please consider citing:

  title={Co-mining: Self-Supervised Learning for Sparsely Annotated Object Detection},
  author={Wang, Tiancai and Yang, Tong and Cao, Jiale and Zhang, Xiangyu},
  journal={Proceedings of the AAAI conference on artificial intelligence},


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