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.
Introduction
TL; DR. Co-ming is a self-supervised learning framework for sparsely annotated object detection.
Get Started
- Install cvpods following the instructions
# Install cvpods
git clone https://github.com/Megvii-BaseDetection/cvpods.git
cd cvpods
## build cvpods (requires GPU)
python3 setup.py build develop
## preprare data path
mkdir datasets
ln -s /path/to/your/coco/dataset datasets/coco-
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.
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For fast evaluation, please download trained model from here.
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Run the project
git clone https://github.com/megvii-research/Co-mining.git
# 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.pthResults
| 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:
@article{wang2021comining,
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},
year={2021}
}