- Sparse RCNN+DetCo improves from 45.0 AP to 46.5 AP(+1.5) with 3x+ms train. See details in SparseRCNN.
- Pretrained weights has been released.
- State-of-the-art transfer performance on dense prediction tasks.
- Improving 1.6/1.2/1.0 AP than supervised ImageNet pretrain on Mask RCNN-C4/FPN/RetinaNet with COCO 1x schedule.
- Comprehensively improving most instance-level detection and semantic segmentation tasks.
Same as OpenSelfSup.
Coming Soon.
We provide training scripts on COCO, because the performance of COCO is more stable than VOC and Cityscapes. See results in Table 3-5 and Table 13.
We provide Mask RCNN-C4, Mask RCNN-FPN and RetinaNet with 12k, 90k and 180k iterations.
First, you need to download model(.pkl) to benchmarks/detection/pths, and convert pretrain model to detectron2_version. See this script.
Second, start training and testing.
sh tools_local/dist_test_coco.sh $PTH $WORK_DIR
For example:
sh tools_local/dist_test_coco.sh benchmarks/detection/pths/detco_200ep_AA.pkl benchmarks/detection/work_dirs/detco_AA
DetCo-200ep: [Google Drive], [Baidu Drive] Fetch Code: okfp
DetCo-200ep-AA: [Google Drive], [Baidu Drive] Fetch Code: fg7h
Please consider citing our paper in your publications if the project helps your research. BibTeX reference is as follows.
@misc{xie2021detco,
title={DetCo: Unsupervised Contrastive Learning for Object Detection},
author={Enze Xie and Jian Ding and Wenhai Wang and Xiaohang Zhan and Hang Xu and Zhenguo Li and Ping Luo},
year={2021},
eprint={2102.04803},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
We would like to thank Huawei AI Theory Group to support 200+ V100 GPUs for this research project without which this work would not be possible.
For academic use, this project is licensed under the 2-clause BSD License - see the LICENSE file for details. For commercial use, please contact the authors.