https://github.com/open-mmlab/openunreid
PyTorch open-source toolbox for unsupervised or domain adaptive object re-ID.
https://github.com/open-mmlab/openunreid
domain-translation image-retrieval open-set-domain-adaptation pseudo-labeling re-identification unsupervised-domain-adaptation unsupervised-learning
Last synced: 3 months ago
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PyTorch open-source toolbox for unsupervised or domain adaptive object re-ID.
- Host: GitHub
- URL: https://github.com/open-mmlab/openunreid
- Owner: open-mmlab
- License: apache-2.0
- Created: 2020-05-25T03:33:45.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2021-06-14T08:43:21.000Z (about 4 years ago)
- Last Synced: 2025-03-30T05:09:40.069Z (3 months ago)
- Topics: domain-translation, image-retrieval, open-set-domain-adaptation, pseudo-labeling, re-identification, unsupervised-domain-adaptation, unsupervised-learning
- Language: Python
- Homepage:
- Size: 636 KB
- Stars: 402
- Watchers: 12
- Forks: 68
- Open Issues: 23
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# OpenUnReID
## Introduction
`OpenUnReID` is an open-source PyTorch-based codebase for both unsupervised learning (**USL**) and unsupervised domain adaptation (**UDA**) in the context of object re-ID tasks. It provides strong baselines and multiple state-of-the-art methods with highly refactored codes for both *pseudo-label-based* and *domain-translation-based* frameworks. It works with **Python >=3.5** and **PyTorch >=1.1**.
We are actively updating this repo, and more methods will be supported soon. Contributions are welcome.
### Major features
- [x] Distributed training & testing with multiple GPUs and multiple machines.
- [x] High flexibility on various combinations of datasets, backbones, losses, etc.
- [x] GPU-based pseudo-label generation and k-reciprocal re-ranking with quite high speed.
- [x] Plug-and-play domain-specific BatchNorms for any backbones, sync BN is also supported.
- [x] Mixed precision training is supported, achieving higher efficiency.
- [x] A strong cluster baseline, providing high extensibility on designing new methods.
- [x] State-of-the-art methods and performances for both USL and UDA problems on object re-ID.
### Supported methods
Please refer to [MODEL_ZOO.md](docs/MODEL_ZOO.md) for trained models and download links, and please refer to [LEADERBOARD.md](docs/LEADERBOARD.md) for the leaderboard on public benchmarks.
| Method | Reference | USL | UDA |
| ------ | :---: | :-----: | :-----: |
| [UDA_TP](tools/UDA_TP) | [PR'20 (arXiv'18)](https://arxiv.org/abs/1807.11334) | ✓ | ✓ |
| [SPGAN](tools/SPGAN) | [CVPR'18](https://arxiv.org/abs/1711.07027) | n/a | ✓ |
| SSG | [ICCV'19](https://arxiv.org/abs/1811.10144) | ongoing | ongoing |
| [strong_baseline](tools/strong_baseline) | Sec. 3.1 in [ICLR'20](https://openreview.net/pdf?id=rJlnOhVYPS) | ✓ | ✓ |
| [MMT](tools/MMT/) | [ICLR'20](https://openreview.net/pdf?id=rJlnOhVYPS) | ✓ | ✓ |
| [SpCL](tools/SpCL/) | [NeurIPS'20](https://arxiv.org/abs/2006.02713) | ✓ | ✓ |
| SDA | [arXiv'20](https://arxiv.org/abs/2003.06650) | n/a | ongoing |
## Updates
[2020-08-02] Add the leaderboard on public benchmarks: [LEADERBOARD.md](docs/LEADERBOARD.md)
[2020-07-30] `OpenUnReID` v0.1.1 is released:
+ Support domain-translation-based frameworks, [CycleGAN](tools/CycleGAN) and [SPGAN](tools/SPGAN).
+ Support mixed precision training (`torch.cuda.amp` in PyTorch>=1.6), use it by adding `TRAIN.amp True` at the end of training commands.
[2020-07-01] `OpenUnReID` v0.1.0 is released.
## Installation
Please refer to [INSTALL.md](docs/INSTALL.md) for installation and dataset preparation.
## Get Started
Please refer to [GETTING_STARTED.md](docs/GETTING_STARTED.md) for the basic usage of `OpenUnReID`.
## License
`OpenUnReID` is released under the [Apache 2.0 license](LICENSE).
## Citation
If you use this toolbox or models in your research, please consider cite:
```
@inproceedings{ge2020mutual,
title={Mutual Mean-Teaching: Pseudo Label Refinery for Unsupervised Domain Adaptation on Person Re-identification},
author={Yixiao Ge and Dapeng Chen and Hongsheng Li},
booktitle={International Conference on Learning Representations},
year={2020},
url={https://openreview.net/forum?id=rJlnOhVYPS}
}
@inproceedings{ge2020selfpaced,
title={Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-ID},
author={Yixiao Ge and Feng Zhu and Dapeng Chen and Rui Zhao and Hongsheng Li},
booktitle={Advances in Neural Information Processing Systems},
year={2020}
}
```
## Acknowledgement
Some parts of `openunreid` are learned from [torchreid](https://github.com/KaiyangZhou/deep-person-reid) and [fastreid](https://github.com/JDAI-CV/fast-reid). We would like to thank for their projects, which have boosted the research of supervised re-ID a lot. We hope that `OpenUnReID` could well benefit the research community of unsupervised re-ID by providing strong baselines and state-of-the-art methods.
## Contact
This project is developed by Yixiao Ge ([@yxgeee](https://github.com/yxgeee)), Tong Xiao ([@Cysu](https://github.com/Cysu)), Zhiwei Zhang ([@zwzhang121](https://github.com/zwzhang121)).