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

Distributed training & testing with multiple GPUs and multiple machines.
High flexibility on various combinations of datasets, backbones, losses, etc.
GPU-based pseudo-label generation and k-reciprocal re-ranking with quite high speed.
Plug-and-play domain-specific BatchNorms for any backbones, sync BN is also supported.
Mixed precision training is supported, achieving higher efficiency.
A strong cluster baseline, providing high extensibility on designing new methods.
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 for trained models and download links, and please refer to LEADERBOARD.md for the leaderboard on public benchmarks.

Method	Reference	USL	UDA
UDA_TP	PR'20 (arXiv'18)	✓	✓
SPGAN	CVPR'18	n/a	✓
SSG	ICCV'19	ongoing	ongoing
strong_baseline	Sec. 3.1 in ICLR'20	✓	✓
MMT	ICLR'20	✓	✓
SpCL	NeurIPS'20	✓	✓
SDA	arXiv'20	n/a	ongoing

Updates

[2020-08-02] Add the leaderboard on public benchmarks: LEADERBOARD.md

[2020-07-30] OpenUnReID v0.1.1 is released:

Support domain-translation-based frameworks, CycleGAN and 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 for installation and dataset preparation.

Get Started

Please refer to GETTING_STARTED.md for the basic usage of OpenUnReID.

License

OpenUnReID is released under the Apache 2.0 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 and fastreid. 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), Tong Xiao (@Cysu), Zhiwei Zhang (@zwzhang121).

sokazaki/OpenUnReID