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Self-Supervised Pre-Training for Transformer-Based Person Re-Identification

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Python >=3.6 PyTorch >=1.7

Self-Supervised Pre-Training for Transformer-Based Person Re-Identification [pdf]

The official repository for Self-Supervised Pre-Training for Transformer-Based Person Re-Identification.

Requirements

Installation

pip install -r requirements.txt

We recommend to use /torch=1.7.1 /torchvision=0.8.2 /timm=0.3.4 /cuda>10.1 /faiss-gpu=1.7.1/ 16G or 32G V100 for training and evaluation. If you find some packages are missing, please install them manually. You can refer to DINO, TransReID and cluster-contrast-reid to install the environment of pre-training, supervised ReID and unsupervised ReID, respectively.

Prepare Datasets

mkdir data

Download the datasets:

  • Market-1501
  • MSMT17
  • LUPerson. We don't have the copyright of the LUPerson dataset. Please contact authors of LUPerson to get this dataset.
  • You can download the file list ordered by the CFS score for the LUPerson. [CFS_list.pkl]

Then unzip them and rename them under the directory like

data
├── market1501
│   └── bounding_box_train
│   └── bounding_box_test
│   └── ..
├── MSMT17
│   └── train
│   └── test
│   └── ..
└── LUP
    └── images 
    └── CFS_list.pkl 

Pre-trained Models

Model Download
ViT-S/16 link
ViT-S/16+ICS link
ViT-B/16+ICS link

Please download pre-trained models and put them into your custom file path.

ReID performance

We have reproduced the performance to verify the reproducibility. The reproduced results may have a gap of about 0.1~0.2% with the numbers in the paper.

Supervised ReID

Market-1501
Model Image Size Paper Reproduce Download
ViT-S/16 256*128 91.0/96.0 91.2/95.8 model / log
ViT-S/16+ICS 256*128 91.3/96.2 91.4/96.2 model / log
ViT-B/16+ICS 384*128 93.2/96.7 93.1/96.6 model / log
MSMT17
Model Image Size Paper Reproduce Download
ViT-S/16 256*128 66.1/84.6 66.3/84.8 model / log
ViT-S/16+ICS 256*128 68.1/86.1 68.3/86.2 model / log
ViT-B/16+ICS 384*128 75.0/89.6 75.1/89.6 model / log

USL ReID

Market-1501
Model Image Size Paper Reproduce Download
ViT-S/16 256*128 88.2/94.2 88.4/94.6 model / log
ViT-S/16+ICS 256*128 89.6/95.3 89.5/95.3 model / log
MSMT17
Model Image Size Paper Reproduce Download
ViT-S/16 256*128 40.9/66.4 40.9/66.4 model / log
ViT-S/16+ICS 256*128 50.6/75.0 50.6/75.0 model / log

UDA ReID

MSMT2Market
Model Image Size Paper Reproduce Download
ViT-S/16 256*128 89.4/95.4 89.2/95.3 model / log
ViT-S/16+ICS 256*128 89.9/95.5 89.9/95.4 model / log
Market2MSMT
Model Image Size Paper Reproduce Download
ViT-S/16 256*128 47.4/70.8 47.7/71.2 model / log
ViT-S/16+ICS 256*128 57.8/79.5 57.8/79.4 model / log

Acknowledgment

Our implementation is mainly based on the following codebases. We gratefully thank the authors for their wonderful works.

LUPerson, DINO, TransReID, cluster-contrast-reid.

Citation

If you find this code useful for your research, please cite our paper

@article{luo2021self,
  title={Self-Supervised Pre-Training for Transformer-Based Person Re-Identification},
  author={Luo, Hao and Wang, Pichao and Xu, Yi and Ding, Feng and Zhou, Yanxin and Wang, Fan and Li, Hao and Jin, Rong},
  journal={arXiv preprint arXiv:2111.12084},
  year={2021}
}

Contact

If you have any question, please feel free to contact us. E-mail: michuan.lh@alibaba-inc.com or haoluocsc@zju.edu.cn

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Self-Supervised Pre-Training for Transformer-Based Person Re-Identification

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