HHCL-ReID

This repository is the official implementation of our paper "Hard-sample Guided Hybrid Contrast Learning for Unsupervised Person Re-Identification!".

Requirements

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git clone https://github.com/bupt-ai-cz/HHCL-ReID.git
cd HHCL-ReID
pip install -r requirements.txt
python setup.py develop

Prepare Datasets

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Download the datasets Market-1501,MSMT17,DukeMTMC-reID from this link and unzip them under the directory like:

HHCL-ReID/examples/data
├── market1501
│   └── Market-1501-v15.09.15
└── dukemtmcreid
    └── DukeMTMC-reID

Prepare ImageNet Pre-trained Models for IBN-Net

When training with the backbone of IBN-ResNet, you need to download the ImageNet-pretrained model from this link and save it under the path of examples/pretrained/.

HHCL-ReID/examples
└── pretrained
    └── resnet50_ibn_a.pth.tar

Training

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We utilize 4 GTX-2080TI GPUs for training. Examples:

Market-1501:

CUDA_VISIBLE_DEVICES=0,1,2,3 python examples/train.py -b 256 -a resnet50 -d market1501 --iters 200 --eps 0.45 --momentum 0.1 --num-instances 16 --pooling-type avg --memorybank CMhybrid --epochs 60 --logs-dir examples/logs/market1501/resnet50_avg_cmhybrid

DukeMTMC-reID:

CUDA_VISIBLE_DEVICES=0,1,2,3 python examples/train.py -b 256 -a resnet50 -d dukemtmcreid --iters 200 --eps 0.6 --momentum 0.1 --num-instances 16 --pooling-type avg --memorybank CMhybrid --epochs 60 --logs-dir examples/logs/dukemtmcreid/resnet50_avg_cmhybrid

• use -a resnet50 (default) for the backbone of ResNet-50, and -a resnet_ibn50a for the backbone of IBN-ResNet;
• use --pooling-type gem for Generalized Mean Pooling (GEM) pooling and --smooth for label smoothing.

Evaluation

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To evaluate my model on ImageNet, run:

CUDA_VISIBLE_DEVICES=0 python examples/test.py -d $DATASET --resume $PATH --pooling-type avg

Results

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Our model achieves the following performance on :

Dataset	Market1501				DukeMTMC-reID
Setting	mAP	R1	R5	R10	mAP	R1	R5	R10
Fully Unsupervised	84.2	93.4	97.7	98.5	73.3	85.1	92.4	94.6
Supervised	87.2	94.6	98.5	99.1	80.0	89.8	95.2	96.7

You can download the above models in the paper from Google Drive

Citation

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

@article{hu2021hard,
  title={Hard-sample Guided Hybrid Contrast Learning for Unsupervised Person Re-Identification},
  author={Hu, Zheng and Zhu, Chuang and He, Gang},
  journal={arXiv preprint arXiv:2109.12333},
  year={2021}
}

Acknowledgements

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This project is not possible without multiple great opensourced codebases. We list them below.

• SpCL
• cluster-contrast-reid