Cluster-aware Diversity Samples Mining for Unsupervised Person Re-Identification

This repository is the official implementation of our paper "Cluster-aware Diversity Samples Mining for Unsupervised Person Re-Identification".

Requirements

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git clone https://github.com/colinzhaoxp/CDSM.git
cd CDSM
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:

CDSM/examples/data
├── market1501
│   └── Market-1501-v15.09.15
├── dukemtmcreid
│   └── DukeMTMC-reID
└── msmt17
    └── MSMT17_V1

Training

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Examples:

Market-1501:

python examples/train.py -b 128 -a resnet50 -d market1501 --k1 25 --iters 400 --eps 0.45 --momentum 0.05 --num-instances 8 --mem-instances 4 --hard-weight 0.5 --pooling-type gem --memorybank CMhybird_v5 --epochs 50 --logs-dir examples/logs/market1501/resnet50_gem_cmhybird

DukeMTMC-reID:

python examples/train.py -b 256 -a resnet50 -d dukemtmcreid --k1 30 --iters 400 --eps 0.6 --momentum 0.05 --num-instances 8 --mem-instances 4 --hard-weight 0.5 --pooling-type gem --memorybank CMhybird_v5 --epochs 50 --logs-dir examples/logs/dukemtmcreid/resnet50_gem_cmhybird

MSMT17:

python examples/train.py -b 256 -a resnet50 -d msmt17 --k1 30 --iters 400 --eps 0.6 --momentum 0.05 --num-instances 8 --mem-instances 4 --hard-weight 0.5 --pooling-type gem --memorybank CMhybird_v5 --epochs 50 --logs-dir examples/logs/msmt17/resnet50_gem_cmhybird

• use -a resnet50 (default) for the backbone of ResNet-50;
• 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:

python examples/test.py -d $DATASET --resume $PATH --pooling-type gem

Results

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Our model achieves the following performance on :

Dataset	Market1501				DukeMTMC-reID				MSMT17
Setting	mAP	R1	R5	R10	mAP	R1	R5	R10	mAP	R1	R5	R10
Fully Unsupervised	85.6	93.9	97.8	98.5	73.7	85.1	92.4	94.7	31.0	61.1	71.3	76.2

Citation

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

@inproceedings{zhao2022cdsm,  
    title={Cluster-aware Diversity Samples Mining for Unsupervised Person Re-Identification},
    author={Zhao, Xinpeng and Dou, Xiao and Zhang, Xiaowei},  
    booktitle={2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC)},
    year={2022},
    pages={1707-1712},  
    doi={10.1109/SMC53654.2022.9945479}
}

Acknowledgements

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

• SpCL
• HHCL