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The source code for our work "Towards better Validity: Dispersion based Clustering for unsupervised Person Re-identification"

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Towards better Validity: Dispersion based Clustering for unsupervised Person Re-identification

This respository is an official implementation of our paper titled "Towards better Validity: Dispersion based Clustering for unsupervised Person Re-identification". Click here to access the manuscript.

This code is based on the Open-ReID library and adopted from BUC.

Preparation

Dependencies

  • Python 3.6
  • PyTorch (version >= 0.4.1)
  • h5py, scikit-learn, metric-learn, tqdm

Download datasets

Usage

sh ./run.sh

--size_penalty parameter lambda to balance the intra-dispersion regularization term.

--merge_percent percent of data to merge at each iteration.

Citation

If you use this code or part of it in your work, please cite our paper:

@article{ding2019towards,
  title={Towards better Validity: Dispersion based Clustering for Unsupervised Person Re-identification},
  author={Ding, Guodong and Khan, Salman and Tang, Zhenmin and Zhang, Jian and Porikli, Fatih},
  journal={arXiv preprint arXiv:1906.01308},
  year={2019}
}

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The source code for our work "Towards better Validity: Dispersion based Clustering for unsupervised Person Re-identification"

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