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A Bottom-Up Clustering Approach to Unsupervised Person Re-identification, AAAI 2019 (Oral)
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README.md

A Bottom-Up Clustering Approach to Unsupervised Person Re-identification

Pytorch implementation for our paper [Link]. This code is based on the Open-ReID library.

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 diversity regularization term.

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

Citation

Please cite the following paper in your publications if it helps your research:

@inproceedings{lin2019aBottom,
    title     = {A Bottom-Up Clustering Approach to Unsupervised Person Re-identification},
    author    = {Lin, Yutian and Dong, Xuanyi and Zheng, Liang and Yan, Yan and Yang, Yi},
    booktitle = {AAAI Conference on Artificial Intelligence (AAAI)},
    year      = {2019}
}
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