Dispersion-based-Clustering

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Dispersion-based-Clustering

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

Dispersion-based-Clustering 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

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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](https://arxiv.org/pdf/1906.01308.pdf) to access the manuscript.

This code is based on the [Open-ReID](https://github.com/Cysu/open-reid) library and adopted from [BUC](https://github.com/vana77/Bottom-up-Clustering-Person-Re-identification).

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

### Download datasets 
- DukeMTMC-VideoReID: [[Direct Link]](http://vision.cs.duke.edu/DukeMTMC/data/misc/DukeMTMC-VideoReID.zip)  [[Google Drive]](https://drive.google.com/open?id=1Fdu5GK-C7P8M9QiLbiQNyT_RUFt8oFco)  [[BaiduYun]](https://pan.baidu.com/s/1qL39rnjTjyzjqaD-Wuv8KQ). This [page](https://github.com/Yu-Wu/DukeMTMC-VideoReID) contains more details and baseline code.
- MARS: [[Google Drive]](https://drive.google.com/open?id=1m6yLgtQdhb6pLCcb6_m7sj0LLBRvkDW0)   [[BaiduYun]](https://pan.baidu.com/s/1mByTdvXFsmobXOXBEkIWFw).
- Market-1501: [[Direct Link]](http://45.32.69.75/share/market1501.tar)
- DukeMTMC-reID: [[Direct Link]](http://45.32.69.75/share/duke.tar)
- Move the downloaded zip files to `./data/` and unzip here.

## Usage

```shell
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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