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Code for Camera Style Adaptation for Person Re-identification (CVPR 2018).

1. Install Pytorch

2. Download dataset

   • Market-1501 [BaiduYun] [GoogleDriver]

   • DukeMTMC-reID [BaiduYun] (password: chu1) [GoogleDriver]

   • Move them to 'CamStyle/data/market (or duke)'

3. Download CamStyle Images

   • Market-1501-Camstyle [GoogleDriver]

   • DukeMTMC-reID-CamStyle [GoogleDriver]

   • Move them to 'CamStyle/data/market (or duke)/bounding_box_train_camstyle'

1. IDE

# For Market-1501
python main.py -d market --logs-dir logs/market-ide
# For Duke
python main.py -d duke --logs-dir logs/duke-ide

2. IDE + CamStyle

# For Market-1501
python main.py -d market --logs-dir logs/market-ide-camstyle --camstyle 46
# For Duke
python main.py -d duke --logs-dir logs/duke-ide--camstyle --camstyle 46

3. IDE + CamStyle + Random Erasing[4]

# For Market-1501
python main.py -d market --logs-dir logs/market-ide-camstyle-re --camstyle 46 --re 0.5
# For Duke
python main.py -d duke --logs-dir logs/duke-ide--camstyle-re --camstyle 46 --re 0.5

4. IDE + CamStyle + Random Erasing[4] + re-ranking[3]

# For Market-1501
python main.py -d market --logs-dir logs/market-ide-camstyle-re --camstyle 46 --re 0.5 --rerank
# For Duke
python main.py -d duke --logs-dir logs/duke-ide--camstyle-re --camstyle 46 --re 0.5 --rerank

• [1] Our code is conducted based on open-reid

• [2] Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks, ICCV 2017

• [3] Re-ranking Person Re-identification with k-reciprocal Encoding. CVPR 2017.

• [4] Random Erasing Data Augmentation. Arxiv 2017.

If you find this code useful in your research, please consider citing:

@inproceedings{zhong2018camera,
title={Camera Style Adaptation for Person Re-identification},
author={Zhong, Zhun and Zheng, Liang and Zheng, Zhedong and Li, Shaozi and Yang, Yi},
booktitle={CVPR},
year={2018}
}

If you have any questions about this code, please do not hesitate to contact me.

Zhun Zhong