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Install Pytorch
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Download dataset
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Market-1501 [BaiduYun] [GoogleDriver] CamStyle (generated by CycleGAN) [GoogleDriver] [BaiduYun] (password: 6bu4)
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DukeMTMC-reID [BaiduYun] (password: bhbh) [GoogleDriver] CamStyle (generated by CycleGAN) [GoogleDriver] [BaiduYun] (password: 6bu4)
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MSMT17 + CamStyle (generated by StarGAN) [BaiduYun] (password: 6bu4) [GoogleDriver] We reformulate the structure of MSMT17 the same as Market-1501.
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Unzip each dataset and corresponding CamStyle under 'ECN/data/'
Ensure the File structure is as follow:
ECN/data │ └───market OR duke OR msmt17 │ └───bounding_box_train │ └───bounding_box_test │ └───bounding_box_train_camstyle | └───query -
# For Duke to Market-1501
python main.py -s duke -t market --logs-dir logs/duke2market-ECN
# For Market-1501 to Duke
python main.py -s market -t duke --logs-dir logs/market2duke-ECN
# For Market-1501 to MSMT17
python main.py -s market -t msmt17 --logs-dir logs/market2msmt17-ECN --re 0
# For Duke to MSMT17
python main.py -s duke -t msmt17 --logs-dir logs/duke2msmt17-ECN --re 0
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[1] Our code is conducted based on open-reid
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[2] Camera Style Adaptation for Person Re-identification. CVPR 2018.
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[3] Generalizing A Person Retrieval Model Hetero- and Homogeneously. ECCV 2018.
If you find this code useful in your research, please consider citing:
@inproceedings{zhong2019invariance,
title={Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-identification},
author={Zhong, Zhun and Zheng, Liang and Luo, Zhiming and Li, Shaozi and Yang, Yi},
booktitle={Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2019},
}
If you have any questions about this code, please do not hesitate to contact me.