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ECN

Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-identification CVPR 2019

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/learn @zhunzhong07/ECN
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0/100

Supported Platforms

Universal

README

Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-identification CVPR 2019

Preparation

Requirements: Python=3.6 and Pytorch>=1.0.0

  1. Install Pytorch

  2. Download dataset

    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
    

Training and test domain adaptation model for person re-ID

# 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

Results

References

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

  • [2] Camera Style Adaptation for Person Re-identification. CVPR 2018.

  • [3] Generalizing A Person Retrieval Model Hetero- and Homogeneously. ECCV 2018.

Citation

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},
}

Contact me

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

Zhun Zhong

View on GitHub
GitHub Stars304
CategoryDevelopment
Updated12d ago
Forks59

Languages

Python

Security Score

95/100

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