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项目作者: zhunzhong07

项目描述 :
不变性问题:域自适应人员重新识别CVPR 2019的示例存储器
高级语言: Python
项目地址: git://github.com/zhunzhong07/ECN.git
创建时间: 2019-03-25T08:34:15Z
项目社区:https://github.com/zhunzhong07/ECN

开源协议:Apache License 2.0

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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:

    1. ECN/data
    2. └───market OR duke OR msmt17
    3. └───bounding_box_train
    4. └───bounding_box_test
    5. └───bounding_box_train_camstyle
    6. |
    7. └───query

Training and test domain adaptation model for person re-ID

  1. # For Duke to Market-1501
  2. python main.py -s duke -t market --logs-dir logs/duke2market-ECN
  3. # For Market-1501 to Duke
  4. python main.py -s market -t duke --logs-dir logs/market2duke-ECN
  5. # For Market-1501 to MSMT17
  6. python main.py -s market -t msmt17 --logs-dir logs/market2msmt17-ECN --re 0
  7. # For Duke to MSMT17
  8. 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:

  1. @inproceedings{zhong2019invariance,
  2. title={Invariance Matters: Exemplar Memory for Domain Adaptive Person Re-identication},
  3. author={Zhong, Zhun and Zheng, Liang and Luo, Zhiming and Li, Shaozi and Yang, Yi},
  4. booktitle={Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  5. year={2019},
  6. }

Contact me

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

Zhun Zhong