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README.md

Generalizing A Person Retrieval Model Hetero- and Homogeneously

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Code for Generalizing A Person Retrieval Model Hetero- and Homogeneously (ECCV 2018). [paper]

Preparation

Requirements: Python=3.6 and Pytorch=0.4.0

  1. Install Pytorch

  2. Download dataset

    • reid_dataset [GoogleDriver]

    • The reid_dataset including Market-1501 (with CamStyle), DukeMTMC-reID (with CamStyle), and CUHK03

    • Unzip reid_dataset under 'HHL/data/'

CamStyle Generation

You can train CamStyle model and generate CamStyle imgaes with stargan4reid

Training and test domain adaptation model for person re-ID

  1. Baseline
# For Duke to Market-1501
python baseline.py -s duke -t market --logs-dir logs/duke2market-baseline
# For Market-1501 to Duke
python baseline.py -s market -t duke --logs-dir logs/market2duke-baseline
  1. HHL
# For Duke to Market-1501
python HHL.py -s duke -t market --logs-dir logs/duke2market-HHL
# For Market-1501 to Duke
python HHL.py -s market -t duke --logs-dir logs/market2duke-HHL

Results

Duke to Market Market to Duke
Methods Rank-1 mAP Rank-1 mAP
Baseline 44.6 20.6 32.9 16.9
HHL 62.2 31.4 46.9 27.2

References

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

  • [2] StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation , CVPR 2018

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

Citation

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

@inproceedings{zhong2018generalizing,
title={Generalizing A Person Retrieval Model Hetero- and Homogeneously},
author={Zhong, Zhun and Zheng, Liang and Li, Shaozi and Yang, Yi},
booktitle ={ECCV},
year={2018}
}

Contact me

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

Zhun Zhong

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Generalizing A Person Retrieval Model Hetero- and Homogeneously ECCV 2018

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