About

This project implements paper Adversarially Occluded Samples for Person Re-identification using pytorch.

Requirements

• Python 2.7 (Recommend to use Anaconda for easy package management.)
• Pytorch 0.3

The other packages and versions are listed in requirements.txt. You can install them by pip install -r requirements.txt.

Prepare Datasets

Create directory dataset under the project directory, then place datasets in it, as follows.

${project_dir}/dataset
    market1501
        Market-1501-v15.09.15                   # Extracted from Market-1501-v15.09.15.zip, http://www.liangzheng.org/Project/project_reid.html
    cuhk03
        cuhk03-np                               # Extracted from cuhk03-np.zip, https://pan.baidu.com/s/1RNvebTccjmmj1ig-LVjw7A
    duke
        DukeMTMC-reID                           # Extracted from DukeMTMC-reID.zip, https://github.com/layumi/DukeMTMC-reID_evaluation

Then run following command to transform datasets.

python script/dataset/transform_market1501.py
python script/dataset/transform_cuhk03.py
python script/dataset/transform_duke.py

Experiments

Step I: Train Baseline

To train Baseline on Market1501, with GPU 0, run

bash script/experiment/train.sh Baseline market1501 0

Step II: Sliding Window Occlusion

To apply sliding window occlusion with the trained Baseline model and obtain recognition probability, for Market1501, with GPU 0, run

bash script/experiment/sw_occlude.sh market1501 0

Step III: Re-train Model

To re-train the model on Market1501 with original and occluded images, with GPU 0, run

bash script/experiment/train.sh OCCLUSION_TYPE market1501 0

where OCCLUSION_TYPE should be set to No-Adversary, Random, Hard-1, or Sampling.

Citation

If you find our work useful, please kindly cite our paper:

@inproceedings{huang2018adversarially,
  title={Adversarially Occluded Samples for Person Re-Identification},
  author={Huang, Houjing and Li, Dangwei and Zhang, Zhang and Chen, Xiaotang and Huang, Kaiqi},
  booktitle={Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition},
  pages={5098--5107},
  year={2018}
}