MSCLNet for VI-ReID (ECCV 2022)

Modality Synergy Complement Learning with Cascaded Aggregation for Visible-Infrared Person Re-Identification

Getting Started

• Clone this repo:

  git clone https://github.com/bitreidgroup/VI-ReID-MSCLNet.git

  cd VI-ReID-MSCLNet

• Create a conda environment and activate the environment conda env create -f environment.yml

  conda activate mscl

We recommend Python = 3.6, CUDA = 10.0, Cudnn = 7.6.5, Pytorch = 1.2, and CudaToolkit = 10.0.130 for the environment.

Preparing dataset

• RegDB Dataset : The RegDB dataset can be downloaded from this website by submitting a copyright form.

  (Named: "Dongguk Body-based Person Recognition Database (DBPerson-Recog-DB1)" on their website).

  We do not preprocess the RegDB dataset.

• SYSU-MM01 Dataset : The SYSU-MM01 dataset can be downloaded from this website.

• We preprocess the SYSU-MM01 dataset to speed up the training process.

  • if you do not need the identities of the cameras, run the preprocess scripts

    python pre_process_sysu.py

  After running, the training data will be stored in ".npy" format.

  • if your need the identities of the cameras, run :

    python pre_process_sysu_cam.py

  The identities of cameras will be also stored in ".npy" format.

Pre-trained Models and Reproduce our experimental results

You may need manually define the data path in the utils/data_loader.py and utils/data_manager.py first.

bash scripts/reproduce.sh 

4. Citation

If this repository helps your research, please cite :

@inproceedings{zhang2022modality,
  title={Modality Synergy Complement Learning with Cascaded Aggregation for Visible-Infrared Person Re-Identification},
  author={Zhang, Yiyuan and Zhao, Sanyuan and Kang, Yuhao and Shen, Jianbing},
  booktitle={European Conference on Computer Vision},
  pages={462--479},
  year={2022},
  organization={Springer}
}

Acknowledgement

Many thanks to the authors of AGW