Body Part-level Domain Alignment for Domain-adaptive Person Re-identification with Transformer Framework[TIFS]

Pytorch Implementation of our paper Body Part-level Domain Alignment for Domain-adaptive Person Re-identification with Transformer Framework accepted by IEEE Transactions on Information Forensics and Security.

Framework of our method

Usage

This project is based on TransReID[1] (paper and official code)
Usage of this code is free for research purposes only.

Requirements

python = 3.9
torch = 1.7.1
torchvision = 0.8.2
opencv-python = 4.5.1.48
timm = 0.4.5
yacs = 0.1.8

we use a single NVIDIA GeForce RTX2080TI GPU(CUDA 10.1) for training and evaluation.

Prepare Datasets

mkdir data

Download the person datasets Market1501,DukeMTMC-reID.

you can find the Duke-new,Market1501-new,MSMT17-new in [DRDL] [2], and Duke-SCT,Market1501-SCT in [SCT-ReID] [3].

Make new directories in data and organize them as follows:

data
├── market1501
│       └── bounding_box_train
│       └── bounding_box_train_s
│       └── bounding_box_train_sct
│       └── bounding_box_test
│       └── query

├── dukemtmcreid
│   └── DukeMTMC-reID 
│       └── bounding_box_train
│       └── bounding_box_train_s
│       └── bounding_box_train_sct
│       └── bounding_box_test
│       └── query

├── MSMT17
│   └── test
│   └── train
│   └── list_gallery.txt
│   └── list_query.txt
│   └── list_train.txt
│   └── list_val.txt

Tips:

Duke:

the file "bounding_box_train_s" is the training set for duke-new and market-new

Market:

the file "bounding_box_train_sct" is the training set for duke-SCT and market-SCT

When use different protocol dataset for train, please change the train_dir in dataset files:

Duke:

dukemtmcreid.py

    self.train_dir = osp.join(self.dataset_dir, 'DukeMTMC-reID/bounding_box_train')
  # self.train_dir = osp.join(self.dataset_dir, 'DukeMTMC-reID/bounding_box_train_s') 
  # self.train_dir = osp.join(self.dataset_dir, 'DukeMTMC-reID/bounding_box_train_sct')

Market:

market1501.py

    self.train_dir = osp.join(self.dataset_dir, 'bounding_box_train')
  # self.train_dir = osp.join(self.dataset_dir, 'bounding_box_train_s') 
  # self.train_dir = osp.join(self.dataset_dir, 'bounding_box_train_sct')

Training

1.Replace dataset path with your own path in vit_3_domain.yml

DATASETS:
  NAMES: ('') # source data
  TARGET: ('') # target data
  ROOT_DIR: ('')

2.Begin the training

  python train.py

Test

1.Replace test file path with your own path in vit_3_domain.yml

TEST:
  WEIGHT: '*.pth'

2.Replace dataset path with your own path in vit_3_domain.yml

# keep the source & target data are same as the *.pth
DATASETS:
  NAMES: ('') # source data
  TARGET: ('') # target data
  ROOT_DIR: ('')

3.Change the num_class in test.py

      # Duke source num_class = 702
      # Market source num_class = 751
      model, _, _ = make_model(cfg, num_class=702, num_class_t=c_pids, camera_num=camera_num, view_num=view_num)

4.Begin the test

  python test.py

Citation

Please cite this paper if it helps your research:

@ARTICLE{BPDA2022,
  author={Wang, Yiming and Qi, Guanqiu and Li, Shuang and Chai, Yi and Li, Huafeng},
  journal={IEEE Transactions on Information Forensics and Security}, 
  title={Body Part-Level Domain Alignment for Domain-Adaptive Person Re-Identification With Transformer Framework}, 
  year={2022},
  volume={17},
  pages={3321-3334}}

Contact

If you have any question, please feel free to contact us. E-mail: shuangli936@gmail.com

Reference

[1]S He, H Luo, P Wang, et al. Transreid: Transformer-based object re-identification[C]//Proceedings of the IEEE/CVF international conference on computer vision. 2021: 15013-15022.
[2]HF Li, KX X, JX Li, et al. Dual-stream Reciprocal Disentanglement Learning for domain adaptation person re-identification[J]//Knowledge-Based Systems. 2022, 251: 109315.
[3]TY Zhang, LX Xie, LH Wei, et al. Single Camera Training for Person Re-Identification[C]. The AAAI Conference on Artificial Intelligence (AAAI). 2020, 34(7): 12878-12885.

Pretrain & load Model Loading

BaiduYun:[url]

Load code：fcbm

lhf12278 / BPDA