UCT: Unbiased Feature Learning with Causal Intervention for Visible-Infrared Person Re-identification (TOMM 2024)

Pytorch code for "Unbiased Feature Learning with Causal Intervention for Visible-Infrared Person Re-identification"

Updates

[24.06.28] TOMM 2024 published. We will upload the code and weight very soon. :)

[24.01.04] We have optimized our model for better performance.

[23.08.16] We publish the checkpoint for testing our model.

[23.08.16] We publish the code for testing, and after the paper is accepted, we will publish the training code.

Results

Datasets	Settings	Pretrained	Rank@1	mAP	mINP	Model
#SYSU-MM01	All-Search	ImageNet	81.72%	76.59%	63.80%	wangpan
#SYSU-MM01	Indoor-Search	ImageNet	84.67%	85.28%	82.10%	available soon
#RegDB	Visible2Infrared	ImageNet	95.29%	95.87%	95.23%	wangpan
#RegDB	Infrared2Visible	ImageNet	94.31%	93.44%	92.35%	available soon

Installation

# Create python environment (optional)
conda create -n UCT python==3.8.16
conda activate UCT

# Install python dependencies
pip install pytorch==1.13.1 torchvision==0.14.1 

Data Preparation

• (1) 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).

  • A private download link can be requested in Github.

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

  • run python pre_process_sysu.py to prepare the dataset, the training data will be stored in ".npy" format.

Evaluation

Test a model on SYSU-MM01 or RegDB dataset by

python test_mine.py --mode all --model_path 'downloaded/checkpoint/path/' --resume 'sysu_all_mAP_best.t' --gpu 1 --dataset sysu

• --dataset: which dataset "sysu" or "regdb".
• --mode: "all" or "indoor" all search or indoor search (only for sysu dataset).
• --trial: testing trial (only for RegDB dataset).
• --model_path: the saved model path.
• --resume: the saved model name.
• --gpu: which gpu to run.

For example:

Download sysu_all_mAP_best.t in wangpan and put it in '/home/user/UCT/'.

Test our model on SYSU-MM01 dataset in All-Search settings by

python test_mine.py --mode all --model_path '/home/user/UCT/' --resume 'sysu_all_mAP_best.t' --gpu 1 --dataset sysu

Usage

Our code extends the pytorch implementation of HCT in Github.