# pixel_sampling

**Repository Path**: chaucerg/pixel_sampling

## Basic Information

- **Project Name**: pixel_sampling
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-11-13
- **Last Updated**: 2021-11-13

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Semantic-guided Pixel Sampling for Cloth-Changing Person Re-identification

In our paper [IEEE](https://ieeexplore.ieee.org/abstract/document/9463711/), we propose a semantic-guided pixel sampling approach for the cloth-changing person re-ID task.  This repo contains the training and testing codes.

## Prepare Dataset
1. Download the PRCC dataset:  [PRCC](http://isee-ai.cn/~yangqize/clothing.html)
2. Obtain the human body parts: [SCHP](https://github.com/PeikeLi/Self-Correction-Human-Parsing)


## Trained Models
The trained models can be downloaded in BaiduPan: [models](https://pan.baidu.com/s/1JOOJp_NPbsU19DdBr7ze9g) password: 6ulj
```
Put the trained models to corresponding directories:
>pixel_sampling/imagenet/resnet50-19c8e357.pth
>pixel_sampling/logs/prcc_base/checkpoint_best.pth
>pixel_sampling/logs/prcc_hpm/checkpoint_best.pth
>...... 
 ```
 
 ## Training and Testing Models
 Only need to modify several parameters:
 ```
 >parser.add_argument('--train', type=str, default='train', help='train, test')
 
 >parser.add_argument('--data_dir', type=str, default='/data/prcc/')
```
then
```
>python train_prcc_base.py
```

## Citations
```bibtex
@article{shu2021semantic,
  title={Semantic-guided Pixel Sampling for Cloth-Changing Person Re-identification},
  author={Shu, Xiujun and Li, Ge and Wang, Xiao and Ruan, Weijian and Tian, Qi},
  journal={IEEE Signal Processing Letters},
  volume={28},
  pages={1365-1369},
  year={2021}, 
}
```