Prerequisite: Python 2.7 and Pytorch 0.3.1

1. Install Pytorch

2. Download and prepare the dataset as follow:

   a. PETA Baidu Yun, passwd: 5vep, or Google Drive.

   ./dataset/peta/images/*.png ./dataset/peta/PETA.mat ./dataset/peta/README 

   python script/dataset/transform_peta.py 

   b. RAP Google Drive.

   ./dataset/rap/RAP_dataset/*.png ./dataset/rap/RAP_annotation/RAP_annotation.mat 

   python script/dataset/transform_rap.py 

   c. PA100K Links

   ./dataset/pa100k/data/*.png ./dataset/pa100k/annotation.mat 

   python script/dataset/transform_pa100k.py 

   d. RAP(v2) Links.

   ./dataset/rap2/RAP_dataset/*.png ./dataset/rap2/RAP_annotation/RAP_annotation.mat 

   python script/dataset/transform_rap2.py 

sh script/experiment/train.sh 

sh script/experiment/test.sh 

python script/experiment/demo.py 

@inproceedings{li2015deepmar, author = {Dangwei Li and Xiaotang Chen and Kaiqi Huang}, title = {Multi-attribute Learning for Pedestrian Attribute Recognition in Surveillance Scenarios}, booktitle = {ACPR}, pages={111--115}, year = {2015} } 

Partial codes are based on the repository from Houjing Huang.

The code should only be used for academic research.