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dangweili
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Description

A simple baseline for pedestrian attribute recognition in surveillance scenarios

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# 253,900
MATLAB
C
19 commits

Implement of Deep Multi-attribute Recognition model under ResNet50 backbone network

Preparation

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
    

Train the model

   sh script/experiment/train.sh

Test the model

   sh script/experiment/test.sh

Demo

   python script/experiment/demo.py

Citation

Please cite this paper in your publications if it helps your research:

@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}
}

Thanks

Partial codes are based on the repository from Houjing Huang.

The code should only be used for academic research.

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