https://github.com/2014gaokao/pedestrian-attribute-recognition-with-GCN

Pytorch implementation of pedestrian attribute recognition with graph convolutional network
https://github.com/2014gaokao/pedestrian-attribute-recognition-with-GCN

attribute cnn gcn pytorch

Last synced: about 1 month ago
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Pytorch implementation of pedestrian attribute recognition with graph convolutional network

• Host: GitHub
• URL: https://github.com/2014gaokao/pedestrian-attribute-recognition-with-GCN
• Owner: 2014gaokao
• Created: 2019-06-07T04:35:21.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2019-10-10T13:03:17.000Z (almost 5 years ago)
• Last Synced: 2024-07-07T06:35:40.265Z (2 months ago)
• Topics: attribute, cnn, gcn, pytorch
• Language: Python
• Homepage:
• Size: 9.22 MB
• Stars: 77
• Watchers: 5
• Forks: 17
• Open Issues: 15
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• awesome-gcn - 2014gaokao/pedestrian-attribute-recognition-with-GCN

README

        
# pedestrian-attribute-recognition-with-GCN

## Preparation

  

**Prerequisite: Python 3.6 and torch 1.1.0 and tqdm**

**Download RAP(v2) dataset and annotation then put in dataset directory**

## Train the model

  

  ( If you simply want to run the demo code without further modification, you might skip this step by downloading the weight file from

  [Baidu Yun](https://pan.baidu.com/s/1m4Na3AFtZrl5i1jsEJD8qQ) with password "5z1j" and put the model_best.pth.tar into directory         /checkpoint/ then run 


  python demo.py )

   ```

   python transform_rap2.py     (transform data)

   python glove.py      (word2vec)

   python adj.py      (Adjacency matrix)

   python train.py      (weight file will locate in checkpoint directory)

   ``` 

## Methodology

![image](https://github.com/2014gaokao/pedestrian-attribute-recognition-with-GCN/blob/master/image/%E7%BB%98%E5%9B%BE1.jpg)

## Superiority

| method | mA | accuracy | precision | recall | F1 |

|:-----:|---|---|---|---|---|

|ACN|69.66|62.61|80.12|72.26|75.98|

|DeepMar|73.79|	62.02|	74.92|	76.21	|75.56|

|HP-Net|76.12	|65.39	|77.33	|78.79	|78.05|

|JRL|77.81|	-|	78.11|	78.98|	78.58|

|VeSPa|77.70	|67.35	|79.51|	79.67	|79.59|

|Ours|75.97	|**68.99**	|**81.48**	|**79.97** |**80.72**|