# pedestrian-attribute-recognition-with-GCN

**Repository Path**: milo7hao/pedestrian-attribute-recognition-with-GCN

## Basic Information

- **Project Name**: pedestrian-attribute-recognition-with-GCN
- **Description**: Pytorch implementation of pedestrian attribute recognition with graph convolutional network
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 1
- **Forks**: 0
- **Created**: 2020-08-10
- **Last Updated**: 2021-03-16

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# pedestrian-attribute-recognition-with-GCN

## Preparation

<font face="Times New Roman" size=4>
  
**Prerequisite: Python 3.6 and torch 1.1.0 and tqdm**

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

</font>

## Train the model

<font face="Times New Roman" size=4>
  
  ( 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 <br />
  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)
   ``` 
</font>

## 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**|