Awesome
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 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
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 |