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 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

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