Parameter-Free Spatial Attention Network for Person Re-Identification

This is the implementation of the arxiv paper "Parameter-Free Spatial Attention Network for Person Re-Identification".

We propose a modification to the global average pooling called spatial attention which shows a consistent improvement in the generic classfication tasks. Currently the experiments are only conducted on the Person-ReID tasks (which is formulated into a fine-grained classification problem). Our code is mainly based on PCB.

Network

Preparation

Prerequisite: Python 2.7 and Pytorch 0.4.0(we run the code under version 0.4.0, maybe versions <= 0.4.0 also work.)

Dataset

Market-1501 (password: 1ri5)

Training

if you are going to train on the dataset of market-1501, run:

python2 main.py -d market -b 48 -j 4 --epochs 100 --log logs/market/ --combine-trainval --step-size 40 --data-dir Market-1501

also, you can just download a trained weight file from BaiduYun (password: wwjv)

Results

Citiaion

Please cite the paper if it helps your research:

@article{wang2018parameter,
  title={Parameter-Free Spatial Attention Network for Person Re-Identification},
  author={Wang, Haoran and Fan, Yue and Wang, Zexin and Jiao, Licheng and Schiele, Bernt},
  journal={arXiv preprint arXiv:1811.12150},
  year={2018}
}