wangleislam / ml_gcn

PyTorch implementation of Multi-Label Image Recognition with Graph Convolutional Networks, CVPR 2019.

ML_GCN.pytorch

PyTorch implementation of Multi-Label Image Recognition with Graph Convolutional Networks, CVPR 2019.

Requirements

Please, install the following packages

• numpy
• torch-0.3.1
• torchnet
• torchvision-0.2.0
• tqdm

Download pretrain models

checkpoint/coco (GoogleDrive)

checkpoint/voc (GoogleDrive)

Options

• lr: learning rate
• lrp: factor for learning rate of pretrained layers. The learning rate of the pretrained layers is lr * lrp
• batch-size: number of images per batch
• image-size: size of the image
• epochs: number of training epochs
• evaluate: evaluate model on validation set
• resume: path to checkpoint

Demo VOC 2007

python3 demo_voc2007_gcn.py data/voc --image-size 448 --batch-size 32 -e --resume checkpoint/voc/voc_checkpoint.pth.tar

Demo COCO 2014

python3 demo_coco_gcn.py data/coco --image-size 448 --batch-size 32 -e --resume checkpoint/coco/coco_checkpoint.pth.tar

Citing this repository

If you find this code useful in your research, please consider citing us:

@inproceedings{ML_GCN_CVPR_2019,
author = {Zhao-Min, Chen and Xiu-Shen, Wei and Peng, Wang and Yanwen, Guo},
title = {{Multi-Label Image Recognition with Graph Convolutional Networks}},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2019}
}

Reference

This project is based on https://github.com/durandtibo/wildcat.pytorch