Awesome

HMA

Updates

• ✅ 2023-09-09: Release the codes and results of HMA.
• ✅ 2024-09-26: Release the pre-train models of HMA.

Overview

Benchmark results on SRx4.

Comparison with the state-of-the-art methods. <img src="https://github.com/korouuuuu/HMA/blob/main/figs/Visual_Results.png" width="800"/>

Environment

• PyTorch >= 1.9.1

• BasicSR == 1.3.4.9

• Installation

Install Pytorch first. Then,

pip install -r requirements.txt
python setup.py develop

How To Test

• Refer to ./options/test for the configuration file of the model to be tested, and prepare the testing data and pretrained model.
• The pretrained models are available at Google Drive.
• Then run the following codes (taking HMA_SRx2_pretrain.pth as an example):

python hma/test.py -opt options/test/HMA_SRx2.yml

The testing results will be saved in the ./results folder.

How To Train

• Refer to ./options/train for the configuration file of the model to train.
• Preparation of training data can refer to this page. ImageNet dataset can be downloaded at the official website.
• The training command is like

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=8 --use_env --master_port=4321 hma/train.py -opt options/train/train_HMA_SRx4_from_Imagenet.yml --launcher pytorch

The training logs and weights will be saved in the ./experiments folder.

Results

The inference results on benchmark datasets are available at Google Drive.

Citations

BibTeX

@InProceedings{Chu_2024_CVPR,
author    = {Chu, Shu-Chuan and Dou, Zhi-Chao and Pan, Jeng-Shyang and Weng, Shaowei and Li, Junbao},
title     = {HMANet: Hybrid Multi-Axis Aggregation Network for Image Super-Resolution},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month     = {June},
year      = {2024},
pages     = {6257-6266}
}

Contact

If you have any question, please email douzhichao2021@163.com to discuss with the authors.