# MAN **Repository Path**: buptybx/MAN ## Basic Information - **Project Name**: MAN - **Description**: https://github.com/icandle/MAN - **Primary Language**: Unknown - **License**: Apache-2.0 - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2026-01-31 - **Last Updated**: 2026-01-31 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README ##
Multi-scale Attention Network for Single Image Super-Resolution
[Yan Wang](https://scholar.google.com/citations?user=SXIehvoAAAAJ&hl=en)†, [Yusen Li](https://scholar.google.com/citations?user=4EJ9aekAAAAJ&hl=en&oi=ao)†, Gang Wang, Xiaoguang Liu

Nankai University

**Overview:** To unleash the potential of ConvNet in super-resolution, we propose a multi-scale attention network (MAN), by coupling a classical multi-scale mechanism with emerging large kernel attention. In particular, we proposed multi-scale large kernel attention (MLKA) and gated spatial attention unit (GSAU). Experimental results illustrate that our MAN can perform on par with SwinIR and achieve varied trade-offs between state-of-the-art performance and computations. This repository contains [PyTorch](https://pytorch.org/) implementation for ***MAN*** (CVPRW 2024).
Table of contents

1. [Requirements](#%EF%B8%8F-requirements) 2. [Datasets](#-datasets) 3. [Implementary Details](#-implementary-details) 4. [Train and Test](#%EF%B8%8F-train-and-test) 5. [Results and Models](#-results-and-models) 6. [Acknowledgments](#-acknowledgments) 7. [Citation](#-citation)

--- ⚙️ Requirements --- - [PyTorch >= 1.8](https://pytorch.org/) - [BasicSR >= 1.3.5](https://github.com/xinntao/BasicSR-examples/blob/master/README.md) 🎈 Datasets --- *Training*: [DIV2K](https://data.vision.ee.ethz.ch/cvl/DIV2K/) or [DF2K](https://openmmlab.medium.com/awesome-datasets-for-super-resolution-introduction-and-pre-processing-55f8501f8b18). *Testing*: Set5, Set14, BSD100, Urban100, Manga109 ([Google Drive](https://drive.google.com/file/d/1SbdbpUZwWYDIEhvxQQaRsokySkcYJ8dq/view?usp=sharing)/[Baidu Netdisk](https://pan.baidu.com/s/1zfmkFK3liwNpW4NtPnWbrw?pwd=nbjl)). *Preparing*: Please refer to the [Dataset Preparation](https://github.com/XPixelGroup/BasicSR/blob/master/docs/DatasetPreparation.md) of BasicSR. 🔎 Implementary Details --- *Network architecture*: Group number (n_resgroups): *1 for simplicity*, MAB number (n_resblocks): *5/24/36*, channel width (n_feats): *48/60/180* for *tiny/light/base MAN*.


Overview of the proposed MAN constituted of three components: the shallow feature extraction module (SF), the deep feature extraction module (DF) based on multiple multi-scale attention blocks (MAB), and the high-quality image reconstruction module.   *Component details:* Three multi-scale decomposition modes are utilized in MLKA. The 7×7 depth-wise convolution is used in the GSAU.


Details of Multi-scale Large Kernel Attention (MLKA), Gated Spatial Attention Unit (GSAU), and Large Kernel Attention Tail (LKAT).   ▶️ Train and Test --- The [BasicSR](https://github.com/XPixelGroup/BasicSR) framework is utilized to train our MAN, also testing. #### Training with the example option ``` CUDA_VISIBLE_DEVICES=0,1,2,3 \ python -m torch.distributed.launch --nproc_per_node=4 --master_port=4321 train.py -opt options/trian_MAN.yml --launcher pytorch ``` #### Testing with the example option ``` python test.py -opt options/test_MAN.yml ``` The training/testing results will be saved in the `./experiments` and `./results` folders, respectively. 📊 Results and Models --- Pretrained models available at [Google Drive](https://drive.google.com/drive/folders/1sARYFkVeTIFVCa2EnZg9TjZvirDvUNOL?usp=sharing) and [Baidu Netdisk](https://pan.baidu.com/s/15CTY-mgdTuOc1I8mzIA4Ug?pwd=mans) (pwd: **mans** for all links). |HR (x4)|MAN-tiny|[EDSR-base+](https://github.com/sanghyun-son/EDSR-PyTorch)|MAN-light|[EDSR+](https://github.com/sanghyun-son/EDSR-PyTorch)|MAN| | :----- | :-----: | :-----: | :-----: | :-----: | :-----: | | | ||||| | | ||||| | | ||||| | | ||||| |**Params/FLOPs**| 150K/8G|1518K/114G|840K/47G|43090K/2895G|8712K/495G| Results of our MAN-tiny/light/base models. Set5 validation set is used below to show the general performance. The visual results of five testsets are provided in the last column. | Methods | Params | FLOPs |PSNR/SSIM (x2)|PSNR/SSIM (x3)|PSNR/SSIM (x4)|Results| |:---------|:---------:|:--------:|:------:|:------:|:------:|:--------:| | MAN-tiny | 150K | 8.4G | 37.91/0.9603 | 34.23/0.9258 | 32.07/0.8930 | [x2](https://pan.baidu.com/s/1mYkGvAlz0bSZuCVubkpsmg?pwd=mans)/[x3](https://pan.baidu.com/s/1RP5gGu-QPXTkH1NPH7axag?pwd=mans)/[x4](https://pan.baidu.com/s/1u22su2bT4Pq_idVxAnqWdw?pwd=mans) | | MAN-light| 840K | 47.1G | 38.18/0.9612 | 34.65/0.9292 | 32.50/0.8988 | [x2](https://pan.baidu.com/s/1AVuPa7bsbb3qMQqMSM-IJQ?pwd=mans)/[x3](https://pan.baidu.com/s/1TRL7-Y23JddVOpEhH0ObEQ?pwd=mans)/[x4](https://pan.baidu.com/s/1T2bPZcjFRxAgMxGWtPv-Lw?pwd=mans) | | MAN+ | 8712K | 495G | 38.44/0.9623 | 34.97/0.9315 | 32.87/0.9030 | [x2](https://pan.baidu.com/s/1pTb3Fob_7MOxMKIdopI0hQ?pwd=mans)/[x3](https://pan.baidu.com/s/1L3HEtcraU8Y9VY-HpCZdfg?pwd=mans)/[x4](https://pan.baidu.com/s/1FCNqht9zi9HecG3ExRdeWQ?pwd=mans) | 💖 Acknowledgments --- We would thank [VAN](https://github.com/Visual-Attention-Network/VAN-Classification) and [BasicSR](https://github.com/XPixelGroup/BasicSR) for their enlightening work! 🎓 Citation --- ``` @inproceedings{wang2024multi, title={Multi-scale Attention Network for Single Image Super-Resolution}, author={Wang, Yan and Li, Yusen and Wang, Gang and Liu, Xiaoguang}, booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops}, year={2024} } ``` or ``` @article{wang2022multi, title={Multi-scale Attention Network for Single Image Super-Resolution}, author={Wang, Yan and Li, Yusen and Wang, Gang and Liu, Xiaoguang}, journal={arXiv preprint arXiv:2209.14145}, year={2022} } ```