Swin2SR - An AI model for image super-resolution and restoration, specializing in lossless image upscaling.

## Primary Use of Swin2SR Swin2SR is primarily used for image super-resolution and restoration, with a focus on lossless image upscaling and JPEG compression artifact removal. It excels in enhancing the resolution of compressed images and improving their overall quality. ## Technology Behind Swin2SR Swin2SR is based on Swin Transformer V2 technology, which improves upon the earlier SwinIR model. This architecture enhances training convergence speed and performance in super-resolution tasks. ## Academic Performance of Swin2SR Swin2SR was showcased at the 2022 ECCV AIM Workshop and ranked in the top five at the AIM 2022 Challenge on Compressed Image and Video Super-Resolution, demonstrating its reliability and competitiveness in both academic and practical applications. ## Key Features of Swin2SR Key features of Swin2SR include: - State-of-the-art performance in classic, lightweight, and real-world image super-resolution tasks. - Exceptional performance in JPEG compression artifact removal and compressed input super-resolution. - Faster training convergence compared to SwinIR, particularly in lightweight super-resolution tasks. - Training on datasets like DIV2K and Flickr2K, and testing on benchmarks such as RealSRSet, Classic5/Set5, Set14, BSD100, Urban100, and Manga109. ## Primary Use of Swin2SR Swin2SR offers the following functionalities: - Image super-resolution with up to 4x scaling, suitable for high-definition outputs. - Restoration of compressed images, improving their quality. - Removal of JPEG compression artifacts to enhance image clarity. ## Running Swin2SR Users can run Swin2SR in several ways: - On the Replicate platform, with each run costing approximately $0.020 (50 runs for $1), suitable for quick testing and small projects. - Locally using Docker, which requires downloading pre-trained models and related code. - Through online demos available on Kaggle, Google Colab, and Huggingface Spaces, allowing users to experience the model without a local setup. ## Hardware Requirements for Swin2SR Swin2SR supports Nvidia T4 GPU, with prediction times typically under 89 seconds, depending on the size and complexity of the input image. ## Pre-trained Models for Swin2SR Pre-trained models for Swin2SR can be downloaded from the GitHub repository's releases section at [GitHub](https://github.com/mv-lab/swin2sr/releases). ## Datasets for Swin2SR Swin2SR was trained on datasets including DIV2K and Flickr2K, and tested on benchmarks such as RealSRSet, Classic5/Set5, Set14, BSD100, Urban100, and Manga109. ## Additional Resources for Swin2SR Users can find more information about Swin2SR at the following URLs: - Replicate page: [Replicate](https://replicate.com/mv-lab/swin2sr) - GitHub repository: [GitHub](https://github.com/mv-lab/swin2sr) - Academic paper: [arXiv](https://arxiv.org/abs/2209.11345) (optional, for in-depth understanding) - Online demos: [Kaggle](https://www.kaggle.com/code/jesucristo/super-resolution-demo-swin2sr-official/), [Google Colab](https://colab.research.google.com/drive/1paPrt62ydwLv2U2eZqfcFsePI4X4WRR1?usp=sharing), [Huggingface Spaces](https://huggingface.co/spaces/jjourney1125/swin2sr) ### Citation sources: - [Swin2SR](https://replicate.com/mv-lab/swin2sr) - Official URL Updated: 2025-04-01