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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
neosr is an open-source framework for training super-resolution models.
| Date | Stars |
|---|---|
| 2026-07-24 | 321 |
| 2026-07-25 | 321 |
| 2026-07-28 | 321 |
| 2026-07-30 | 321 |
| 2026-08-06 | 321 |
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<div align="center"> <img src="https://github.com/neosr-project/neosr/assets/132400428/54e8f7fa-8705-4ea3-8b6e-c6227117044d?sanitize=true" width="480"></img> <a href="https://discord.gg/NN2HGtJ3d6"><img src="https://github.com/neosr-project/neosr/assets/132400428/4bd54b1d-4639-4940-b9c7-b3f212aea5c8?sanitize=true" width="100"></img></a><br> Join our <a href="https://discord.gg/NN2HGtJ3d6">Discord</a> </div> [neosr](https://github.com/neosr-project/neosr) is an open-source framework for training super-resolution models. It provides a comprehensive and reproducible environment for achieving state-of-the-art image restoration results, making it suitable for both the enthusiastic community, professionals and machine learning academic researchers. It serves as a versatile platform and aims to bridge the gap between practical application and academic research in the field. - **Accessible**: implements a wide range of the latest advancements in single-image super-resolution networks, losses, optimizers and augmentations. Users can easily explore, adapt and experiment with various configurations for their specific needs, even without coding skills. - **Efficient**: optimized for faster training iterations, quicker convergence and low GPU requirements, making it the most efficient choice for both research and practical use cases. - **Practical**: focuses on the real-world use of super-resolution to realistically restore degraded images in various domains, including photos, anime/cartoons, illustrations and more. It's also suitable for critical applications like medical imaging, forensics, geospatial and others (although caution should be taken in those cases). - **Reproducible**: this framework emphasizes the importance of reproducible research. It provides deterministic training environments that can create bit-exact reproducible models (on the same platform), ensuring predictable and reliable results, which are essential for maintaining consistency in academic validation. - **Simple**: features are easy to implement or modify. Code is written in readable Python, no fancy styling. All code is managed, validated and formatted by [`uv`](https://docs.astral.sh/uv/), [`ruff`](https://docs.astral.sh/ruff/), [`mypy`](https://www.mypy-lang.org/) and [`torchfix`](https://github.com/pytorch-labs/torchfix). For more information see our [wiki](https://github.com/neosr-project/neosr/wiki). ## 🤝 support the project > [!TIP] > Consider supporting the project on [**KoFi**](https://ko-fi.com/neosr) ☕ or [**Patreon**](https://www.patreon.com/neosr) ## 💻 installation Requires CUDA >=12.4 **Windows**: ```ps1 powershell -ExecutionPolicy ByPass -c "irm https://raw.githubusercontent.com/neosr-project/neosr/refs/heads/master/install_windows.ps1 | iex" ``` **Linux**: ```bash curl -LsSf https://raw.githubusercontent.com/neosr-project/neosr/refs/heads/master/install_linux.sh | sh ``` Note: `neosr` will be installed on your terminal current path. To update neosr, run: ```bash neosr-update ``` For manual installation details, see our [Installation Instructions](https://github.com/neosr-project/neosr/wiki/Installation-Instructions) wiki. ## ⏩ quick start Start training by running: ```bash neosr-train config.toml ``` Where `config.toml` is a configuration file. Templates can be found in [options](options/). > [!TIP] > Please read the wiki [Configuration Walkthrough](https://github.com/neosr-project/neosr/wiki/Configuration-Walkthrough) for an explanation of each option. ## ✨ features ### [supported archs](https://github.com/neosr-project/neosr/wiki/Arch%E2%80%90specific-options): | arch | option | |---------------------------------------------------------------------------------------------------|-----------------------------------------------------------| | [Real-ESRGAN](https://github.com/xinntao/Real-ESRGAN
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Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:3db736796b6bacd5, topic:super-resolution, desc:super resolution, readme:super resolution