yulunzhang/RCAN

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

1503 stars · 318 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

PyTorch implementation of RCAN, a very deep residual channel attention network for single image super-resolution from an ECCV 2018 paper. It provides training and testing code built on the ESR-PyTorch codebase, plus pretrained visual results for scales 2, 3, 4, and 8.

Use cases

• upscale low-resolution images with a deep learning model
• train a super-resolution network on DIV2K
• reproduce RCAN PSNR/SSIM benchmark results
• compare channel attention architectures for image restoration
• get pretrained super-resolution models for scales 2-8

When to choose

• you need a well-cited reference implementation of channel attention for super-resolution
• you want to reproduce ECCV 2018 paper results or build on RCAN architecture
• you have a CUDA GPU and want to train or test SR models in PyTorch

When to avoid

• you need a maintained production library with a license and active support
• you want a simple CLI upscaler without setting up training data and PyTorch 0.4-era dependencies
• you need super-resolution on CPU or in a browser

Facets

library · maturity maintenance

machine-learning image-processing deep-learning computer-vision image-processing deep-learning python super-resolution pytorch channel-attention eccv-2018 research-code linux gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="yulunzhang/RCAN")