SwinIR
/All repositories
Overview
HEALTH SCORE
Critical*
Partial score (2/3 categories)
19out of 65
Development cadence is the primary crisis signal. Maintainer coverage shows little to offset it. Every health signal is at or near zero. Security & Supply Chain is unavailable, so weights were redistributed.
LIFECYCLE
Active
Consistent commits, responsive maintainers, regular releases, and healthy issue triage.
Health breakdown
Critical*(19/65)
Maintainer availability, known risks and supply chain posture, and whether the project is actively developed or appropriately stable.
Maintainer responsiveness
No PRs or issues were opened in the review window, so response time could not be measured.
Bus factor
No data available. Repository has not been indexed for bus factor.
Organization Diversity
Contributors span 29 organizations.
Project Achievements
Leaderboards
About
SwinIR is a deep learning-based image restoration model that uses the Swin Transformer architecture for tasks like image super-resolution, denoising, and compression artifact reduction. It achieves state-of-the-art performance in various image restoration tasks by leveraging shifted window-based self-attention mechanisms.
First commit
Software value
$46K
Tags
compression-artifact-reduction
deblocking
decompression
denoising
image-deblocking
image-denoising
image-restoration
image-sr
image-super-resolution
lightweight-image-super-resolution
low-level-vision
real-world-image-super-resolution
restoration
super-resolution
transformer
vision-transformer
License(s)
Apache-2.0
Data sources
Git
GitHub