IQA-PyTorch

Health v2 · maintenance only

Flags: no_license

Adoption not part of the score

3380 stars · 252 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A pure Python/PyTorch toolbox for image quality assessment (IQA) providing GPU-accelerated reimplementations of many full-reference and no-reference metrics such as PSNR, SSIM, LPIPS, FID, NIQE, MUSIQ, TOPIQ, and NIMA. Results are calibrated against official MATLAB implementations, and it is installable via pip as `pyiqa`.

Use cases

• compute image quality metrics like psnr ssim lpips in python
• score images with no-reference quality models like musiq or nima
• evaluate generated images with fid
• benchmark image restoration models against quality metrics
• replace slow matlab iqa implementations with fast gpu pytorch versions
• assess aesthetic quality of photos
• measure perceptual image quality for dataset curation

When to choose

• you need many IQA metrics behind one consistent Python API
• you want GPU-accelerated metric computation in a PyTorch pipeline
• you need pretrained no-reference models like MUSIQ, TOPIQ, or NIMA without reimplementing them
• you want results calibrated against official MATLAB reference implementations

When to avoid

• you need a single trivial metric and want zero deep-learning dependencies
• you require strict license compliance and cannot accept a non-standard license
• you work outside Python/PyTorch ecosystems, e.g. pure MATLAB or TensorFlow

Facets

library · maturity active

image-processing machine-learning benchmarking sdk computer-vision image-processing machine-learning deep-learning python cross-platform image-quality-assessment pytorch iqa-metrics no-reference-metrics full-reference-metrics psnr ssim lpips fid musiq nima aesthetic-assessment pip-installable gpu

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