photosynthesis-team/piq

Health v2 · maintenance only

Adoption not part of the score

1574 stars · 123 forks observed · 2026-08-28

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

PyTorch Image Quality (PIQ) is a collection of measures and metrics for image quality assessment in image-to-image tasks, written in pure PyTorch. It provides a unified interface for metrics like SSIM, PSNR, FID, and BRISQUE, many of which can be used as differentiable loss functions.

Use cases

• evaluate image quality of generated images
• compute SSIM or PSNR between two images in PyTorch
• use image quality metrics as loss functions for training
• measure FID or KID for GAN evaluation
• assess quality of super-resolution or denoising outputs
• benchmark image-to-image model performance on GPU

When to choose

• you need a broad set of image quality metrics with a unified PyTorch API
• you want differentiable metrics usable as training losses
• you need GPU-accelerated IQA computation with input validation

When to avoid

• you need metrics for TensorFlow, JAX, or non-PyTorch frameworks
• you only need a single simple metric and prefer a minimal dependency
• your project requires Python beyond the supported 3.7-3.10 range

Facets

library · maturity stable

image-processing machine-learning benchmarking testing image-processing machine-learning computer-vision deep-learning python cross-platform pytorch image-quality-assessment iqa ssim psnr fid kid brisque gan-metrics loss-functions gpu

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