richzhang/PerceptualSimilarity

Health v2 · maintenance only

Adoption not part of the score

4269 stars · 523 forks observed · 2026-08-28

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

A PyTorch library implementing the LPIPS (Learned Perceptual Image Patch Similarity) metric, which measures perceptual distance between images using deep features, usable both as an evaluation metric and as a perceptual loss. It also includes the BAPPS dataset of human perceptual similarity judgments.

Use cases

• compute perceptual similarity between two images
• evaluate image generation or super-resolution quality
• use LPIPS as a perceptual loss for training image synthesis models
• compare all image pairs across two directories
• benchmark perceptual metrics against human judgments
• measure how similar two image patches look to humans

When to choose

• you need a standard perceptual image similarity metric in PyTorch
• you want a differentiable perceptual loss for image generation training
• you are evaluating GANs, super-resolution, or image restoration outputs
• you need human-aligned image distance instead of PSNR or SSIM

When to avoid

• you need a non-PyTorch framework (a separate TensorFlow port exists)
• you only need simple pixel-wise metrics like PSNR or SSIM
• you cannot use GPU or pretrained deep networks
• you need video or audio similarity rather than images

Facets

library · maturity stable

image-processing machine-learning deep-learning benchmarking computer-vision image-processing deep-learning machine-learning python cross-platform lpips perceptual-similarity perceptual-loss pytorch image-quality bapps-dataset cvpr-2018 gpu

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