VainF/pytorch-msssim

Health v2 · maintenance only

Adoption not part of the score

1253 stars · 127 forks observed · 2026-08-28

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

A PyTorch library providing fast, differentiable SSIM and MS-SSIM image quality metrics using separable Gaussian filtering for speed. It can be used both as an evaluation metric and as a differentiable loss function for training neural networks.

Use cases

• compute SSIM between image batches in pytorch
• use MS-SSIM as a differentiable loss for image reconstruction training
• evaluate image quality of generated images against ground truth
• match SSIM results from tensorflow or skimage in pytorch
• speed up SSIM computation with separable Gaussian kernels
• compute SSIM on 3D volumetric images

When to choose

• you need differentiable SSIM/MS-SSIM losses inside PyTorch training loops
• you want fast GPU-accelerated SSIM computation on batches of images
• you need results consistent with TensorFlow or skimage implementations

When to avoid

• you need image quality metrics outside the PyTorch ecosystem
• you need other perceptual metrics like LPIPS or PSNR beyond SSIM
• your project uses TensorFlow, JAX, or NumPy instead of PyTorch

Facets

library · maturity stable

image-processing machine-learning benchmarking computer-vision image-processing machine-learning deep-learning python cross-platform ssim ms-ssim pytorch image-quality differentiable-loss perceptual-metrics gpu

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