{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/7"}],"enrichment":{"capability":"PIQ provides a collection of image quality metrics and measures\u2014both full-reference (comparing two images) and no-reference (assessing a single image)\u2014implemented as PyTorch modules that can be used as loss functions or evaluated independently.","skillfed_tags":["image-quality","loss-function","metrics"],"use_cases":["Training image generation or enhancement models using perceptual loss functions like LPIPS or SSIM.","Evaluating generative model quality with FID or Inception Score on image distributions.","Comparing image restoration or super-resolution outputs against reference images.","Assessing blind image quality using BRISQUE or CLIP-IQA on single images.","Benchmarking image processing pipelines against standard quality metrics on common datasets."],"what_it_does":"PIQ is a library that bundles image quality metrics into a unified, easy-to-use interface. It implements full-reference metrics (SSIM, PSNR, LPIPS, etc.) that compare pairs of images, no-reference metrics (BRISQUE, CLIP-IQA) that assess single images, and distribution-based metrics (FID, IS, KID) that compare feature distributions from image sets. Most metrics can be backpropagated, making them suitable as loss functions for model training.\n\nThe library is built on pure PyTorch with minimal additional dependencies, provides extensive input validation to prevent crashes during training, and supports GPU computation. It evolved from an earlier package called PhotoSynthesis.Metrics and includes code for benchmarking metrics against standard datasets like TID2013 and KADID10k.","worth_installing":"Yes, if you need image quality metrics. The library is well-established (1572 GitHub stars, 256419 monthly downloads), permissively licensed, and has low install friction. The dormant maintenance status is not a blocker for stable metric implementations, but verify that the specific metrics you need are present and that you do not require active bug fixes or new features. No known vulnerabilities."},"id":"piq","links":{"html":"https://skillfed.io/packages/piq","md":"https://skillfed.io/packages/piq.md","pypi":"https://pypi.org/project/piq/"},"maintenance":{"status":"dormant"},"meta":{"latest_release":"2023-07-04","license_spdx":null,"license_treatment":"permissive","name":"piq","python_support":"unspecified","summary":"Measures and metrics for image2image tasks. PyTorch."},"popularity":{"monthly_downloads":256419,"position":8465,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.8.0"}