{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/4"},{"label":"Image Processing","url":"https://skillfed.io/packages/category/scientific-engineering-image-processing"}],"enrichment":{"capability":"PyIQA provides a PyTorch-based toolbox for computing image quality assessment metrics, supporting both full-reference and no-reference methods with GPU acceleration and calibration against official implementations.","skillfed_tags":["image-quality-assessment","pytorch-toolbox","perceptual-metrics"],"use_cases":["Evaluate perceptual quality of generated or compressed images in research or benchmarking workflows.","Use as a differentiable loss function to train image generation or enhancement models.","Batch-score large image collections against reference images using GPU acceleration.","Compare image quality across multiple metrics (FR and NR) in a unified interface.","Load and evaluate models on standard IQA datasets with built-in train/test splitting."],"what_it_does":"PyIQA is a PyTorch-based image quality assessment toolbox that reimplements widely-used full-reference (FR) and no-reference (NR) IQA metrics with results calibrated against official MATLAB implementations. It provides GPU-accelerated computation, making these metrics substantially faster than their MATLAB counterparts. The package includes modern metrics like LPIPS, BRISQUE, SSIM variants, and recent additions like Q-ReAlign and MACLIP, plus support for FID and other generative-model evaluation metrics.\n\nYou can use it as a command-line tool to score images, as a Python library to compute metrics on tensors or image paths, or as a loss function for training neural networks (with gradient support for compatible metrics). It also includes utilities to load popular IQA datasets (KONIQ-10k, CSIQ, etc.) from Hugging Face with automatic downloading and splitting.","worth_installing":"Yes, with conditions. Install if you need image quality metrics for research or non-commercial work and have GPU resources available. The noncommercial license is a hard blocker for any commercial use. The heavy dependency tree (torch, transformers, vision libraries) means substantial first-install time, but low friction once resolved. Active maintenance and no known vulnerabilities are positive signals. Not suitable if you need commercial licensing or minimal dependencies."},"id":"pyiqa","links":{"html":"https://skillfed.io/packages/pyiqa","md":"https://skillfed.io/packages/pyiqa.md","pypi":"https://pypi.org/project/pyiqa/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-07-08","license_spdx":"PolyForm-Noncommercial-1.0.0","license_treatment":"noncommercial","name":"pyiqa","python_support":"supports_current","summary":"PyTorch Toolbox for Image Quality Assessment"},"popularity":{"monthly_downloads":464346,"position":6517,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.1.16"}