{"categories":[{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/5"}],"enrichment":{"capability":"Computes Fr\u00e9chet Inception Distance (FID) and Kernel Inception Distance (KID) scores for evaluating generative models, with corrected image resizing and quantization to ensure consistent results across implementations.","skillfed_tags":["gan-evaluation","image-metrics","generative-models"],"use_cases":["Evaluate a trained GAN or diffusion model against standard benchmarks like FFHQ or CIFAR-10 using precomputed statistics.","Compare FID scores across different generative models or training runs with consistent resizing and quantization.","Compute KID scores for few-shot generation tasks on smaller datasets like AFHQ or BreCaHAD.","Debug image generation quality by computing FID between generated and real image folders with controlled resizing methods."],"what_it_does":"Clean-fid is a library for computing FID and KID metrics used to evaluate the quality of images generated by generative models like GANs. The core problem it solves is that different implementations of FID use different image resizing and quantization methods, leading to inconsistent scores across papers and groups. This library standardizes those operations\u2014particularly addressing aliasing issues in resizing functions and JPEG compression effects\u2014so that FID scores become comparable.\n\nThe package wraps PyTorch and provides a simple API to compute FID between two image folders, between a folder and precomputed dataset statistics (for datasets like CIFAR-10, FFHQ, and LSUN), or between a generative function and precomputed statistics. It also supports KID computation and CLIP-based FID variants. Its main dependencies are torch, torchvision, numpy, scipy, pillow, requests, and tqdm.","worth_installing":"Yes, if you are actively evaluating generative models and need reproducible FID/KID scores. The library solves a real standardization problem in GAN evaluation. However, consider that maintenance is aging (last release December 2022); verify compatibility with your current PyTorch version before relying on it for new research, and monitor the repository for updates or consider alternatives if critical bugs emerge."},"id":"clean-fid","links":{"html":"https://skillfed.io/packages/clean-fid","md":"https://skillfed.io/packages/clean-fid.md","pypi":"https://pypi.org/project/clean-fid/"},"maintenance":{"status":"aging"},"meta":{"latest_release":"2022-12-18","license_spdx":null,"license_treatment":"permissive","name":"clean-fid","python_support":"unspecified","summary":"FID calculation in PyTorch with proper image resizing and quantization steps"},"popularity":{"monthly_downloads":406980,"position":6891,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"0.1.35"}