{"url":"/dataset/agiqa-3k","name":"AGIQA-3K","full_name":null,"description_markdown":"The AGIQA-3K is a fine-grained AI-generated image (AGI) subjective quality assessment database. It was created to address the need for quality models that are consistent with human subjective ratings, considering the large quality variance among different AGIs. The database extensively considers various popular AGI models, generates AGI through different prompts and model parameters, and collects subjective scores at the perceptual quality and text-to-image alignment level.","description_withheld":null,"homepage":"https://github.com/lcysyzxdxc/AGIQA-3k-Database","introduced_date":"2023-06-07","introduced_date_note":null,"introduced_by":{"paper":"/paper/agiqa-3k-an-open-database-for-ai-generated","title":"AGIQA-3K: An Open Database for AI-Generated Image Quality Assessment","first_author":"Chunyi Li","url":null},"license":null,"modalities":[],"tasks":[],"languages":[],"variants":["AGIQA-3K"],"data_loaders":[],"num_papers_in_archive":31,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}