{"url":"/dataset/agiqa-1k","name":"AGIQA-1K","full_name":"AGIQA-1K","description_markdown":"AI Generated Content (AIGC) refers to any form of content, such as text, images, audio, or video, that is created with the help of artificial intelligence technology. With the flourishing development of deep learning, the efficiency of AIGC generation has increased, and AI-Generated Image (AGI) is becoming more prevalent in areas such as culture, entertainment, education, social media, etc.\r\n\r\nUnlike Natural Scene Images (NSIs) captured from natural scenes, AGIs are directly generated from AI models. Thus, AGIs obtain some unique quality characteristics and viewers tend to evaluate the quality of AGIs from some different aspects of NSIs.\r\n\r\nTherefore, we propose the first perceptual AGI Quality Assessment (AGIQA-1K) database, which provides 1,080 AGIs along with quality labels, including technical issues, AI artifacts, unnaturalness, discrepancy, and aesthetics as major evaluation aspects.","description_withheld":null,"homepage":"https://github.com/lcysyzxdxc/AGIQA-1k-Database","introduced_date":"2023-03-30","introduced_date_note":null,"introduced_by":null,"license":{"name":"MIT","url":null},"modalities":[],"tasks":[],"languages":[],"variants":["AGIQA-1K"],"data_loaders":[],"num_papers_in_archive":1,"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."}