{"url":"/dataset/fairface","name":"FairFace","full_name":null,"description_markdown":"**FairFace** is a face image dataset which is race balanced. It contains 108,501 images from 7 different race groups: White, Black, Indian, East Asian, Southeast Asian, Middle Eastern, and Latino. Images were collected from the YFCC-100M Flickr dataset and labeled with race, gender, and age groups.\r\n\r\nSource: [https://github.com/joojs/fairface](https://github.com/joojs/fairface)","description_withheld":null,"homepage":"https://github.com/joojs/fairface","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/fairface-face-attribute-dataset-for-balanced","title":"FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age","first_author":"Kimmo Kärkkäinen","url":null},"license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Fairness","url":"/task/fairness","datasets_with_task":"/datasets/task/fairness"},{"name":"Facial Attribute Classification","url":"/task/facial-attribute-classification","datasets_with_task":"/datasets/task/facial-attribute-classification"},{"name":"Decision Making","url":"/task/decision-making","datasets_with_task":"/datasets/task/decision-making"}],"languages":[],"variants":["FairFace"],"data_loaders":[{"repo":"https://github.com/huggingface/datasets","url":"https://huggingface.co/datasets/nateraw/fairface","frameworks":["tf","pytorch","jax"]},{"repo":"https://github.com/joojs/fairface","url":"https://github.com/joojs/fairface","frameworks":[]}],"num_papers_in_archive":208,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/facial-attribute-classification-on-fairface","task":"Facial Attribute Classification","dataset_variant":"FairFace","rows":3,"metrics":["gender-top1","race-top1","age-top1"],"first_row_in_archive_order":{"model":"MiVOLO-V2","paper":"/paper/beyond-specialization-assessing-the-1","metrics":{"age-top1":"62.28","gender-top1":"97.5"},"code_links":[{"title":"wildchlamydia/mivolo","url":"https://github.com/wildchlamydia/mivolo"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/beyond-specialization-assessing-the-1","title":"Beyond Specialization: Assessing the Capabilities of MLLMs in Age and Gender Estimation","date":"2024-03-04","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/mivolo-multi-input-transformer-for-age-and","title":"MiVOLO: Multi-input Transformer for Age and Gender Estimation","date":"2023-07-10","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":2,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/fairface-face-attribute-dataset-for-balanced","title":"FairFace: Face Attribute Dataset for Balanced Race, Gender, and Age","date":"2019-08-14","rows_on_this_dataset":1,"code_links":7,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":2,"samples_harvested":3,"samples_ran":3,"samples_unverified":0,"pointer_only_for_licence":1,"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."}