{"url":"/dataset/afad","name":"AFAD","full_name":"Asian Face Age Dataset","description_markdown":"The Asian Face Age Dataset (AFAD) is a new dataset proposed for evaluating the performance of age estimation, which contains more than 160K facial images and the corresponding age and gender labels. This dataset is oriented to age estimation on Asian faces, so all the facial images are for Asian faces. It is noted that the AFAD is the biggest dataset for age estimation to date. It is well suited to evaluate how deep learning methods can be adopted for age estimation.","description_withheld":null,"homepage":"https://afad-dataset.github.io/","introduced_date":"2016-06-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/ordinal-regression-with-multiple-output-cnn","title":"Ordinal Regression With Multiple Output CNN for Age Estimation","first_author":"Zhenxing Niu","url":null},"license":null,"modalities":[],"tasks":[{"name":"Age Estimation","url":"/task/age-estimation","datasets_with_task":"/datasets/task/age-estimation"}],"languages":[],"variants":[],"data_loaders":[{"repo":"https://github.com/Chyoro/experimentc","url":"https://colab.research.google.com/drive/16xxsBELuxM7RzBuXyG3J7gTV-vEx9kAl","frameworks":["tf","pytorch"]}],"num_papers_in_archive":27,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/age-estimation-on-afad","task":"Age Estimation","dataset_variant":"AFAD","rows":10,"metrics":["MAE"],"first_row_in_archive_order":{"model":"CORAL","paper":"/paper/consistent-rank-logits-for-ordinal-regression","metrics":{"MAE":"3.48"},"code_links":[{"title":"Raschka-research-group/coral-cnn","url":"https://github.com/Raschka-research-group/coral-cnn"},{"title":"ck37/coral-ordinal","url":"https://github.com/ck37/coral-ordinal"},{"title":"axeber01/dold","url":"https://github.com/axeber01/dold"},{"title":"mshehrozsajjad/Age-Classification","url":"https://github.com/mshehrozsajjad/Age-Classification"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/unraveling-the-age-estimation-puzzle","title":"A Call to Reflect on Evaluation Practices for Age Estimation: Comparative Analysis of the State-of-the-Art and a Unified Benchmark","date":"2023-07-10","rows_on_this_dataset":9,"code_links":1,"syntology":null},{"paper":"/paper/consistent-rank-logits-for-ordinal-regression","title":"Rank consistent ordinal regression for neural networks with application to age estimation","date":"2019-01-20","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":1,"samples_unverified":3,"pointer_only_for_licence":0,"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":1,"samples_harvested":4,"samples_ran":1,"samples_unverified":3,"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."}