{"url":"/dataset/cacd","name":"CACD","full_name":"Cross-Age Celebrity Dataset","description_markdown":"The **Cross-Age Celebrity Dataset** (**CACD**) contains 163,446 images from 2,000 celebrities collected from the Internet. The images are collected from search engines using celebrity name and year (2004-2013) as keywords. Therefore, it is possible to estimate the ages of the celebrities on the images by simply subtract the birth year from the year of which the photo was taken.\r\n\r\nSource: [https://bcsiriuschen.github.io/CARC/](https://bcsiriuschen.github.io/CARC/)\r\nImage Source: [https://www.pkuml.org/resources/pku-vehicleid.html](https://www.pkuml.org/resources/pku-vehicleid.html)","description_withheld":null,"homepage":"https://bcsiriuschen.github.io/CARC/","introduced_date":"2014-01-01","introduced_date_note":null,"introduced_by":{"paper":null,"title":"Cross-Age Reference Coding for Age-Invariant Face Recognition and Retrieval","first_author":null,"url":"https://doi.org/10.1007/978-3-319-10599-4_49"},"license":{"name":"Custom (research-only)","url":"https://bcsiriuschen.github.io/CARC/"},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Age Estimation","url":"/task/age-estimation","datasets_with_task":"/datasets/task/age-estimation"}],"languages":[],"variants":["CACD"],"data_loaders":[{"repo":"https://github.com/activeloopai/Hub","url":"https://docs.activeloop.ai/datasets/cacd-dataset","frameworks":["tf","pytorch"]}],"num_papers_in_archive":69,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/age-estimation-on-cacd","task":"Age Estimation","dataset_variant":"CACD","rows":13,"metrics":["MAE"],"first_row_in_archive_order":{"model":"MiVOLO-V2","paper":"/paper/beyond-specialization-assessing-the-1","metrics":{"MAE":"3.89"},"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/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/moving-window-regression-a-novel-approach-to","title":"Moving Window Regression: A Novel Approach to Ordinal Regression","date":"2022-03-24","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":7,"samples_ran":3,"samples_unverified":4,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/facial-age-estimation-by-deep-residual","title":"Facial age estimation by deep residual decision making","date":"2019-08-28","rows_on_this_dataset":1,"code_links":2,"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":"2026-09-25T09:33:49+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":"2026-09-25T09:33:49+00:00","papers_with_samples":2,"samples_harvested":11,"samples_ran":4,"samples_unverified":7,"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."}