{"url":"/dataset/utkface","name":"UTKFace","full_name":null,"description_markdown":"The **UTKFace** dataset is a large-scale face dataset with long age span (range from 0 to 116 years old). The dataset consists of over 20,000 face images with annotations of age, gender, and ethnicity. The images cover large variation in pose, facial expression, illumination, occlusion, resolution, etc. This dataset could be used on a variety of tasks, e.g., face detection, age estimation, age progression/regression, landmark localization, etc.\r\n\r\nSource: [https://susanqq.github.io/UTKFace/](https://susanqq.github.io/UTKFace/)\r\nImage Source: [https://susanqq.github.io/UTKFace/](https://susanqq.github.io/UTKFace/)","description_withheld":null,"homepage":"https://susanqq.github.io/UTKFace/","introduced_date":"2017-01-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/age-progressionregression-by-conditional","title":"Age Progression/Regression by Conditional Adversarial Autoencoder","first_author":"Zhifei Zhang","url":null},"license":{"name":"Custom (research-only, non-commercial)","url":"https://susanqq.github.io/UTKFace/#:~:text=License%20Claim"},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Age Estimation","url":"/task/age-estimation","datasets_with_task":"/datasets/task/age-estimation"},{"name":"Multi-Task Learning","url":"/task/multi-task-learning","datasets_with_task":"/datasets/task/multi-task-learning"},{"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":"Age/Bias-conflicting","url":"/task/age-bias-conflicting","datasets_with_task":"/datasets/task/age-bias-conflicting"},{"name":"Race/Unbiased","url":"/task/race-unbiased","datasets_with_task":"/datasets/task/race-unbiased"},{"name":"Race/Bias-conflicting","url":"/task/race-bias-conflicting","datasets_with_task":"/datasets/task/race-bias-conflicting"},{"name":"Age/Unbiased","url":"/task/age-unbiased","datasets_with_task":"/datasets/task/age-unbiased"}],"languages":[],"variants":["UTKFace"],"data_loaders":[],"num_papers_in_archive":243,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/age-estimation-on-utkface","task":"Age Estimation","dataset_variant":"UTKFace","rows":14,"metrics":["MAE"],"first_row_in_archive_order":{"model":"MiVOLO-D1","paper":"/paper/mivolo-multi-input-transformer-for-age-and","metrics":{"MAE":"3.7"},"code_links":[{"title":"wildchlamydia/mivolo","url":"https://github.com/wildchlamydia/mivolo"},{"title":"DILiS-lab/drivers-of-predictive-aleatoric-uncertainty","url":"https://github.com/DILiS-lab/drivers-of-predictive-aleatoric-uncertainty"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/facial-attribute-classification-on-utkface","task":"Facial Attribute Classification","dataset_variant":"UTKFace","rows":1,"metrics":["Accuracy (%)"],"first_row_in_archive_order":{"model":"Neighbour Learning","paper":"/paper/deep-generative-views-to-mitigate-gender","metrics":{"Accuracy (%)":"94.76"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/fairness-on-utkface","task":"Fairness","dataset_variant":"UTKFace","rows":1,"metrics":["Degree of Bias (DoB)"],"first_row_in_archive_order":{"model":"Neighbour Learning","paper":"/paper/deep-generative-views-to-mitigate-gender","metrics":{"Degree of Bias (DoB)":"1.96"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/multi-task-learning-on-utkface","task":"Multi-Task Learning","dataset_variant":"UTKFace","rows":1,"metrics":["delta_m"],"first_row_in_archive_order":{"model":"BayesAgg-MTL","paper":"/paper/bayesian-uncertainty-for-gradient-aggregation","metrics":{"delta_m":"-2.23"},"code_links":[{"title":"ssi-research/bayesagg_mtl","url":"https://github.com/ssi-research/bayesagg_mtl"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/bayesian-uncertainty-for-gradient-aggregation","title":"Bayesian Uncertainty for Gradient Aggregation in Multi-Task Learning","date":"2024-02-06","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":8,"samples_ran":7,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"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/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":2,"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/deep-generative-views-to-mitigate-gender","title":"Deep Generative Views to Mitigate Gender Classification Bias Across Gender-Race Groups","date":"2022-08-17","rows_on_this_dataset":2,"code_links":0,"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":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":0,"samples_unverified":7,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/deep-ordinal-regression-with-label-diversity","title":"Deep Ordinal Regression with Label Diversity","date":"2020-06-29","rows_on_this_dataset":1,"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":4,"samples_harvested":21,"samples_ran":10,"samples_unverified":11,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":1,"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."}