{"url":"/dataset/lagenda","name":"LAGENDA","full_name":"Layer Age and Gender Dataset","description_markdown":"The LAGENDA dataset is a large-scale dataset with age and gender annotations for face and body bounding boxes. The dataset consists of 67,159 images from the Open Images Dataset and comprises 84,192 pairs (FaceCrop, BodyCrop). This dataset offers a high level of diversity, encompassing various scenes and domains. It contains minimal celebrity data, thus reflecting real-world, in-the-wild scenarios. The dataset spans a wide age range, from 0 to 95 years old.","description_withheld":null,"homepage":"https://wildchlamydia.github.io/lagenda/","introduced_date":"2023-07-10","introduced_date_note":null,"introduced_by":{"paper":"/paper/mivolo-multi-input-transformer-for-age-and","title":"MiVOLO: Multi-input Transformer for Age and Gender Estimation","first_author":"Maksim Kuprashevich","url":null},"license":{"name":"CC 2.0","url":"https://creativecommons.org/licenses/by/2.0/"},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Age Estimation","url":"/task/age-estimation","datasets_with_task":"/datasets/task/age-estimation"},{"name":"Age And Gender Classification","url":"/task/age-and-gender-classification","datasets_with_task":"/datasets/task/age-and-gender-classification"},{"name":"Gender Prediction","url":"/task/gender-prediction","datasets_with_task":"/datasets/task/gender-prediction"},{"name":"Age and Gender Estimation","url":"/task/age-and-gender-estimation","datasets_with_task":"/datasets/task/age-and-gender-estimation"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["LAGENDA","LAGENDA age","LAGENDA gender"],"data_loaders":[{"repo":"https://github.com/JavohirJalilov/age-gender","url":"https://github.com/JavohirJalilov/age-gender","frameworks":["pytorch"]}],"num_papers_in_archive":3,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/age-and-gender-estimation-on-lagenda-age","task":"Age and Gender Estimation","dataset_variant":"LAGENDA age","rows":2,"metrics":["CS@5","MAE"],"first_row_in_archive_order":{"model":"MiVOLO-V2","paper":"/paper/beyond-specialization-assessing-the-1","metrics":{"CS@5":"74.48","MAE":"3.65"},"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"},{"leaderboard":"/sota/age-and-gender-estimation-on-lagenda-gender","task":"Age and Gender Estimation","dataset_variant":"LAGENDA gender","rows":2,"metrics":["Accuracy","CS@5"],"first_row_in_archive_order":{"model":"MiVOLO-D1","paper":"/paper/mivolo-multi-input-transformer-for-age-and","metrics":{"Accuracy":"97.36"},"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/age-estimation-on-lagenda","task":"Age Estimation","dataset_variant":"LAGENDA","rows":2,"metrics":["MAE"],"first_row_in_archive_order":{"model":"MiVOLO-V2","paper":"/paper/beyond-specialization-assessing-the-1","metrics":{"MAE":"3.65"},"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"},{"leaderboard":"/sota/gender-prediction-on-lagenda","task":"Gender Prediction","dataset_variant":"LAGENDA","rows":2,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"MiVOLO-V2","paper":"/paper/beyond-specialization-assessing-the-1","metrics":{"Accuracy":"97.99"},"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":4,"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":4,"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."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":2,"samples_ran":2,"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."}