{"url":"/dataset/10000-people-human-pose-recognition-data","name":"10,000 People - Human Pose Recognition Data","full_name":"10,000 People - Human Pose Recognition Data","description_markdown":"Description:\r\n10,000 People - Human Pose Recognition Data. This dataset includes indoor and outdoor scenes.This dataset covers males and females. Age distribution ranges from teenager to the elderly, the middle-aged and young people are the majorities. The data diversity includes different shooting heights, different ages, different light conditions, different collecting environment, clothes in different seasons, multiple human poses. For each subject, the labels of gender, race, age, collecting environment and clothes were annotated. The data can be used for human pose recognition and other tasks.\r\n\r\nData size:\r\n10,000 people\r\n\r\nRace distribution:\r\nAsian (Chinese)","description_withheld":null,"homepage":"https://bit.ly/3QvpvYz","introduced_date":"2022-06-21","introduced_date_note":null,"introduced_by":null,"license":{"name":"Commercial license","url":"https://drive.google.com/file/d/1saDCPm74D4UWfBL17VbkTsZLGfpOQj1J/view?usp=sharing"},"modalities":[],"tasks":[{"name":"Object Detection","url":"/task/object-detection","datasets_with_task":"/datasets/task/object-detection"},{"name":"Pose Estimation","url":"/task/pose-estimation","datasets_with_task":"/datasets/task/pose-estimation"},{"name":"Pose Tracking","url":"/task/pose-tracking","datasets_with_task":"/datasets/task/pose-tracking"},{"name":"Contrastive Learning","url":"/task/contrastive-learning","datasets_with_task":"/datasets/task/contrastive-learning"}],"languages":[{"name":"Chinese","url":"/datasets/language/chinese"}],"variants":["10,000 People - Human Pose Recognition Data"],"data_loaders":[],"num_papers_in_archive":265,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/contrastive-learning-on-10000-people-human","task":"Contrastive Learning","dataset_variant":"10,000 People - Human Pose Recognition Data","rows":1,"metrics":["0..5sec"],"first_row_in_archive_order":{"model":"1","paper":"/paper/decisionnce-embodied-multimodal","metrics":{"0..5sec":"1"},"code_links":[{"title":"2toinf/DecisionNCE","url":"https://github.com/2toinf/DecisionNCE"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/decisionnce-embodied-multimodal","title":"DecisionNCE: Embodied Multimodal Representations via Implicit Preference Learning","date":"2024-02-28","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-25T09:33:49+00:00","samples_harvested":3,"samples_ran":3,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-25T09:33:49+00:00","papers_with_samples":1,"samples_harvested":3,"samples_ran":3,"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."}