{"url":"/dataset/peta","name":"PETA","full_name":"Pedestrian Attribute","description_markdown":"The PEdesTrian Attribute dataset (**PETA**) is a dataset fore recognizing pedestrian attributes, such as gender and clothing style, at a far distance. It is of interest in video surveillance scenarios where face and body close-shots and hardly available. It consists of 19,000 pedestrian images with 65 attributes (61 binary and 4 multi-class). Those images contain 8705 persons.\r\n\r\nSource: [Attribute Aware Pooling for Pedestrian Attribute Recognition](https://arxiv.org/abs/1907.11837)\r\nImage Source: [http://mmlab.ie.cuhk.edu.hk/projects/PETA.html](http://mmlab.ie.cuhk.edu.hk/projects/PETA.html)","description_withheld":null,"homepage":"http://mmlab.ie.cuhk.edu.hk/projects/PETA.html","introduced_date":"2014-01-01","introduced_date_note":null,"introduced_by":{"paper":null,"title":"Pedestrian Attribute Recognition At Far Distance","first_author":null,"url":"https://doi.org/10.1145/2647868.2654966"},"license":{"name":"Custom (research-only, non-commercial)","url":"http://mmlab.ie.cuhk.edu.hk/projects/PETA.html"},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Pedestrian Attribute Recognition","url":"/task/pedestrian-attribute-recognition","datasets_with_task":"/datasets/task/pedestrian-attribute-recognition"}],"languages":[],"variants":["PETA"],"data_loaders":[],"num_papers_in_archive":74,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/pedestrian-attribute-recognition-on-peta","task":"Pedestrian Attribute Recognition","dataset_variant":"PETA","rows":6,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"UniHCP (FT)","paper":"/paper/unihcp-a-unified-model-for-human-centric","metrics":{"Accuracy":"88.78%"},"code_links":[{"title":"opengvlab/unihcp","url":"https://github.com/opengvlab/unihcp"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/c2t-net-channel-aware-cross-fused-transformer","title":"C2T-Net: Channel-Aware Cross-Fused Transformer-Style Networks for Pedestrian Attribute Recognition","date":"2023-12-26","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/unihcp-a-unified-model-for-human-centric","title":"UniHCP: A Unified Model for Human-Centric Perceptions","date":"2023-03-06","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":13,"samples_ran":7,"samples_unverified":6,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/rethinking-of-pedestrian-attribute","title":"Rethinking of Pedestrian Attribute Recognition: Realistic Datasets with Efficient Method","date":"2020-05-25","rows_on_this_dataset":2,"code_links":2,"syntology":null},{"paper":"/paper/improving-pedestrian-attribute-recognition","title":"Improving Pedestrian Attribute Recognition With Weakly-Supervised Multi-Scale Attribute-Specific Localization","date":"2019-10-10","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":1,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/hydraplus-net-attentive-deep-features-for","title":"HydraPlus-Net: Attentive Deep Features for Pedestrian Analysis","date":"2017-09-28","rows_on_this_dataset":1,"code_links":2,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":2,"samples_harvested":16,"samples_ran":8,"samples_unverified":8,"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."}