{"url":"/dataset/pa-100k","name":"PA-100K","full_name":"PA-100K Dataset","description_markdown":"**PA-100K** is a recent-proposed large pedestrian attribute dataset, with 100,000 images in total collected from outdoor surveillance cameras. It is split into 80,000 images for the training set, and 10,000 for the validation set and 10,000 for the test set. This dataset is labeled by 26 binary attributes. The common features existing in both selected dataset is that the images are blurry due to the relatively low resolution and the positive ratio of each binary attribute is low.\r\n\r\nSource: [Localization Guided Learning for Pedestrian Attribute Recognition](https://arxiv.org/abs/1808.09102)\r\nImage Source: [https://github.com/xh-liu/HydraPlus-Net](https://github.com/xh-liu/HydraPlus-Net)","description_withheld":null,"homepage":"https://github.com/xh-liu/HydraPlus-Net","introduced_date":"2017-01-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/hydraplus-net-attentive-deep-features-for","title":"HydraPlus-Net: Attentive Deep Features for Pedestrian Analysis","first_author":"Xihui Liu","url":null},"license":{"name":"Unknown","url":null},"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":[{"name":"English","url":"/datasets/language/english"},{"name":"Chinese","url":"/datasets/language/chinese"}],"variants":["PA-100K"],"data_loaders":[{"repo":"https://github.com/xh-liu/HydraPlus-Net","url":"https://github.com/xh-liu/HydraPlus-Net","frameworks":[]}],"num_papers_in_archive":56,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/pedestrian-attribute-recognition-on-pa-100k","task":"Pedestrian Attribute Recognition","dataset_variant":"PA-100K","rows":13,"metrics":["Accuracy","Accuracy ","F1 score"],"first_row_in_archive_order":{"model":"PATH (Partial FT)","paper":"/paper/humanbench-towards-general-human-centric","metrics":{"Accuracy":"90.8"},"code_links":[{"title":"opengvlab/humanbench","url":"https://github.com/opengvlab/humanbench"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/attrivision-advancing-generalization-in","title":"AttriVision: Advancing Generalization in Pedestrian Attribute Recognition using CLIP","date":"2025-02-18","rows_on_this_dataset":2,"code_links":0,"syntology":null},{"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/hulk-a-universal-knowledge-translator-for","title":"Hulk: A Universal Knowledge Translator for Human-Centric Tasks","date":"2023-12-04","rows_on_this_dataset":2,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":24,"samples_ran":12,"samples_unverified":12,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/towards-unified-text-based-person-retrieval-a","title":"Towards Unified Text-based Person Retrieval: A Large-scale Multi-Attribute and Language Search Benchmark","date":"2023-06-05","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":12,"samples_ran":6,"samples_unverified":6,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/beyond-appearance-a-semantic-controllable","title":"Beyond Appearance: a Semantic Controllable Self-Supervised Learning Framework for Human-Centric Visual Tasks","date":"2023-03-30","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":1,"samples_unverified":1,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/humanbench-towards-general-human-centric","title":"HumanBench: Towards General Human-centric Perception with Projector Assisted Pretraining","date":"2023-03-10","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":25,"samples_ran":15,"samples_unverified":10,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"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/label2label-a-language-modeling-framework-for","title":"Label2Label: A Language Modeling Framework for Multi-Attribute Learning","date":"2022-07-18","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":2,"samples_unverified":3,"pointer_only_for_licence":5,"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":1,"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":7,"samples_harvested":84,"samples_ran":44,"samples_unverified":40,"pointer_only_for_licence":6,"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."}