{"url":"/dataset/rap","name":"RAP","full_name":"Richly Annotated Pedestrian","description_markdown":"The **Richly Annotated Pedestrian** (**RAP**) dataset is a dataset for pedestrian attribute recognition. It contains 41,585 images collected from indoor surveillance cameras. Each image is annotated with 72 attributes, while only 51 binary attributes with the positive ratio above 1% are selected for evaluation. There are 33,268 images for the training set and 8,317 for testing.\r\n\r\nSource: [Localization Guided Learning for Pedestrian Attribute Recognition](https://arxiv.org/abs/1808.09102)\r\nImage Source: [http://www.rapdataset.com/rapv1.html](http://www.rapdataset.com/rapv1.html)","description_withheld":null,"homepage":"http://www.rapdataset.com/","introduced_date":"2016-01-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/a-richly-annotated-dataset-for-pedestrian","title":"A Richly Annotated Dataset for Pedestrian Attribute Recognition","first_author":"Dangwei Li","url":null},"license":{"name":"Custom (research-only, non-commercial)","url":"http://www.rapdataset.com/"},"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":["RAP"],"data_loaders":[],"num_papers_in_archive":36,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/pedestrian-attribute-recognition-on-rap","task":"Pedestrian Attribute Recognition","dataset_variant":"RAP","rows":2,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"Attribute-Specific Localization","paper":"/paper/improving-pedestrian-attribute-recognition","metrics":{"Accuracy":"68.17%"},"code_links":[{"title":"chufengt/alm-pedestrian-attribute","url":"https://github.com/chufengt/alm-pedestrian-attribute"},{"title":"chufengt/iccv19_attribute","url":"https://github.com/chufengt/iccv19_attribute"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"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":1,"samples_harvested":3,"samples_ran":1,"samples_unverified":2,"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."}