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Pedestrian Attribute Recognition datasets

archive 2025-07-28

9 datasets carry the task tag "Pedestrian Attribute Recognition" (the task itself: Pedestrian Attribute Recognition), ordered by the archive's paper count. Page 1 of 1: 9 shown of 9. Facet routes are this site's own (the archive records the tag string, not a page).

The archive holds 12,214 dataset rows; 12,172 are listed. 6 are withheld from every listing and count here as vandalised before snapshot (6 with contact-centre spam in the title, 0 with a spam description on a row that has no homepage, no paper and no papers counted; none with more than 1 paper, 0 with a benchmark), listed in withheld.json; 1 listed row carries a vandalised description, withheld on its page. This gate never withholds a row with a homepage or a paper that resolves, and a clean description; the content rules below withhold a row whose name is spam whatever else it carries. The gate is a phrase list: these are the rows it caught, not a claim that the rest is clean. Before that gate, the site's content rules withhold 36 more rows (invite-code, gambling, travel-booking, contact-centre and similar spam in the name or on a row with nothing real behind it); they have no page and are listed in withheld.json.

Filter 51 task tags shown of 3,717, by dataset count; the full filter by modality, task and language is on /datasets

Pedestrian Attribute Recognition datasets 1–9 of 9

PETA (Pedestrian Attribute)
The PEdesTrian Attribute dataset (PETA) is a dataset fore recognizing pedestrian attributes, such as gender and clothing style, at a far distance.
74 papers · 1 benchmark
PA-100K (PA-100K Dataset)
PA-100K is a recent-proposed large pedestrian attribute dataset, with 100,000 images in total collected from outdoor surveillance cameras.
56 papers · 1 benchmark
UAV-Human is a large dataset for human behavior understanding with UAVs.
47 papers · 5 benchmarks
RAP (Richly Annotated Pedestrian)
The Richly Annotated Pedestrian (RAP) dataset is a dataset for pedestrian attribute recognition.
36 papers · 1 benchmark
UPAR (Unified Pedestrian Attribute Recognition)
The Task: The challenge will use an extension of the UPAR Dataset [1], which consists of images of pedestrians annotated for 40 binary attributes.
7 papers · 1 benchmark
The Market1501-Attributes dataset is built from the Market1501 dataset.
5 papers · 1 benchmark
The images in DukeMTMC-attribute dataset comes from Duke University.
4 papers · 1 benchmark
CAR (Cityscapes Attributes Recognition)
CAR contains visual attributes for objects in the Cityscapes dataset.
2 papers · 0 benchmarks
The PEARL dataset comprises with 30K pedestrian images, each annotated with 25 attribute categories, spanning over 146 sub-attributes.
0 papers · 0 benchmarks

Paper counts and descriptions are the archive's, frozen 2025-07-28; no citation counts, no stars, no trending. Sorting by "most cited" or "newest" was a live-site feature the archive does not carry.