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Multi-Label Learning datasets

archive 2025-07-28

8 datasets carry the task tag "Multi-Label Learning" (the task itself: Multi-Label Learning), ordered by the archive's paper count. Page 1 of 1: 8 shown of 8. 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

Multi-Label Learning datasets 1–8 of 8

The COCO (Common Objects in Context) dataset is a large-scale object detection, segmentation, and captioning dataset.
11,922 papers · 77 benchmarks
ExpW (Expression in-the-Wild)
The Expression in-the-Wild (ExpW) dataset is for facial expression recognition and contains 91,793 faces manually labeled with expressions.
41 papers · 1 benchmark
The BirdSong dataset consists of audio recordings of bird songs at the H.
35 papers · 0 benchmarks
Animal Kingdom is a large and diverse dataset that provides multiple annotated tasks to enable a more thorough understanding of natural animal behaviors.
26 papers · 2 benchmarks
EXTREME CLASSIFICATION (Extreme Multi-label Classification)
The objective in extreme multi-label classification is to learn feature architectures and classifiers that can automatically tag a data point with the most relevant subset of labels from an extremely large label set.
18 papers · 0 benchmarks
For each dataset we provide a short description as well as some characterization metrics.
4 papers · 0 benchmarks
This dataset is the images of corn seeds considering the top and bottom view independently (two images for one corn seed: top and bottom).
0 papers · 0 benchmarks
MIMIC Meme Dataset (Misogyny Identification in Multimodal Internet Content in Hindi-English Code-Mix Language)
This dataset endeavors to fill the research void by presenting a meticulously curated collection of misogynistic memes in a code-mixed language of Hindi and English.
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.