{"url":"/dataset/marmoset-8k","name":"Marmoset-8K","full_name":"DeepLabCut multi-animal Marmoset dataset","description_markdown":"All animal procedures are overseen by veterinary staff of the MIT and Broad Institute Department of Comparative Medicine, in compliance with the NIH guide for the care and use of laboratory animals and approved by the MIT and Broad Institute animal care and use committees. Video of common marmosets (Callithrix jacchus) was collected in the laboratory of Guoping Feng at MIT. Marmosets were recorded using Kinect V2 cameras (Microsoft) with a resolution of 1080p and frame rate of 30 Hz. After acquisition, images to be used for training the network were manually cropped to 1000 x 1000 pixels or smaller. The dataset is 7,600 labeled frames from 40 different marmosets collected from 3 different colonies (in different facilities). Each cage contains a pair of marmosets, where one marmoset had light blue dye applied to its tufts. One human annotator labeled the 15 marker points on each animal present in the frame (frames contained either 1 or 2 animals).\r\n\r\nhttps://benchmark.deeplabcut.org/datasets.html","description_withheld":null,"homepage":"https://benchmark.deeplabcut.org/datasets.html","introduced_date":"2022-04-12","introduced_date_note":null,"introduced_by":{"paper":"/paper/multi-animal-pose-estimation-identification","title":"Multi-animal pose estimation, identification and tracking with DeepLabCut","first_author":"Jessy Lauer","url":null},"license":{"name":"CC BY-NC 4.0","url":"https://github.com/DeepLabCut/benchmark/blob/main/source/datasets.rst"},"modalities":[],"tasks":[{"name":"Animal Pose Estimation","url":"/task/animal-pose-estimation","datasets_with_task":"/datasets/task/animal-pose-estimation"}],"languages":[],"variants":["Marmoset-8K"],"data_loaders":[],"num_papers_in_archive":2,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/animal-pose-estimation-on-marmoset-8k","task":"Animal Pose Estimation","dataset_variant":"Marmoset-8K","rows":4,"metrics":["mAP"],"first_row_in_archive_order":{"model":"BUCTD-preNet-W48 (CID-W32)","paper":"/paper/rethinking-pose-estimation-in-crowds","metrics":{"mAP":"93.3"},"code_links":[{"title":"amathislab/BUCTD","url":"https://github.com/amathislab/BUCTD"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/rethinking-pose-estimation-in-crowds","title":"Rethinking pose estimation in crowds: overcoming the detection information-bottleneck and ambiguity","date":"2023-06-13","rows_on_this_dataset":3,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":6,"samples_ran":4,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/multi-animal-pose-estimation-identification","title":"Multi-animal pose estimation, identification and tracking with DeepLabCut","date":"2022-04-12","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":6,"samples_ran":4,"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."}