{"url":"/dataset/trimouse-161","name":"TriMouse-161","full_name":null,"description_markdown":"Three wild-type (C57BL/6J) male mice ran on a paper spool following odor trails (Mathis et al 2018). These experiments were carried out in the laboratory of Venkatesh N. Murthy at Harvard University. Data were recorded at 30 Hz with 640 x 480 pixels resolution acquired with a Point Grey Firefly FMVU-03MTM-CS. One human annotator was instructed to localize the 12 keypoints (snout, left ear, right ear, shoulder, four spine points, tail base and three tail points). All surgical and experimental procedures for mice were in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals and approved by the Harvard Institutional Animal Care and Use Committee. 161 frames were labeled, making this a real-world sized laboratory dataset.","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://benchmark.deeplabcut.org/datasets.html"},"modalities":[],"tasks":[{"name":"Animal Pose Estimation","url":"/task/animal-pose-estimation","datasets_with_task":"/datasets/task/animal-pose-estimation"}],"languages":[],"variants":["TriMouse-161"],"data_loaders":[],"num_papers_in_archive":3,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/animal-pose-estimation-on-trimouse-161","task":"Animal Pose Estimation","dataset_variant":"TriMouse-161","rows":7,"metrics":["mAP"],"first_row_in_archive_order":{"model":"BUCTD-CoAM-W48 (DLCRNet)","paper":"/paper/rethinking-pose-estimation-in-crowds","metrics":{"mAP":"99.1"},"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":2,"code_links":2,"syntology":null},{"paper":"/paper/panoptic-animal-pose-estimators-are-zero-shot","title":"SuperAnimal pretrained pose estimation models for behavioral analysis","date":"2022-03-14","rows_on_this_dataset":2,"code_links":3,"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."}