{"url":"/dataset/fish-100","name":"Fish-100","full_name":null,"description_markdown":"Schools of inland silversides (Menidia beryllina, n=14 individuals per school) were recorded in the Lauder Lab at Harvard University while swimming at 15 speeds (0.5 to 8 BL/s, body length, at 0.5 BL/s intervals) in a flow tank with a total working section of 28 x 28 x 40 cm as described in previous work, at a constant temperature (18±1°C) and salinity (33 ppt), at a Reynolds number of approximately 10,000 (based on BL). Dorsal views of steady swimming across these speeds were recorded by high-speed video cameras (FASTCAM Mini AX50, Photron USA, San Diego, CA, USA) at 60-125 frames per second (feeding videos at 60 fps, swimming alone 125 fps). The dorsal view was recorded above the swim tunnel and a floating Plexiglas panel at the water surface prevented surface ripples from interfering with dorsal view videos. Five keypoints were labeled (tip, gill, peduncle, dorsal fin tip, caudal tip). 100 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":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Animal Pose Estimation","url":"/task/animal-pose-estimation","datasets_with_task":"/datasets/task/animal-pose-estimation"}],"languages":[],"variants":["Fish-100"],"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-fish-100","task":"Animal Pose Estimation","dataset_variant":"Fish-100","rows":4,"metrics":["mAP"],"first_row_in_archive_order":{"model":"HRNet-W48 + Faster R-CNN","paper":"/paper/rethinking-pose-estimation-in-crowds","metrics":{"mAP":"89.1"},"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":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."}