{"url":"/dataset/rmas","name":"RMAS","full_name":"Real-World Marine Animal Segmentation","description_markdown":"We construct a new large-scale real-world MAS data set for conducting extensive experiments. It consists of over 3000 images with various underwater scenes and objects. Each image is annotated with an object-level mask and assigned to a category.","description_withheld":null,"homepage":"https://github.com/zhenqifu/MASNet","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[],"tasks":[{"name":"Image Segmentation","url":"/task/image-segmentation","datasets_with_task":"/datasets/task/image-segmentation"},{"name":"Marine Animal Segmentation","url":"/task/marine-animal-segmentation","datasets_with_task":"/datasets/task/marine-animal-segmentation"}],"languages":[],"variants":["RMAS"],"data_loaders":[],"num_papers_in_archive":4,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/image-segmentation-on-rmas","task":"Image Segmentation","dataset_variant":"RMAS","rows":4,"metrics":["mIoU","S-measure","E-measure","MAE"],"first_row_in_archive_order":{"model":"MAS-SAM","paper":"/paper/mas-sam-segment-any-marine-animal-with","metrics":{"E-measure":"0.948","MAE":"0.021","S-measure":"0.865","mIoU":"0.742"},"code_links":[{"title":"drchip61/mas-sam","url":"https://github.com/drchip61/mas-sam"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/sam2-unet-segment-anything-2-makes-strong","title":"SAM2-UNet: Segment Anything 2 Makes Strong Encoder for Natural and Medical Image Segmentation","date":"2024-08-16","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":5,"samples_unverified":4,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/mas-sam-segment-any-marine-animal-with","title":"MAS-SAM: Segment Any Marine Animal with Aggregated Features","date":"2024-04-24","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":20,"samples_ran":12,"samples_unverified":8,"pointer_only_for_licence":20,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/masnet-a-robust-deep-marine-animal","title":"MASNet: A Robust Deep Marine Animal Segmentation Network","date":"2023-05-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/zoom-in-and-out-a-mixed-scale-triplet-network","title":"Zoom In and Out: A Mixed-scale Triplet Network for Camouflaged Object Detection","date":"2022-03-05","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":13,"samples_ran":12,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":3,"samples_harvested":42,"samples_ran":29,"samples_unverified":13,"pointer_only_for_licence":20,"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."}