{"url":"/dataset/suim","name":"SUIM","full_name":"Segmentation of Underwater IMagery","description_markdown":"The Segmentation of Underwater IMagery (SUIM) dataset contains over 1500 images with pixel annotations for eight object categories: fish (vertebrates), reefs (invertebrates), aquatic plants, wrecks/ruins, human divers, robots, and sea-floor. The images have been rigorously collected during oceanic explorations and human-robot collaborative experiments, and annotated by human participants.\r\n\r\nSource: [Semantic Segmentation of Underwater Imagery: Dataset and Benchmark](https://arxiv.org/abs/2004.01241)\r\nImage Source: [http://irvlab.cs.umn.edu/resources/suim-dataset](http://irvlab.cs.umn.edu/resources/suim-dataset)","description_withheld":null,"homepage":"http://irvlab.cs.umn.edu/resources/suim-dataset","introduced_date":"2020-04-02","introduced_date_note":null,"introduced_by":{"paper":"/paper/semantic-segmentation-of-underwater-imagery","title":"Semantic Segmentation of Underwater Imagery: Dataset and Benchmark","first_author":"Md Jahidul Islam","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Semantic Segmentation","url":"/task/semantic-segmentation","datasets_with_task":"/datasets/task/semantic-segmentation"},{"name":"Semi-Supervised Semantic Segmentation","url":"/task/semi-supervised-semantic-segmentation","datasets_with_task":"/datasets/task/semi-supervised-semantic-segmentation"},{"name":"Unsupervised Semantic Segmentation","url":"/task/unsupervised-semantic-segmentation","datasets_with_task":"/datasets/task/unsupervised-semantic-segmentation"},{"name":"Scene Understanding","url":"/task/scene-understanding","datasets_with_task":"/datasets/task/scene-understanding"},{"name":"Saliency Prediction","url":"/task/saliency-prediction","datasets_with_task":"/datasets/task/saliency-prediction"}],"languages":[],"variants":["SUIM"],"data_loaders":[{"repo":"https://github.com/AliMuwafaq92/test","url":"https://github.com/AliMuwafaq92/test","frameworks":[]}],"num_papers_in_archive":34,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/unsupervised-semantic-segmentation-on-suim","task":"Unsupervised Semantic Segmentation","dataset_variant":"SUIM","rows":4,"metrics":["Pixel Accuracy","mIoU"],"first_row_in_archive_order":{"model":"DatUS (ViT-B/8) + OC","paper":"/paper/datus-2-data-driven-unsupervised-semantic","metrics":{"Pixel Accuracy":"69.98","mIoU":"34.02"},"code_links":[{"title":"SonalKumar95/DatUS","url":"https://github.com/SonalKumar95/DatUS"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/semi-supervised-semantic-segmentation-on-suim","task":"Semi-Supervised Semantic Segmentation","dataset_variant":"SUIM","rows":1,"metrics":["Mean IoU (class)"],"first_row_in_archive_order":{"model":"AIM+ (256x256, 2.7m parameters, 10% labeled data, no pretraining)","paper":"/paper/inconsistency-masks-removing-the-uncertainty","metrics":{"Mean IoU (class)":"0.482"},"code_links":[{"title":"michaelvorndran/inconsistencymasks","url":"https://github.com/michaelvorndran/inconsistencymasks"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/grapix-exploring-graph-modularity","title":"GraPix: Exploring Graph Modularity Optimization for Unsupervised Pixel Clustering","date":"2024-12-04","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/inconsistency-masks-removing-the-uncertainty","title":"Inconsistency Masks: Removing the Uncertainty from Input-Pseudo-Label Pairs","date":"2024-01-25","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/datus-2-data-driven-unsupervised-semantic","title":"DatUS^2: Data-driven Unsupervised Semantic Segmentation with Pre-trained Self-supervised Vision Transformer","date":"2024-01-23","rows_on_this_dataset":2,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"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."}