{"url":"/dataset/oodis","name":"OoDIS","full_name":"Anomaly Instance Segmentation Benchmark","description_markdown":"OoDIS is a benchmark dataset for anomaly instance segmentation, crucial for autonomous vehicle safety. It extends existing anomaly segmentation benchmarks to focus on the segmentation of individual out-of-distribution (OOD) objects.\r\n\r\nThe dataset addresses the need for identifying and segmenting unknown objects, which are critical to avoid accidents. It includes diverse scenes with various anomalies, pushing the boundaries of current segmentation capabilities.\r\n\r\nThe benchmark is focused on evaluation of detection and instance segmentation of unexpected obstacles on roads.\r\n\r\nFor more details, refer to the [OoDIS paper](https://arxiv.org/abs/2406.11835)","description_withheld":null,"homepage":"https://kumuji.github.io/oodis_website/","introduced_date":"2024-06-15","introduced_date_note":null,"introduced_by":{"paper":"/paper/oodis-anomaly-instance-segmentation-benchmark","title":"OoDIS: Anomaly Instance Segmentation Benchmark","first_author":"Alexey Nekrasov","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Object Detection","url":"/task/object-detection","datasets_with_task":"/datasets/task/object-detection"},{"name":"Anomaly Detection","url":"/task/anomaly-detection","datasets_with_task":"/datasets/task/anomaly-detection"},{"name":"Instance Segmentation","url":"/task/instance-segmentation","datasets_with_task":"/datasets/task/instance-segmentation"},{"name":"Anomaly Instance Segmentation","url":"/task/anomaly-instance-segmentation","datasets_with_task":"/datasets/task/anomaly-instance-segmentation"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["OoDIS"],"data_loaders":[{"repo":"https://github.com/kumuji/ugains","url":"https://github.com/kumuji/ugains","frameworks":["pytorch"]}],"num_papers_in_archive":5,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/instance-segmentation-on-oodis","task":"Instance Segmentation","dataset_variant":"OoDIS","rows":3,"metrics":["AP","AP50"],"first_row_in_archive_order":{"model":"UGainS","paper":"/paper/ugains-uncertainty-guided-anomaly-instance","metrics":{"AP":"25.19","AP50":"42.81"},"code_links":[{"title":"kumuji/ugains","url":"https://github.com/kumuji/ugains"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/object-detection-on-oodis","task":"Object Detection","dataset_variant":"OoDIS","rows":3,"metrics":["AP","AP50"],"first_row_in_archive_order":{"model":"UGainS","paper":"/paper/ugains-uncertainty-guided-anomaly-instance","metrics":{"AP":"11.14","AP50":"16.75"},"code_links":[{"title":"kumuji/ugains","url":"https://github.com/kumuji/ugains"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/ugains-uncertainty-guided-anomaly-instance","title":"UGainS: Uncertainty Guided Anomaly Instance Segmentation","date":"2023-08-03","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/unmasking-anomalies-in-road-scene","title":"Unmasking Anomalies in Road-Scene Segmentation","date":"2023-07-25","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/holistic-segmentation","title":"Segmenting Known Objects and Unseen Unknowns without Prior Knowledge","date":"2022-09-12","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."}