{"url":"/dataset/streethazards","name":"StreetHazards","full_name":null,"description_markdown":"StreetHazards is a synthetic dataset for anomaly detection, created by inserting a diverse array of foreign objects into driving scenes and re-render the scenes with these novel objects.\r\n\r\nSource: [Scaling Out-of-Distribution Detection for Real-World Settings](/paper/a-benchmark-for-anomaly-segmentation)","description_withheld":null,"homepage":"https://github.com/hendrycks/anomaly-seg","introduced_date":"2019-11-25","introduced_date_note":null,"introduced_by":{"paper":"/paper/a-benchmark-for-anomaly-segmentation","title":"Scaling Out-of-Distribution Detection for Real-World Settings","first_author":"Dan Hendrycks","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Scene Segmentation","url":"/task/scene-segmentation","datasets_with_task":"/datasets/task/scene-segmentation"}],"languages":[],"variants":["StreetHazards"],"data_loaders":[{"repo":"https://github.com/hendrycks/anomaly-seg","url":"https://github.com/hendrycks/anomaly-seg","frameworks":["pytorch"]}],"num_papers_in_archive":20,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/scene-segmentation-on-streethazards","task":"Scene Segmentation","dataset_variant":"StreetHazards","rows":3,"metrics":["Open-mIoU"],"first_row_in_archive_order":{"model":"Mask2Anomaly","paper":"/paper/unmasking-anomalies-in-road-scene","metrics":{"Open-mIoU":"59.8"},"code_links":[{"title":"shyam671/mask2anomaly-unmasking-anomalies-in-road-scene-segmentation","url":"https://github.com/shyam671/mask2anomaly-unmasking-anomalies-in-road-scene-segmentation"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/unmasking-anomalies-in-road-scene","title":"Unmasking Anomalies in Road-Scene Segmentation","date":"2023-07-25","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/residual-pattern-learning-for-pixel-wise-out","title":"Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic Segmentation","date":"2022-11-26","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":10,"samples_ran":3,"samples_unverified":7,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/densehybrid-hybrid-anomaly-detection-for","title":"DenseHybrid: Hybrid Anomaly Detection for Dense Open-set Recognition","date":"2022-07-06","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":13,"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":2,"samples_harvested":23,"samples_ran":15,"samples_unverified":8,"pointer_only_for_licence":13,"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."}