{"url":"/dataset/wilddash","name":"WildDash","full_name":null,"description_markdown":"WildDash is a benchmark evaluation method is presented that uses the meta-information to calculate the robustness of a given algorithm with respect to the individual hazards.\r\n\r\nSource: [WildDash - Creating Hazard-Aware Benchmarks](/paper/wilddash-creating-hazard-aware-benchmarks)\r\nImage Source: [https://wilddash.cc/](https://wilddash.cc/)","description_withheld":null,"homepage":"https://wilddash.cc","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/wilddash-creating-hazard-aware-benchmarks","title":"WildDash - Creating Hazard-Aware Benchmarks","first_author":"Oliver Zendel","url":null},"license":{"name":"Custom (non-commercial)","url":"https://wilddash.cc/license/wilddash"},"modalities":[],"tasks":[{"name":"Semantic Segmentation","url":"/task/semantic-segmentation","datasets_with_task":"/datasets/task/semantic-segmentation"},{"name":"Instance Segmentation","url":"/task/instance-segmentation","datasets_with_task":"/datasets/task/instance-segmentation"},{"name":"Domain Generalization","url":"/task/domain-generalization","datasets_with_task":"/datasets/task/domain-generalization"},{"name":"Autonomous Driving","url":"/task/autonomous-driving","datasets_with_task":"/datasets/task/autonomous-driving"}],"languages":[],"variants":["WildDash"],"data_loaders":[],"num_papers_in_archive":47,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/domain-generalization-on-wilddash","task":"Domain Generalization","dataset_variant":"WildDash","rows":1,"metrics":["Mean IoU"],"first_row_in_archive_order":{"model":"ITEN","paper":"/paper/exploiting-image-translations-via-ensemble","metrics":{"Mean IoU":"31.2"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/semantic-segmentation-on-wilddash","task":"Semantic Segmentation","dataset_variant":"WildDash","rows":1,"metrics":["Mean IoU"],"first_row_in_archive_order":{"model":"SIW","paper":"/paper/the-devil-is-in-the-labels-semantic","metrics":{"Mean IoU":"69.7"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/the-devil-is-in-the-labels-semantic","title":"Scaling up Multi-domain Semantic Segmentation with Sentence Embeddings","date":"2022-02-04","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/exploiting-image-translations-via-ensemble","title":"Exploiting Image Translations via Ensemble Self-Supervised Learning for Unsupervised Domain Adaptation","date":"2021-07-13","rows_on_this_dataset":1,"code_links":0,"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."}