{"url":"/dataset/all-day-cityscapes","name":"All-day CityScapes","full_name":null,"description_markdown":"We design an all-day semantic segmentation benchmark all-day CityScapes. It is the first semantic segmentation benchmark that contains samples from all-day scenarios, i.e., from dawn to night. Our dataset will be made publicly available at [https://isis-data.science.uva.nl/cv/1ADcityscape.zip].","description_withheld":null,"homepage":"","introduced_date":"2023-07-07","introduced_date_note":null,"introduced_by":{"paper":"/paper/interactive-learning-of-intrinsic-and","title":"Interactive Learning of Intrinsic and Extrinsic Properties for All-day Semantic Segmentation","first_author":"Qi Bi","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":"Domain Adaptation","url":"/task/domain-adaptation","datasets_with_task":"/datasets/task/domain-adaptation"},{"name":"Domain Generalization","url":"/task/domain-generalization","datasets_with_task":"/datasets/task/domain-generalization"},{"name":"Universal Domain Adaptation","url":"/task/universal-domain-adaptation","datasets_with_task":"/datasets/task/universal-domain-adaptation"},{"name":"All-day Semantic Segmentation","url":"/task/all-day-semantic-segmentation","datasets_with_task":"/datasets/task/all-day-semantic-segmentation"}],"languages":[],"variants":["All-day CityScapes"],"data_loaders":[{"repo":"https://github.com/BiQiWHU/All-day-CityScapes-segmentation","url":"https://github.com/BiQiWHU/All-day-CityScapes-segmentation","frameworks":["pytorch"]}],"num_papers_in_archive":3,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/all-day-semantic-segmentation-on-all-day","task":"All-day Semantic Segmentation","dataset_variant":"All-day CityScapes","rows":3,"metrics":["mIoU"],"first_row_in_archive_order":{"model":"AO-SegNet (Swin-Base)","paper":"/paper/interactive-learning-of-intrinsic-and","metrics":{"mIoU":"78.6"},"code_links":[{"title":"BiQiWHU/All-day-CityScapes-segmentation","url":"https://github.com/BiQiWHU/All-day-CityScapes-segmentation"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/interactive-learning-of-intrinsic-and","title":"Interactive Learning of Intrinsic and Extrinsic Properties for All-day Semantic Segmentation","date":"2023-07-07","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/deep-dual-resolution-networks-for-real-time","title":"Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes","date":"2021-01-15","rows_on_this_dataset":1,"code_links":8,"syntology":null},{"paper":"/paper/two-at-once-enhancing-learning-and","title":"Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net","date":"2018-07-25","rows_on_this_dataset":1,"code_links":25,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":16,"samples_ran":4,"samples_unverified":12,"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":1,"samples_harvested":16,"samples_ran":4,"samples_unverified":12,"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."}