{"url":"/dataset/synscapes","name":"Synscapes","full_name":null,"description_markdown":"Synscapes is a synthetic dataset for street scene parsing created using photorealistic rendering techniques, and show state-of-the-art results for training and validation as well as new types of analysis. \r\n\r\nSource: [Synscapes: A Photorealistic Synthetic Dataset for Street Scene Parsing](https://arxiv.org/pdf/1810.08705v1.pdf)\r\nImage Source: [https://7dlabs.com/synscapes-overview](https://7dlabs.com/synscapes-overview)","description_withheld":null,"homepage":"https://7dlabs.com/synscapes-overview","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/synscapes-a-photorealistic-synthetic-dataset","title":"Synscapes: A Photorealistic Synthetic Dataset for Street Scene Parsing","first_author":"Magnus Wrenninge","url":null},"license":{"name":"Custom (non-commercial, attribution)","url":"https://7dlabs.com/synscapes-license"},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Object Detection","url":"/task/object-detection","datasets_with_task":"/datasets/task/object-detection"},{"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":"Instance Segmentation","url":"/task/instance-segmentation","datasets_with_task":"/datasets/task/instance-segmentation"},{"name":"Image-to-Image Translation","url":"/task/image-to-image-translation","datasets_with_task":"/datasets/task/image-to-image-translation"}],"languages":[],"variants":["Synscapes to BDD100K","Synscapes","Synscapes-to-Cityscapes"],"data_loaders":[],"num_papers_in_archive":46,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/domain-adaptation-on-synscapes-to-cityscapes","task":"Domain Adaptation","dataset_variant":"Synscapes-to-Cityscapes","rows":3,"metrics":["mIoU"],"first_row_in_archive_order":{"model":"ProDA+CRA","paper":"/paper/cross-region-domain-adaptation-for-class","metrics":{"mIoU":"60.2"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/cross-region-domain-adaptation-for-class","title":"Cross-Region Domain Adaptation for Class-level Alignment","date":"2021-09-14","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/unsupervised-intra-domain-adaptation-for","title":"Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision","date":"2020-04-16","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":8,"samples_ran":2,"samples_unverified":6,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/learning-to-adapt-structured-output-space-for","title":"Learning to Adapt Structured Output Space for Semantic Segmentation","date":"2018-02-28","rows_on_this_dataset":1,"code_links":12,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":6,"samples_unverified":1,"pointer_only_for_licence":7,"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":15,"samples_ran":8,"samples_unverified":7,"pointer_only_for_licence":7,"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."}