{"url":"/dataset/ddd17-seg","name":"DDD17-SEG","full_name":null,"description_markdown":"Based on the  [DDD17](https://pkuml.org/resources/pku-ddd17-car.html) dataset, we select some image-event pairs to evaluate the segmentation performance, namely DDD17-SEG, which only serves as a test set. The DDD17-SEG consists of 1,000 image-event pairs in five sequences (dir0,dir1,dir3,dir4,dir7), containing 86,000 masks.","description_withheld":null,"homepage":"","introduced_date":"2023-12-24","introduced_date_note":null,"introduced_by":{"paper":"/paper/segment-any-events-via-weighted-adaptation-of","title":"Segment Any Events via Weighted Adaptation of Pivotal Tokens","first_author":"Zhiwen Chen","url":null},"license":null,"modalities":[],"tasks":[{"name":"Event-based Object Segmentation","url":"/task/event-based-object-segmentation","datasets_with_task":"/datasets/task/event-based-object-segmentation"}],"languages":[],"variants":["DDD17-SEG"],"data_loaders":[],"num_papers_in_archive":2,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/event-based-object-segmentation-on-ddd17-seg","task":"Event-based Object Segmentation","dataset_variant":"DDD17-SEG","rows":2,"metrics":["mIoU"],"first_row_in_archive_order":{"model":"EventSAM","paper":"/paper/segment-any-events-via-weighted-adaptation-of","metrics":{"mIoU":"0.37"},"code_links":[{"title":"happychenpipi/eventsam","url":"https://github.com/happychenpipi/eventsam"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/segment-any-events-via-weighted-adaptation-of","title":"Segment Any Events via Weighted Adaptation of Pivotal Tokens","date":"2023-12-24","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":10,"samples_ran":6,"samples_unverified":4,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/event-based-video-reconstruction-using","title":"Event-Based Video Reconstruction Using Transformer","date":"2021-01-01","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":10,"samples_ran":6,"samples_unverified":4,"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."}