{"url":"/dataset/ceymo","name":"CeyMo","full_name":null,"description_markdown":"CeyMo is a novel benchmark dataset for road marking detection which covers a wide variety of challenging urban, sub-urban and rural road scenarios. The dataset consists of 2887 total images of 1920 × 1080 resolution with 4706 road marking instances belonging to 11 classes. The test set is divided into six categories: normal, crowded, dazzle light, night, rain and shadow.","description_withheld":null,"homepage":"https://github.com/oshadajay/CeyMo","introduced_date":"2021-10-22","introduced_date_note":null,"introduced_by":{"paper":"/paper/ceymo-see-more-on-roads-a-novel-benchmark","title":"CeyMo: See More on Roads -- A Novel Benchmark Dataset for Road Marking Detection","first_author":"Oshada Jayasinghe","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"2D Object Detection","url":"/task/2d-object-detection","datasets_with_task":"/datasets/task/2d-object-detection"}],"languages":[],"variants":["CeyMo"],"data_loaders":[],"num_papers_in_archive":6,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/2d-object-detection-on-ceymo","task":"2D Object Detection","dataset_variant":"CeyMo","rows":5,"metrics":["mAP"],"first_row_in_archive_order":{"model":"TransMind","paper":"/paper/open-transmind-a-new-baseline-and-benchmark","metrics":{"mAP":"70.7"},"code_links":[{"title":"Traffic-X/Open-TransMind","url":"https://github.com/Traffic-X/Open-TransMind"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/open-transmind-a-new-baseline-and-benchmark","title":"Open-TransMind: A New Baseline and Benchmark for 1st Foundation Model Challenge of Intelligent Transportation","date":"2023-04-12","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/yolov7-trainable-bag-of-freebies-sets-new","title":"YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors","date":"2022-07-06","rows_on_this_dataset":1,"code_links":21,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":11,"samples_ran":1,"samples_unverified":10,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/tood-task-aligned-one-stage-object-detection","title":"TOOD: Task-aligned One-stage Object Detection","date":"2021-08-17","rows_on_this_dataset":1,"code_links":6,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":2,"samples_unverified":2,"pointer_only_for_licence":2,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/yolox-exceeding-yolo-series-in-2021","title":"YOLOX: Exceeding YOLO Series in 2021","date":"2021-07-18","rows_on_this_dataset":1,"code_links":42,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":23,"samples_ran":1,"samples_unverified":22,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/sparse-r-cnn-end-to-end-object-detection-with","title":"Sparse R-CNN: End-to-End Object Detection with Learnable Proposals","date":"2020-11-25","rows_on_this_dataset":1,"code_links":6,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":3,"samples_harvested":38,"samples_ran":4,"samples_unverified":34,"pointer_only_for_licence":2,"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."}