{"url":"/dataset/n-cars","name":"N-CARS","full_name":null,"description_markdown":"A large real-world event-based dataset for object classification.\r\n\r\nSource: [HATS: Histograms of Averaged Time Surfaces for Robust Event-based Object Classification](/paper/hats-histograms-of-averaged-time-surfaces-for)","description_withheld":null,"homepage":"https://www.prophesee.ai/2018/03/13/dataset-n-cars/","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/hats-histograms-of-averaged-time-surfaces-for","title":"HATS: Histograms of Averaged Time Surfaces for Robust Event-based Object Classification","first_author":"Amos Sironi","url":null},"license":{"name":"Custom","url":"https://www.prophesee.ai/2018/03/13/dataset-n-cars/"},"modalities":[],"tasks":[{"name":"Classification","url":"/task/classification-1","datasets_with_task":"/datasets/task/classification-1"},{"name":"Object Recognition","url":"/task/object-recognition","datasets_with_task":"/datasets/task/object-recognition"},{"name":"Optical Flow Estimation","url":"/task/optical-flow-estimation","datasets_with_task":"/datasets/task/optical-flow-estimation"}],"languages":[],"variants":["N-CARS"],"data_loaders":[{"repo":"https://github.com/uzh-rpg/aegnn","url":"https://github.com/uzh-rpg/aegnn","frameworks":["pytorch"]}],"num_papers_in_archive":56,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/classification-on-n-cars","task":"Classification","dataset_variant":"N-CARS","rows":6,"metrics":["Accuracy (%)","Architecture","Representation","Representation Time( ms / 100ms events)","Inference Time","Params (M)"],"first_row_in_archive_order":{"model":"MEM","paper":"/paper/masked-event-modeling-self-supervised","metrics":{"Accuracy (%)":"98.55","Architecture":"Transformer","Representation":"Event Histogram"},"code_links":[{"title":"tum-vision/mem","url":"https://github.com/tum-vision/mem"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/object-recognition-on-n-cars","task":"Object Recognition","dataset_variant":"N-CARS","rows":1,"metrics":["Accuracy (% )"],"first_row_in_archive_order":{"model":"Spike-VGG11","paper":"/paper/eventrpg-event-data-augmentation-with","metrics":{"Accuracy (% )":"96.00"},"code_links":[{"title":"myuansun/eventrpg","url":"https://github.com/myuansun/eventrpg"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/eventrpg-event-data-augmentation-with","title":"EventRPG: Event Data Augmentation with Relevance Propagation Guidance","date":"2024-03-14","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":16,"samples_ran":5,"samples_unverified":11,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/get-group-event-transformer-for-event-based-1","title":"GET: Group Event Transformer for Event-Based Vision","date":"2023-10-04","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":11,"samples_ran":8,"samples_unverified":3,"pointer_only_for_licence":11,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/masked-event-modeling-self-supervised","title":"Masked Event Modeling: Self-Supervised Pretraining for Event Cameras","date":"2022-12-20","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":18,"samples_ran":5,"samples_unverified":13,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/object-detection-with-spiking-neural-networks","title":"Object Detection with Spiking Neural Networks on Automotive Event Data","date":"2022-05-09","rows_on_this_dataset":3,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/end-to-end-learning-of-representations-for","title":"End-to-End Learning of Representations for Asynchronous Event-Based Data","date":"2019-04-17","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":3,"samples_unverified":1,"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":5,"samples_harvested":50,"samples_ran":22,"samples_unverified":28,"pointer_only_for_licence":11,"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."}