{"url":"/dataset/lost-and-found","name":"Lost and Found","full_name":null,"description_markdown":"**Lost and Found** is a novel lost-cargo image sequence dataset comprising more than two thousand frames with pixelwise annotations of obstacle and free-space and provide a thorough comparison to several stereo-based baseline methods. The dataset will be made available to the community to foster further research on this important topic.\r\n\r\nSource: [Lost and Found: Detecting Small Road Hazards for Self-Driving Vehicles](/paper/lost-and-found-detecting-small-road-hazards)\r\nImage Source: [http://www.6d-vision.com/lostandfounddataset](http://www.6d-vision.com/lostandfounddataset)","description_withheld":null,"homepage":"http://www.6d-vision.com/lostandfounddataset","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/lost-and-found-detecting-small-road-hazards","title":"Lost and Found: Detecting Small Road Hazards for Self-Driving Vehicles","first_author":"Peter Pinggera","url":null},"license":{"name":"Custom","url":"http://www.6d-vision.com/lostandfounddataset"},"modalities":[],"tasks":[{"name":"Semantic Segmentation","url":"/task/semantic-segmentation","datasets_with_task":"/datasets/task/semantic-segmentation"},{"name":"Anomaly Detection","url":"/task/anomaly-detection","datasets_with_task":"/datasets/task/anomaly-detection"},{"name":"Autonomous Driving","url":"/task/autonomous-driving","datasets_with_task":"/datasets/task/autonomous-driving"},{"name":"Self-Driving Cars","url":"/task/self-driving-cars","datasets_with_task":"/datasets/task/self-driving-cars"}],"languages":[],"variants":["Lost and Found"],"data_loaders":[{"repo":"https://github.com/tensorflow/datasets","url":"https://www.tensorflow.org/datasets/catalog/lost_and_found","frameworks":["tf","jax"]}],"num_papers_in_archive":57,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/anomaly-detection-on-lost-and-found","task":"Anomaly Detection","dataset_variant":"Lost and Found","rows":4,"metrics":["AP","FPR"],"first_row_in_archive_order":{"model":"Mask2Anomaly","paper":"/paper/unmasking-anomalies-in-road-scene","metrics":{"AP":"86.59","FPR":"5.75"},"code_links":[{"title":"shyam671/mask2anomaly-unmasking-anomalies-in-road-scene-segmentation","url":"https://github.com/shyam671/mask2anomaly-unmasking-anomalies-in-road-scene-segmentation"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/unmasking-anomalies-in-road-scene","title":"Unmasking Anomalies in Road-Scene Segmentation","date":"2023-07-25","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/pixel-wise-energy-biased-abstention-learning","title":"Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentation on Complex Urban Driving Scenes","date":"2021-11-24","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":3,"samples_unverified":0,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/standardized-max-logits-a-simple-yet","title":"Standardized Max Logits: A Simple yet Effective Approach for Identifying Unexpected Road Obstacles in Urban-Scene Segmentation","date":"2021-07-23","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":0,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/pixel-wise-anomaly-detection-in-complex","title":"Pixel-wise Anomaly Detection in Complex Driving Scenes","date":"2021-03-09","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":2,"samples_harvested":4,"samples_ran":3,"samples_unverified":1,"pointer_only_for_licence":3,"papers_with_no_sample_that_ran":1,"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."}