{"url":"/dataset/tusimple","name":"TuSimple","full_name":null,"description_markdown":"The **TuSimple** dataset consists of 6,408 road images on US highways. The resolution of image is 1280×720. The dataset is composed of 3,626 for training, 358 for validation, and 2,782 for testing called the TuSimple test set of which the images are under different weather conditions.\r\n\r\nSource: [End-to-End Lane Marker Detection via Row-wise Classification](https://arxiv.org/abs/2005.08630)\r\nImage Source: [https://www.researchgate.net/figure/a-Example-from-TuSimple-dataset-b-Derived-dataset-for-training-coordinate-network_fig4_330589970](https://www.researchgate.net/figure/a-Example-from-TuSimple-dataset-b-Derived-dataset-for-training-coordinate-network_fig4_330589970)","description_withheld":null,"homepage":"https://github.com/TuSimple/tusimple-benchmark","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":null,"title":"TuSimple benchmark","first_author":null,"url":"https://github.com/TuSimple/tusimple-benchmark"},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Lane Detection","url":"/task/lane-detection","datasets_with_task":"/datasets/task/lane-detection"}],"languages":[],"variants":["TuSimple"],"data_loaders":[{"repo":"https://github.com/TuSimple/tusimple-benchmark","url":"https://github.com/TuSimple/tusimple-benchmark","frameworks":[]}],"num_papers_in_archive":76,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/lane-detection-on-tusimple","task":"Lane Detection","dataset_variant":"TuSimple","rows":43,"metrics":["Accuracy","F1 score"],"first_row_in_archive_order":{"model":"SCNN_UNet_Attention_PL*","paper":"/paper/robust-lane-detection-through-self-pre","metrics":{"Accuracy":"98.38"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/clrnetv2-a-faster-and-stronger-lane-detector","title":"CLRNetV2: A Faster and Stronger Lane Detector","date":"2025-03-18","rows_on_this_dataset":2,"code_links":0,"syntology":null},{"paper":"/paper/contrastive-learning-for-lane-detection-via","title":"Contrastive Learning for Lane Detection via cross-similarity","date":"2023-08-16","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/robust-lane-detection-through-self-pre","title":"Robust Lane Detection through Self Pre-training with Masked Sequential Autoencoders and Fine-tuning with Customized PolyLoss","date":"2023-05-26","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/canet-curved-guide-line-network-with-adaptive","title":"CANet: Curved Guide Line Network with Adaptive Decoder for Lane Detection","date":"2023-04-23","rows_on_this_dataset":3,"code_links":0,"syntology":null},{"paper":"/paper/a-keypoint-based-global-association-network","title":"A Keypoint-based Global Association Network for Lane Detection","date":"2022-04-15","rows_on_this_dataset":3,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":3,"samples_unverified":4,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/eigenlanes-data-driven-lane-descriptors-for","title":"Eigenlanes: Data-Driven Lane Descriptors for Structurally Diverse Lanes","date":"2022-03-29","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":15,"samples_ran":3,"samples_unverified":12,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/lane-detection-with-position-embedding","title":"Lane detection with Position Embedding","date":"2022-03-23","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/clrnet-cross-layer-refinement-network-for","title":"CLRNet: Cross Layer Refinement Network for Lane Detection","date":"2022-03-19","rows_on_this_dataset":3,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":0,"samples_unverified":4,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/rethinking-efficient-lane-detection-via-curve","title":"Rethinking Efficient Lane Detection via Curve Modeling","date":"2022-03-04","rows_on_this_dataset":2,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":12,"samples_ran":0,"samples_unverified":12,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/focus-on-local-detecting-lane-marker-from","title":"Focus on Local: Detecting Lane Marker from Bottom Up via Key Point","date":"2021-05-28","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/condlanenet-a-top-to-down-lane-detection","title":"CondLaneNet: a Top-to-down Lane Detection Framework Based on Conditional Convolution","date":"2021-05-11","rows_on_this_dataset":4,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":1,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; 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not a correctness claim."}},{"paper":"/paper/learning-lightweight-lane-detection-cnns-by","title":"Learning Lightweight Lane Detection CNNs by Self Attention Distillation","date":"2019-08-02","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/lane-detection-and-classification-using","title":"Lane Detection and Classification using Cascaded CNNs","date":"2019-07-02","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/190503704","title":"Agnostic Lane Detection","date":"2019-05-02","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/el-gan-embedding-loss-driven-generative","title":"EL-GAN: Embedding Loss Driven Generative Adversarial Networks for Lane Detection","date":"2018-06-14","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/learning-to-cluster-for-proposal-free","title":"Learning to Cluster for Proposal-Free Instance Segmentation","date":"2018-03-17","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/towards-end-to-end-lane-detection-an-instance","title":"Towards End-to-End Lane Detection: an Instance Segmentation Approach","date":"2018-02-15","rows_on_this_dataset":1,"code_links":22,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":34,"samples_ran":3,"samples_unverified":31,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/semantic-instance-segmentation-with-a","title":"Semantic Instance Segmentation with a Discriminative Loss Function","date":"2017-08-08","rows_on_this_dataset":1,"code_links":8,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":0,"samples_unverified":2,"pointer_only_for_licence":2,"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":10,"samples_harvested":99,"samples_ran":13,"samples_unverified":86,"pointer_only_for_licence":3,"papers_with_no_sample_that_ran":4,"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."}