{"url":"/dataset/apolloscape-1","name":"ApolloScape","full_name":null,"description_markdown":"**ApolloScape** is a large dataset consisting of over 140,000 video frames (73 street scene videos) from various locations in China under varying weather conditions. Pixel-wise semantic annotation of the recorded data is provided in 2D, with point-wise semantic annotation in 3D for 28 classes. In addition, the dataset contains lane marking annotations in 2D.\r\n\r\nSource: [A2D2: Audi Autonomous Driving Dataset](https://arxiv.org/abs/2004.06320)\r\nImage Source: [https://arxiv.org/pdf/1803.06184.pdf](https://arxiv.org/pdf/1803.06184.pdf)","description_withheld":null,"homepage":"http://apolloscape.auto/","introduced_date":"2018-01-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/the-apolloscape-open-dataset-for-autonomous","title":"The ApolloScape Open Dataset for Autonomous Driving and its Application","first_author":"Xinyu Huang","url":null},"license":{"name":"Custom (research-only)","url":"http://apolloscape.auto/trajectory.html"},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Videos","url":"/datasets/modality/videos"}],"tasks":[{"name":"Object Detection","url":"/task/object-detection","datasets_with_task":"/datasets/task/object-detection"},{"name":"Semantic Segmentation","url":"/task/semantic-segmentation","datasets_with_task":"/datasets/task/semantic-segmentation"},{"name":"Trajectory Prediction","url":"/task/trajectory-prediction","datasets_with_task":"/datasets/task/trajectory-prediction"},{"name":"Image Inpainting","url":"/task/image-inpainting","datasets_with_task":"/datasets/task/image-inpainting"},{"name":"Autonomous Driving","url":"/task/autonomous-driving","datasets_with_task":"/datasets/task/autonomous-driving"},{"name":"Motion Segmentation","url":"/task/motion-segmentation","datasets_with_task":"/datasets/task/motion-segmentation"}],"languages":[],"variants":["ApolloScape"],"data_loaders":[],"num_papers_in_archive":74,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/motion-segmentation-on-apolloscape","task":"Motion Segmentation","dataset_variant":"ApolloScape","rows":5,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"Rule Based","paper":"/paper/understanding-dynamic-scenes-using-graph","metrics":{"Accuracy":"90"},"code_links":[{"title":"ma8sa/Undersrtanding-Dynamic-Scenes-using-MR-GCN","url":"https://github.com/ma8sa/Undersrtanding-Dynamic-Scenes-using-MR-GCN"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/semantic-segmentation-on-apolloscape","task":"Semantic Segmentation","dataset_variant":"ApolloScape","rows":2,"metrics":["mIoU"],"first_row_in_archive_order":{"model":"ERFNet-IntRA-KD (ours)","paper":"/paper/inter-region-affinity-distillation-for-road","metrics":{"mIoU":"43.2"},"code_links":[{"title":"cardwing/Codes-for-IntRA-KD","url":"https://github.com/cardwing/Codes-for-IntRA-KD"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/trajectory-prediction-on-apolloscape","task":"Trajectory Prediction","dataset_variant":"ApolloScape","rows":2,"metrics":["ADE","FDE"],"first_row_in_archive_order":{"model":"SpectralCows","paper":null,"metrics":{"ADE":"0.005"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/image-inpainting-on-apolloscape","task":"Image Inpainting","dataset_variant":"ApolloScape","rows":1,"metrics":["MAE","PSNR","RMSE","SSIM"],"first_row_in_archive_order":{"model":"DVI","paper":"/paper/dvi-depth-guided-video-inpainting-for","metrics":{"MAE":"6.135","PSNR":"21.631","RMSE":"9.633","SSIM":"0.895"},"code_links":[{"title":"ApolloScapeAuto/dataset-api","url":"https://github.com/ApolloScapeAuto/dataset-api"},{"title":"sibozhang/Depth-Guided-Inpainting","url":"https://github.com/sibozhang/Depth-Guided-Inpainting"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/vrem-fl-mobility-aware-computation-scheduling","title":"VREM-FL: Mobility-Aware Computation-Scheduling Co-Design for Vehicular Federated Learning","date":"2023-11-30","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/prank-motion-prediction-based-on-ranking","title":"PRANK: motion Prediction based on RANKing","date":"2020-10-22","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/dvi-depth-guided-video-inpainting-for","title":"DVI: Depth Guided Video Inpainting for Autonomous Driving","date":"2020-07-17","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":10,"samples_ran":2,"samples_unverified":8,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/understanding-dynamic-scenes-using-graph","title":"Understanding Dynamic Scenes using Graph Convolution Networks","date":"2020-05-09","rows_on_this_dataset":5,"code_links":1,"syntology":null},{"paper":"/paper/inter-region-affinity-distillation-for-road","title":"Inter-Region Affinity Distillation for Road Marking Segmentation","date":"2020-04-11","rows_on_this_dataset":1,"code_links":1,"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."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":2,"samples_harvested":14,"samples_ran":2,"samples_unverified":12,"pointer_only_for_licence":0,"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."}