{"url":"/dataset/gtsrb","name":"GTSRB","full_name":"German Traffic Sign Recognition Benchmark","description_markdown":"The **German Traffic Sign Recognition Benchmark** (**GTSRB**) contains 43 classes of traffic signs, split into 39,209 training images and 12,630 test images. The images have varying light conditions and rich backgrounds.\r\n\r\nSource: [Invisible Backdoor Attacks Against Deep Neural Networks](https://arxiv.org/abs/1909.02742)\r\nImage Source: [https://www.researchgate.net/figure/An-example-of-the-43-traffic-sign-classes-of-GTSRB-dataset_fig9_311896388](https://www.researchgate.net/figure/An-example-of-the-43-traffic-sign-classes-of-GTSRB-dataset_fig9_311896388)","description_withheld":null,"homepage":"https://benchmark.ini.rub.de/","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":null,"title":"computer: Benchmarking machine learning algorithms for traffic sign recognition","first_author":null,"url":"http://www.sciencedirect.com/science/article/pii/S0893608012000457"},"license":{"name":"Unknown","url":null},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Image Classification","url":"/task/image-classification","datasets_with_task":"/datasets/task/image-classification"},{"name":"Domain Adaptation","url":"/task/domain-adaptation","datasets_with_task":"/datasets/task/domain-adaptation"},{"name":"Image Clustering","url":"/task/image-clustering","datasets_with_task":"/datasets/task/image-clustering"},{"name":"Traffic Sign Recognition","url":"/task/traffic-sign-recognition","datasets_with_task":"/datasets/task/traffic-sign-recognition"}],"languages":[],"variants":["GTSRB","Synth Signs-to-GTSRB","SYNSIG-to-GTSRB"],"data_loaders":[{"repo":"https://github.com/pytorch/vision","url":"https://pytorch.org/vision/stable/generated/torchvision.datasets.GTSRB.html","frameworks":["pytorch"]},{"repo":"https://github.com/activeloopai/Hub","url":"https://docs.activeloop.ai/datasets/gtsrb-dataset","frameworks":["tf","pytorch"]},{"repo":"https://github.com/Kaggle/kaggle-api","url":"https://www.kaggle.com/datasets/meowmeowmeowmeowmeow/gtsrb-german-traffic-sign","frameworks":[]}],"num_papers_in_archive":374,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/domain-adaptation-on-synsig-to-gtsrb","task":"Domain Adaptation","dataset_variant":"SYNSIG-to-GTSRB","rows":6,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"DFA-MCD","paper":"/paper/discriminative-feature-alignment","metrics":{"Accuracy":"97.5"},"code_links":[{"title":"JingWang18/Discriminative-Feature-Alignment","url":"https://github.com/JingWang18/Discriminative-Feature-Alignment"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/traffic-sign-recognition-on-gtsrb","task":"Traffic Sign Recognition","dataset_variant":"GTSRB","rows":5,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"CNN with 3 Spatial Transformers","paper":"/paper/deep-neural-network-for-traffic-sign","metrics":{"Accuracy":"99.71%"},"code_links":[{"title":"aarcosg/tsr-torch","url":"https://github.com/aarcosg/tsr-torch"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/domain-adaptation-on-synth-signs-to-gtsrb","task":"Domain Adaptation","dataset_variant":"Synth Signs-to-GTSRB","rows":4,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"Mean teacher","paper":"/paper/self-ensembling-for-visual-domain-adaptation","metrics":{"Accuracy":"98.66"},"code_links":[{"title":"thuml/Transfer-Learning-Library","url":"https://github.com/thuml/Transfer-Learning-Library"},{"title":"domainadaptation/salad","url":"https://github.com/domainadaptation/salad"},{"title":"Britefury/self-ensemble-visual-domain-adapt","url":"https://github.com/Britefury/self-ensemble-visual-domain-adapt"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/image-classification-on-gtsrb","task":"Image Classification","dataset_variant":"GTSRB","rows":1,"metrics":["F1"],"first_row_in_archive_order":{"model":"SAG-ViT","paper":"/paper/sag-vit-a-scale-aware-high-fidelity-patching","metrics":{"F1":"99.58"},"code_links":[{"title":"shravan-18/SAG-ViT","url":"https://github.com/shravan-18/SAG-ViT"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/image-clustering-on-gtsrb","task":"Image Clustering","dataset_variant":"GTSRB","rows":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"TURTLE (CLIP + DINOv2)","paper":"/paper/let-go-of-your-labels-with-unsupervised-1","metrics":{"Accuracy":"48.4"},"code_links":[{"title":"mlbio-epfl/turtle","url":"https://github.com/mlbio-epfl/turtle"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/sag-vit-a-scale-aware-high-fidelity-patching","title":"SAG-ViT: A Scale-Aware, High-Fidelity Patching Approach with Graph Attention for Vision Transformers","date":"2024-11-14","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/seqnet-sequential-networks-for-one-shot","title":"SeqNet: Sequential Networks for One-Shot Traffic Sign Recognition With Transfer Learning","date":"2024-09-23","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/let-go-of-your-labels-with-unsupervised-1","title":"Let Go of Your Labels with Unsupervised Transfer","date":"2024-06-11","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":4,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/vision-models-are-more-robust-and-fair-when","title":"Vision Models Are More Robust And Fair When Pretrained On Uncurated Images Without Supervision","date":"2022-02-16","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/sill-net-feature-augmentation-with-separated","title":"Sill-Net: Feature Augmentation with Separated Illumination Representation","date":"2021-02-06","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/discriminative-feature-alignment","title":"Discriminative Feature Alignment: Improving Transferability of Unsupervised Domain Adaptation by Gaussian-guided Latent Alignment","date":"2020-06-23","rows_on_this_dataset":2,"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/190510748","title":"Learning Smooth Representation for Unsupervised Domain Adaptation","date":"2019-05-26","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/micronnet-a-highly-compact-deep-convolutional","title":"MicronNet: A Highly Compact Deep Convolutional Neural Network Architecture for Real-time Embedded Traffic Sign Classification","date":"2018-03-28","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/deep-neural-network-for-traffic-sign","title":"Deep neural network for traffic sign recognition systems: An analysis of spatial transformers and stochastic optimisation methods","date":"2018-03-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/maximum-classifier-discrepancy-for","title":"Maximum Classifier Discrepancy for Unsupervised Domain Adaptation","date":"2017-12-07","rows_on_this_dataset":1,"code_links":9,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/associative-domain-adaptation","title":"Associative Domain Adaptation","date":"2017-08-02","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/self-ensembling-for-visual-domain-adaptation","title":"Self-ensembling for visual domain adaptation","date":"2017-06-16","rows_on_this_dataset":1,"code_links":3,"syntology":null},{"paper":"/paper/domain-separation-networks","title":"Domain Separation Networks","date":"2016-08-22","rows_on_this_dataset":1,"code_links":6,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":14,"samples_ran":5,"samples_unverified":9,"pointer_only_for_licence":6,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/return-of-frustratingly-easy-domain","title":"Return of Frustratingly Easy Domain Adaptation","date":"2015-11-17","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":0,"samples_unverified":2,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/learning-transferable-features-with-deep","title":"Learning Transferable Features with Deep Adaptation Networks","date":"2015-02-10","rows_on_this_dataset":2,"code_links":5,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":2,"samples_unverified":0,"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":7,"samples_harvested":25,"samples_ran":12,"samples_unverified":13,"pointer_only_for_licence":12,"papers_with_no_sample_that_ran":2,"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."}