{"url":"/dataset/tsinghua-tencent-100k","name":"Tsinghua-Tencent 100K","full_name":"Traffic-Sign Detection and Classification in the Wild","description_markdown":"Although promising results have been achieved in the areas of traffic-sign detection and classification, few works have provided simultaneous solutions to these two tasks for realistic real world images. We make two contributions to this problem. Firstly, we have created a large traffic-sign benchmark from 100000 Tencent Street View panoramas, going beyond previous benchmarks. We call this benchmark Tsinghua-Tencent 100K. It provides 100000 images containing 30000 traffic-sign instances. These images cover large variations in illuminance and weather conditions. Each traffic-sign in the benchmark is annotated with a class label, its bounding box and pixel mask. Secondly, we demonstrate how a robust end-to-end convolutional neural network (CNN) can simultaneously detect and classify traffic-signs. Most previous CNN image processing solutions target objects that occupy a large proportion of an image, and such networks do not work well for target objects occupying only a small fraction of an image like the traffic-signs here. Experimental results show the robustness of our network and its superiority to alternatives. The benchmark, source code and the CNN model introduced in this paper is publicly available.","description_withheld":null,"homepage":"https://cg.cs.tsinghua.edu.cn/traffic-sign/","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Traffic Sign Recognition","url":"/task/traffic-sign-recognition","datasets_with_task":"/datasets/task/traffic-sign-recognition"}],"languages":[],"variants":["Tsinghua-Tencent 100K"],"data_loaders":[],"num_papers_in_archive":3,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/traffic-sign-recognition-on-tsinghua-tencent","task":"Traffic Sign Recognition","dataset_variant":"Tsinghua-Tencent 100K","rows":6,"metrics":["MAP","FPS (V100, b=1)","Accuracy"],"first_row_in_archive_order":{"model":"TSR-SA(with RFB-C)","paper":"/paper/a-real-time-and-high-precision-method-for","metrics":{"FPS (V100, b=1)":"48.8","MAP":"0.902"},"code_links":[{"title":"Kunkun-Jia/TSR-SA","url":"https://github.com/Kunkun-Jia/TSR-SA"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/a-real-time-and-high-precision-method-for","title":"A real-time and high-precision method for small traffic-signs recognition","date":"2021-09-25","rows_on_this_dataset":2,"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/a-hierarchical-deep-architecture-and-mini","title":"A Hierarchical Deep Architecture and Mini-Batch Selection Method For Joint Traffic Sign and Light Detection","date":"2018-06-20","rows_on_this_dataset":3,"code_links":2,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"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."}