{"url":"/dataset/tsinghua-tencent-100k-official-training-and","name":"TT100K","full_name":"Tsinghua-Tencent 100K(official training and testing set)","description_markdown":"Trainging and testing data: The original training set includes 6105 images, and the original testing set includes 3071 images.\r\n\r\nDescription: 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":[],"tasks":[{"name":"Traffic Sign Detection","url":"/task/traffic-sign-detection","datasets_with_task":"/datasets/task/traffic-sign-detection"}],"languages":[{"name":"Chinese","url":"/datasets/language/chinese"}],"variants":["TT100K"],"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-detection-on-tsinghua-tencent","task":"Traffic Sign Detection","dataset_variant":"TT100K","rows":1,"metrics":["mAP@0.5"],"first_row_in_archive_order":{"model":"CABNet","paper":"/paper/context-aware-block-net-for-small-object","metrics":{"mAP@0.5":"78.0"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/context-aware-block-net-for-small-object","title":"Context-Aware Block Net for Small Object Detection","date":"2023-04-01","rows_on_this_dataset":1,"code_links":0,"syntology":null}],"syntology_totals":{"read_at":"2026-09-25T09:33:49+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."}