Tsinghua-Tencent 100K 是清华大学与腾讯发布的大规模交通标志数据集，原数据集无法直接使用于YOLO训练，因此发布经过清洗后的TT100K数据集，用于训练交通标志检测模型。
数据集使用Tsinghua-Tencent 100K Annotations 2021 ，由Tianli转换为
yolo数据格式，使用请遵循TT100K数据集相关许可协议！
数据集样本
Chinese traffic-sign classes. Signs in yellow, red and blue boxes are warning, prohibitory and mandatory signs respectively. Each traffic-sign has a unique label. Some signs shown are representative of a family (e.g. speed limit signs for different speeds). Such signs are generically denoted above (e.g. ‘pl*’); the unique label is determined by replacing ‘*’ by a specific value (e.g. ‘pl40’ for a 40 kmh speed limit sign).
数据集
Traffic-Sign Detection and Classification in the Wild
Zhe Zhu, Dun Liang, Songhai Zhang, Xiaolei Huang, Baoli Li, Shimin Hu
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.
@InProceedings{Zhe_2016_CVPR,
author = {Zhu, Zhe and Liang, Dun and Zhang, Songhai and Huang, Xiaolei and Li, Baoli and Hu, Shimin},
title = {Traffic-Sign Detection and Classification in the Wild},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2016}
}Download
天翼云：
（访问码：pbp6）
直链：
注意事项
使用时注意核查yml文件，确保yml目录结构正确