  Traffic signs are essential map features globally in the era of autonomous
driving and smart cities. To develop accurate and robust algorithms for traffic
sign detection and classification, a large-scale and diverse benchmark dataset
is required. In this paper, we introduce a traffic sign benchmark dataset of
100K street-level images around the world that encapsulates diverse scenes,
wide coverage of geographical locations, and varying weather and lighting
conditions and covers more than 300 manually annotated traffic sign classes.
The dataset includes 52K images that are fully annotated and 48K images that
are partially annotated. This is the largest and the most diverse traffic sign
dataset consisting of images from all over world with fine-grained annotations
of traffic sign classes. We have run extensive experiments to establish strong
baselines for both the detection and the classification tasks. In addition, we
have verified that the diversity of this dataset enables effective transfer
learning for existing large-scale benchmark datasets on traffic sign detection
and classification. The dataset is freely available for academic research:
https://www.mapillary.com/dataset/trafficsign.
