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Mapillary Traffic Sign
2D Box
Autonomous Driving
|...
License: Unknown

Overview

The Mapillary Traffic Sign Dataset is the world’s largest and most diverse publicly available
traffic sign dataset for teaching machines to detect and recognize traffic signs. The dataset
consists of 100,000 images from all over the world, with high variability in everything from
weather and time of day to camera sensors and viewpoints.

More than 300 different traffic
sign classes have been verified and annotated, resulting in more than 320,000 labeled traffic
signs across the images. Over 52,000 images have been fully verified and annotated by humans,
with the remaining images annotated partially, using our computer vision technology.

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.

We have also studied the impact of transfer learning using our traffic
sign dataset and other traffic sign datasets released in the past. Our results show that
pretraining on our dataset boosts the average precision of the binary detection task by ~6%,
thanks to the completeness and diversity of our dataset.

Features

  • 100,000 high-resolution images (52,000 fully annotated, 48,000 partially annotated)
  • Over 300 traffic sign classes with bounding box annotations
  • Global geographic reach of images and traffic sign classes, covering 6 continents
  • Variety of weather, season, time of day, camera, and viewpoint
Data Summary
Type
Image,
Amount
100K
Size
41.72GB
Provided by
Mapillary
Mapillary is the platform that makes street-level images and map data available to scale and automate mapping. We're committed to building a global service for everyone.
| Amount 100K | Size 41.72GB
Mapillary Traffic Sign
2D Box
Autonomous Driving
License: Unknown

Overview

The Mapillary Traffic Sign Dataset is the world’s largest and most diverse publicly available
traffic sign dataset for teaching machines to detect and recognize traffic signs. The dataset
consists of 100,000 images from all over the world, with high variability in everything from
weather and time of day to camera sensors and viewpoints.

More than 300 different traffic
sign classes have been verified and annotated, resulting in more than 320,000 labeled traffic
signs across the images. Over 52,000 images have been fully verified and annotated by humans,
with the remaining images annotated partially, using our computer vision technology.

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.

We have also studied the impact of transfer learning using our traffic
sign dataset and other traffic sign datasets released in the past. Our results show that
pretraining on our dataset boosts the average precision of the binary detection task by ~6%,
thanks to the completeness and diversity of our dataset.

Features

  • 100,000 high-resolution images (52,000 fully annotated, 48,000 partially annotated)
  • Over 300 traffic sign classes with bounding box annotations
  • Global geographic reach of images and traffic sign classes, covering 6 continents
  • Variety of weather, season, time of day, camera, and viewpoint
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