Train recognition models for street scenes
Develop your algorithms with the most diverse data from all over the world, including different weather, season, time of day, camera, and viewpoint conditions.
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Mapillary Vistas Dataset
A benchmark dataset of manually annotated training data for semantic segmentation of street scenes. 25,000 images pixel-accurately labeled into 152 object categories, 100 of those instance-specific.
Mapillary Traffic Sign Dataset
A benchmark dataset with bounding box annotations for detecting and classifying traffic signs around the world. 100,000 images with over 300 traffic sign classes, with manual and machine annotations.
Mapillary Street-level Sequences Dataset
A benchmark dataset for lifelong place recognition from image sequences. 1.6 million images from diverse geographies and scene characteristics, provided with GPS coordinates and sequence information.
Mapillary Planet-Scale Depth Dataset
A diverse dataset of 750'000 street-level images with metric depth information for outdoor metric depth estimation.
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computer vision models
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Autonomous Intelligent Driving uses Mapillary data to train robust perception models for autonomous vehicles.
Toyota Research Institute accelerates deployment of machine learning algorithms with Mapillary data.
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Mapillary is the platform that makes street-level images and map data available to scale and automate mapping.
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