Time per annotation
Cost per annotation
Well-designed tools to support large number of classes of interest.
Consensus models to verify each of the annotations by multiple users for label accuracy & annotation perfection.
Recursive checks from multiple users to detect all the objects of interest in the frame.
Extensively used to train autonomous driving perception models for pedestrians, traffic signs, lane obstacles, etc.
Used to train visual search machine learning models for recognition of various fashion accessories and furniture.
Identification of car damage, roof damage or safety parameters from live world images to train machine learning models that detect the degree of damage for insurance claims.
Labelled images for training smart surveillance drones and robots to identify a variety of objects.
All shape annotations on single media
Support for hundreds of classes
Guaranteed highly accurate results
Contexual information from the data
Guaranteed accuracy. We know you’d have it no other way.
Use our API to get responses within minutes.
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