The quality of labeling drives the quality of models. Using automation, distributing the labeling tasks across teams, and controlling quality can drastically reduce the time to deploy.

 

• Collect data from multiple sources

• Labeling support for images, videos and documents

• Distribute labeling tasks across distributed team members

• Outsource your labeling tasks

• Ensure quality with the audit and review process

• Create multiple training datasets from a common set of labelled images

• Use auto labeling feature to reduce labeling effort by up to 70%