Feature counts and object-level analytics for your training data create more informed decision making about the state of your model capabilities today, and how to improve them.
Use model-assisted labeling to import pre-labeled data for labeling teams to review and adjust directly. The result: savings of 50-70% in terms of annotation costs.
With automatic task distribution and active learning, easily distribute and prioritize data attached to a project to your labelers.
Re-queue low confidence labels and always label the most important data with API driven labeling queue prioritization.