Machine learning powered pre-labeling and active tooling as well as ML-based quality checks accurately annotate large volumes of images efficiently and at high quality.
Quality assurance systems rapidly monitor and prevent errors. Varying levels and types of human review are triggered based on ML model confidence scores.
Specify geometries for different classes (e.g. boxes for people, polygons for furniture, ellipses for plates) and combine different annotation types in a singular task.