Use rule-based validation and machine learning to automatically catch annotation errors before they impact your training data.
Rule-Based Annotation Validation
- Bounding boxes must exceed minimum size thresholds
- Required attributes must be filled for all annotations
- Labels must be within the valid class ontology
- Polygons must have minimum number of vertices
- Overlapping annotations flagged for review
ML-Powered Quality Detection
- Anomaly Detection - Flag unusual annotations that differ from typical patterns
- Confidence Scoring - Use pre-trained models to score annotation likelihood
- Automatic Error Flagging - Route potential errors to QA review queue
- Drift Detection - Identify when annotation patterns change over time
Pro Tip: Combine automated checks with human review for optimal quality. Machines catch systematic errors; humans catch semantic issues.