Edge cases are the ambiguous, unusual, or difficult scenarios that annotation guidelines don't explicitly cover. Here's how to manage them in your data labeling workflow.
Identifying Edge Cases in Data Annotation
Edge cases in training data typically emerge from:
- Unusual viewing angles or lighting conditions in image annotation
- Partial occlusion or truncation of objects
- Rare objects or categories not well-represented in your dataset
- Ambiguous classifications where multiple labels could apply
- Domain-specific scenarios requiring expert knowledge
Edge Case Documentation Strategy
- Create an "Edge Cases" channel in your team communication tool
- Collect examples with screenshots and detailed context
- Discuss and decide as a team during calibration sessions
- Document the decision in your annotation guidelines with visual examples
- Update your labeling ontology if new categories are needed
When Annotators Are Unsure
Establish a clear escalation path for data labelers. It's better to flag uncertain annotations for review than to guess wrong and introduce errors into your training data.