Label images as defective or not defective.
Reduce judgment differences between annotators.
Choose two data practices.
Document boundary and missing cases and measure agreement among annotators, reviewing disputed examples instead of deleting them blindly.
Detailed explanation
Label uncertainty becomes visible.
Difficult cases and production behavior may disappear.
Consistency and reproducibility are lost.
Annotators can apply a consistent rule.
Check official documentation
Compare the following points with the official documentation.
Review label quality and human audits in Task 4.1 and the annotation-consolidation guide. For hypothetical borderline defect images, distinguish the labeling rule, uncertainty handling, annotator disagreement, and documented revisions after review.
Official references
What to confirm
Define how to handle ambiguity and disagreement, without relying on deleting difficult cases or treating a simple majority as guaranteed truth.
Key points
- Label guideline
- Boundary case
- Agreement
Notes
- Environment: Reference review of official documentation and hypothetical examples; no AWS resources or model training are required.
- Disagreement may reflect poor image quality or incomplete criteria as well as worker error. Record criterion versions and affected existing labels when review changes the rules.
- A consolidated label is still an estimate rather than guaranteed truth. Ground Truth is closed to new customers, so this exercise requires no new labeling job.
Foundation review
Labeling guideline
A written rule set for assigning classes and handling ambiguous, missing, or exceptional cases.
Annotator agreement
The degree to which independent labelers assign the same labels to the same records.