Local dataset quality check
Find duplicate annotations before training
Repeated annotations can inflate class counts, hide labeling mistakes, and make review lists noisy.
What to check
Identify exact duplicate labels and duplicate boxes locally, then review the affected records before export.
Why it matters
Duplicate labels over-weight examples and can make a model appear to learn patterns that are annotation artifacts.
When Pro helps
Pro adds bulk corrections and saved repair sessions for recurring review.
What your report can look like
Duplicate annotation review
An illustrative example of the findings and repair guidance Labelyst produces for your dataset.
Status: Ready with warnings
Score: 81/100
2 findings
Findings
- 3 exact duplicate annotations found
- 1 repeated label needs context review
Passed check
- No duplicate image bytes found across splits
Suggested next step: Remove only the confirmed duplicate annotations.
Rules: standard profile · local check
Illustrative local result. It is not a certification.
Check it before training
No account or upload is required for a Free check. Analysis, repairs, and reports stay on your device.