Accepted inputs
- Newly produced annotations
- Existing vendor datasets
- Model-generated pre-labels
- Client-defined gold or acceptance samples
DATASET QUALITY ASSURANCE
Annotation QA, Review and Dataset Correction
Quality assurance should show what was checked, what failed and how recurring errors were corrected. We review labels against the project guideline and acceptance criteria, classify defects, return feedback to production and verify agreed outputs before delivery.
SCOPE
What this service covers
First-pass and final reviewSample-based or full-dataset checksObject-level approval and rejectionError categorizationCorrection workflowsExport and schema validation
Annotation approach
- Guideline-based checklists
- Class-specific error review
- Root-cause and ambiguity logging
- Feedback and rework loops
- Version-aware final validation
Common edge cases
- Subjective class boundaries
- Inconsistent historical guidelines
- Duplicate frames
- Non-sequential track IDs
- Format-valid but semantically incorrect labels
QUALITY ASSURANCE
Review that follows the guideline
Quality checks are selected for the data type and acceptance criteria. Metrics and review coverage depend on the project, platform and agreed workflow.
Acceptance and rejection trendsMissed-object and false-positive errorsClass-confusion and attribute errorsGeometry, boundary and Track-ID errorsTemporal-boundary and export checks
Applications
- Pre-training validation
- Vendor dataset audits
- Model-prelabel correction
- Migration between annotation platforms
- Audit-sensitive datasets
Output formats
- Review reports
- Corrected source-format labels
- Issue logs
- Acceptance summaries
- Client-defined QA outputs
What to include in your brief
- Current guidelines and examples
- Dataset version and source
- Acceptance criteria
- Sampling or full-review requirement
- Expected report and correction format
START WITH A CALIBRATION BATCH
Reduce uncertainty before production.
Share your guidelines, sample characteristics, volume, preferred platform and timeline. We’ll assess the scope and propose a representative calibration step.
Discuss your dataset