Identify data shortages before model development
VISION AI
DataForge
Synthetic defects, assisted labeling and dataset management — Supports rare-defect development while preserving the source, label, usage rights and training lineage of manufacturing datasets.
Concept · validation requiredControl the approved use of synthetic and augmented data
Trace label quality, dataset versions and training history
COMPONENTS
Components
- Dataset registry
- Labeling workbench
- Synthetic and augmentation pipeline
- Lineage and access controls
INTEGRATION
Integration
- Image storage
- VisionOps
- Training environment
- Identity and access management
DELIVERABLES
Deliverables
- Dataset card
- Labeling guideline
- Synthetic-data review record
- Model training lineage
VALIDATION ITEMS
What must be validated before specifications
Performance, specifications and schedule are confirmed only after validating the actual samples and site conditions.
- 01Data rights
- 02Synthetic-data bias
- 03Label consistency
- 04Security and data-export conditions
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Validate feasibility with actual samples and site conditions
Product status and specifications are stated in the proposal based on validation and supply conditions.