Spartina TechnologyIndustrial Data Enhancement
Industrial Vision Data Enhancement
From a handful of real samples to a model-ready, annotated dataset.
We don't just generate more images. We diagnose what's missing, control defect type and morphology, and validate that the augmented set actually improves your model.
20+
Input Samples
500+
Generated
8+
Defect Types
4-Step
Validation
Why It Matters
Five problems we solve.
Industrial vision teams hit the same walls. We built the platform to break through each one.
Defect samples are scarce
Real defects are rare on the line. Models trained on thin samples miss edge cases and leak defects into production.
Annotation is expensive
Manual labeling of defect masks is slow and costly. Scaling a dataset by hand rarely fits the timeline.
New lines lack history
New products and materials ship without historical data. AI models can't ramp fast enough to protect yield.
Models fail on transfer
A model that performs on line A often degrades on line B. Re-training requires data you don't yet have.
No boundary testing
Without edge-case samples, you ship blind. Models break silently on the extremes that matter most.
Platform Showcase
Four stages, one pipeline.
Click through each stage to see the actual interface.
Find the gaps in your dataset
We profile class distribution, defect coverage, and imbalance before generating anything. You see exactly what's missing.
- Class distribution analysis
- Defect coverage map
- Imbalance & rarity report
Workflow Dashboard
Every step visible.
Track samples, generations, filters, and benchmark lifts in a single dashboard. No black boxes — every output is traceable back to its source sample.
Model-ready Package
Before → After, in one package.
Every delivery ships with images, labels, masks, train/val split, and metadata. Plus a before/after benchmark report so your team can verify the lift.
- COCO-format images + annotations
- Paired masks and class labels
- Train / validation split
- Before/after benchmark report
Workflow
Six-step validation.
Dataset Audit
Profile the gaps
Defect Taxonomy
Define defect classes
Synthetic Generation
Controlled augmentation
Quality Filtering
Reject artifacts
Model-ready Package
Images + labels + masks
Benchmark Report
Prove the lift
Trial Path
Start with your samples.
Send samples
Upload a handful of real images
Diagnosis
We analyze defect distribution and gaps
Demo augmentation
We generate augmented samples + labels
Validation report
Benchmark before/after lift
Full project
Scale to a production dataset
Contact