TargetModeler
Synthetic Data Engine for EO/IR Vision
Models
Build robust vision models with balanced, labeled EO/IR data—no cloud, no manual tagging.
Why TargetModeler
Collecting real imagery is slow, costly, and often misses edge cases. TargetModeler gives AI teams controlled, repeatable datasets so models improve without waiting on field collection.
Minutes to thousands of images
Generate mission-realistic EO/IR sets in under 10 minutes.
Clean, auto labels
Boxes, polygons, masks, keypoints, 3D cuboids produced at scale.
Balanced by design
Control class balance, ranges, poses, backgrounds, and sensor effects.
Operator-ready workflow
No code required; operators and engineers can build, adjust, and export datasets without a data-science queue.
Physics-based, not guesswork
Rendering is driven by physics-based models for controllable, repeatable results.
What TargetModeler can do
Cover edge cases on demand
Occlusion, low light, glare, clutter, small objects, and look-alikes.
Mirror operational sensors
Configure EO/IR, SAR, noise, blur, compression, and optics limits.
Auto-label at scale
Generate rich annotations and QA summaries with each dataset.
Rebalance quickly
Adjust class ratios and hard negatives without rebuilding the entire dataset.
Export and train
Push datasets into existing pipelines (COCO, YOLO, MMDet, custom) and track model performance changes over time.
The TargetModeler stack
Scene Builder
Drag-and-drop assets, terrains, routes, behaviors, and injects.
Sensor Forge
Physics-based EO/IR/SAR effects with parameter presets that can be saved.
Label Engine
Auto boxes, polygons, masks, keypoints, 3D cuboids; QA reports included.
Dataset Manager
Balance controls, splits, versioning, and export formats.
Why teams trust TargetModeler
Documented model gains
Repeatable by design
Fits your stack
Operational impact
Model improvement cadence
Model updates increasingly driven by synthetic runs between live collections, reducing dependence on new field data.
Performance in hard conditions
Precision and recall strengthened in night, glare, clutter, and small-target scenarios when tuned to program requirements.
Labeling efficiency
Labeling and relabeling effort shifted from manual tools into automated annotation workflows.
Test and data planning
Data collection and re-test effort moved from ad hoc live events into planned synthetic campaigns that can be repeated and extended.
Defense & commercial use cases
Defense
ATR/ISR target sets, look-alike discrimination, EW-degraded conditions.
Public safety & critical infrastructure
Perimeter, substation, and pipeline scenario data.
Industrial
Yard logistics, warehouse detection, inspection defects.
Platform OEMs & system integrators
Customer-specific datasets aligned with training and acceptance criteria.
How teams use TargetModeler
- 01
Build
- 02
Rehearse
- 03
Review
- 04
Balance/split
- 05
Export
- 06
Train
- 05
Measure
- 06
Iterate
Pricing & deployment
Start with a single project and expand as requirements grow. On-prem install with optional cloud-assist.
Program impact at a glance
< 10 minutes
to generate an initial 1,000 labeled images in a typical configuration
Manual labeling time
reduced significantly through automatic annotation
Edge-case coverage expanded
across conditions such as night, glare, clutter, and small targets
Common questions
Yes, CAD models and terrain can be brought in and tagged once for reuse.
Yes, processing and decisions run on device.
Yes, processing and decisions run on device.
Yes, processing and decisions run on device.