TargetModeler
Synthetic Data Platform for AI/ML Training
Create Precision Labeled Synthetic Imagery for Commercial and Defense AI/ML Models in Minutes, Not Months
Break the Data Bottleneck—Build Smarter AI Models, Faster
TargetModeler delivers precision-labeled synthetic imagery for AI/ML teams building autonomous systems that demand speed
and accuracy. Its intuitive 3D serious gaming interface slashes data acquisition time, boosts labeling accuracy, and generates
diverse, balanced datasets—accelerating the path to high- performance models now, not later.
Training high-performance computer vision models for target acquisition demands vast, well-balanced datasets—but real-world image collection is slow, costly, and often misses the rare edge cases that matter most. Manual labeling and balancing can take weeks or months, only to result in biased or underperforming models.
The TargetModeler Difference
Mission-Specific Datasets in Minutes
- Auto-generates thousands of labeled EO/IR images in under 10 minutes.
- Customizable for target type, environment, sensor, and mission profile.
- Supports rapid AI adaptation to evolving operational needs.
Auto-Labeled and Physics-Based, Not Hallucinated
- Synthetic data is generated through physics- grounded rendering, not generative AI.
- Avoids hallucination risks common to GAN- or LLM-based data approaches.
- Maintains control over geometry, lighting, occlusion, and sensor effects.
No-Code, Algorithmic Data Generation
- Built for non-technical users with an intuitive 3D visual interface.
- Automatically generates balanced, diverse datasets using job-configurable parameters and mission context.
- Eliminates reliance on remote data science teams or manual annotation workflows.
Flexible Labeling Options for Mission- Specific AI
- Supports Multiple Annotation Types, to include Bounding boxes, polygons, segmentation masks, keypoints, 3D cuboids, and more.
- Optimized for ISR and CV Tasks. Configure labels for EO/IR, SAR, full-motion video, and multi-sensor fusion scenarios.
- Auto-Labelling at Scale. Algorithmic generation of rich annotations ensures fast, accurate datasets without manual tagging.
Proven Synthetic-to-Real Performance
- Operationally validated with upwards of 95% false positive reduction.
- Outperforms real-only datasets in many ISR/ATR missions.
- Enables confidence in fielded autonomy through real-world test results.
Slash development time & boost accuracy — contact us today to elevate your data strategy.
See TargetModeler in action with your use case and hardware. Watch us generate a custom dataset for you in real time.
Contact Us
- solutions@sensorops.io