TargetModeler
Synthetic Data Platform for AI/ML Training
Break the Data Bottleneck—Build Smarter AI Models, Faster
Training high-performance computer vision models for target acquisition requires large, well-balanced datasets. Yet collecting real-world images is slow, expensive, and often overlooks critical edge cases. Manual labeling and balancing can often take months, only to produce models that end up biased or unreliable.
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.
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.
Contact us today to elevate your data strategy.
Contact Us
- solutions@sensorops.io