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Model
SytheticaDETR is a real-time object detection model based on a transformer architecture trained entirely in simulation and works on real images zero-shot.
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| Field | Response |
|---|---|
| Intended Application(s) & Domain(s): | Learning to detect objects in a scene with prediction confidence suitable for robotics and autonomous machines. |
| Model Type: | Object detection model. |
| Intended Users: | Developers building and/or customizing robotics applications. |
| Output: | Bounding box coordinates and confidence. |
| Describe how the model works: | Model detects objects in a scene. |
| Technical Limitations: | SyntheticaDETR is not able to accurately detect objects outside the class of objects it was trained on. |
| Performance Metrics: | Mean Average Precision (mAP) |
| Potential Known Risks: | N/A |
| Licensing: | https://developer.download.nvidia.com/licenses/tao_toolkit_21-08_models_eula.pdf |