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Model
TAO Pretrained Sparse4D with ResNet-101 Backbone
By using the Model(s), you agree to the Model License(s).
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| Field | Response |
|---|---|
| Intended Application(s) & Domain(s): | 3D Multi-Camera Detection & Tracking of Objects. |
| Model Type: | Resnet101 Backbone + Transformer |
| Intended Users: | This model is intended for developers working in industrial applications. |
| Output: | Bounding box information & object IDs in 3D coordinates |
| Describe how the model works: | Input images from multiple cameras are encoded & passed onto decoder layers for regression & classification tasks. |
| Name the adversely impacted groups this has been tested to deliver comparable outcomes: | Not Applicable. |
| Technical Limitations & Mitigations: | This model may not perform well for objects with poor visibility across all cameras. Model may be fine-tuned to mitigate for accuracy. Additional data can be collected using NVIDIA Omniverse to mitigate poor object visibility across cameras. |
| Verified to have met prescribed NVIDIA standards: | Yes |
| Performance Metrics: | Average Precision (AP) & Mean Average Precision (mAP) |
| Potential Known Risks: | This model may produce incorrectly placed bounding boxes and tracking IDs, particularly in instances with poor visibility. This model was trained primarily on synthetic data from warehouse scenes. As a result, it may produce false positives and false negatives when applied to different scenes, domains, or object categories outside its training scope. |
| Licensing: | GOVERNING TERMS: Use of this model is governed by the NVIDIA Open Model License. |