tao-train-bevfusion

BEVFusion training is automated using predefined configurations and dataset conversion to streamline multi-sensor 3D object detection model development for autonomous driving.

BEVFusion multi-sensor 3D object detection is a technique that fuses data from multiple sensors to train, evaluate, and run inference on models, enabling accurate 3D perception for autonomous driving.

Yes, NVIDIA GPUs are required for optimal performance when running BEVFusion training and inference, alongside the specific BEVFusion TAO container environment.

Yes, AutoML is supported for training BEVFusion models. The toolkit integrates AutoML alongside predefined configurations to automate and optimize the 3D object detection pipeline.

You evaluate trained BEVFusion models using the built-in evaluation feature, which assesses accuracy, security, and efficiency against multiple predefined metrics to verify readiness.

Run inference on BEVFusion models using predefined configurations within the TAO container to generate 3D object detection results on new real-world autonomous driving data.