tao-train-grounding-dino

To train an open-set object detection model using PyTorch, you can configure and run the Grounding DINO training process by specifying your image directories and epoch count. This automates parameter tuning, dataset preparation, and monitoring for your custom visual and textual data.

Open-set object detection identifies objects from a diverse set of images without needing predefined classes. You need Grounding DINO when your model must combine visual and textual information to detect dynamic or unseen objects during inference.

Yes, you need Docker and the NVIDIA Container Toolkit to run Grounding DINO training. This environment setup is required to execute the deep learning framework and handle the underlying PyTorch and ONNX dependencies for model development.

You can export a trained Grounding DINO model to ONNX for inference by using the deployment options provided in the framework. This handles the model configuration and evaluation, outputting the ONNX file for your open-set object detection deployment.

Yes, TorchScript is supported as a dependency for deploying your open-set object detection model. Alongside ONNX, you can utilize TorchScript to export your trained Grounding DINO model for efficient inference and evaluation.