senior-computer-vision

To deploy a PyTorch computer vision model to ONNX or TensorRT, you generate a training configuration and use deployment templates that export and optimize your architecture for production-grade inference backends.

Building an end-to-end object detection pipeline involves dataset preparation, generating training configurations for CNNs or Vision Transformers, benchmarking, and exporting to deployment backends like OpenVINO or ONNX.

Yes, the computer vision workflow supports image segmentation and zero-shot tasks by providing architecture guidance and training configuration generation across both CNN and Vision Transformer models.

You can use Ultralytics and Detectron2 for rapid prototyping and training, then transition to production model deployment using supported backends like ONNX, TensorRT, and OpenVINO.

You generate training configurations for Vision Transformers and CNNs by selecting a target architecture for detection or segmentation, which streamlines the setup for standard CV frameworks like MMDetection.

To run computer vision model benchmarking and deployment, you need dependencies including PyTorch, numpy, opencv-python, ONNX, onnxruntime, and pyyaml for processing and configuration.