senior-computer-vision

Build an end-to-end computer vision pipeline by automating dataset preparation, training configuration generation, and model benchmarking. This workflow supports object detection and image segmentation tasks using frameworks like YOLO, Detectron2, and MMDetection.

Yes, you can deploy PyTorch computer vision models using ONNX. The workflow supports cross-framework deployment via ONNX and onnxruntime, allowing you to export trained models and optimize inference for production environments.

Yes, this workflow supports instance segmentation with Mask R-CNN and SAM. It provides production-ready workflows for both object detection and instance segmentation model development, benchmarking, and deployment.

The best way to automate dataset preparation for image segmentation is using a dedicated dataset pipeline builder script. It assembles your computer vision model workflow by structuring training data inputs before model training and analysis.

You optimize inference for production computer vision models by running an inference optimizer script. This analyzes your trained model and prepares it for cross-framework deployment using ONNX and onnxruntime to ensure scalable performance.

Yes, you need PyTorch and YAML to configure computer vision model training. The workflow generates training configurations in YAML and relies on PyTorch to execute the model training scripts for your object detection or segmentation tasks.