senior-computer-vision

To deploy a PyTorch computer vision model to production, export trained architectures to ONNX, TensorRT, or TorchScript formats for scalable inference. This workflow covers model selection, dataset preparation, and optimized deployment across edge devices or cloud GPUs.

Configure training for object detection and segmentation by generating YAML training configurations tailored to your CV dataset. Specify architectures like YOLOv8m, set epoch counts, and leverage PyTorch-based stacks such as Ultralytics, Detectron2, or MMDetection for real-world pipelines.

Yes, the computer vision workflow supports both edge devices and cloud GPUs by providing reference architectures and deployment tooling. It uses optimized models through OpenVINO and TensorRT, ensuring validated data pipelines run efficiently across different hardware environments.

Yes, you can use Ultralytics and Detectron2 to build production-grade vision pipelines. The workflow provides reference architectures and tooling for these PyTorch-based stacks, covering object detection, instance segmentation, and semantic segmentation guidance for real-world applications.

To scale computer vision solutions end-to-end, you need dependencies including numpy, opencv-python, torch, onnxruntime, and yaml. These libraries support dataset preparation, PyTorch model training, and optimized ONNX inference deployment for production-grade vision systems.