senior-computer-vision

Deploy a PyTorch object detection model to production by exporting it to ONNX, applying TensorRT quantization for optimization, and serving it via an ONNX Runtime server as an API service.

Build an end-to-end image segmentation pipeline by preparing datasets, selecting models via MMDetection or Detectron2, training, optimizing with quantization, and deploying the inference service to a runtime server.

TensorRT optimization for computer vision models works by converting trained PyTorch networks into optimized inference graphs, reducing latency through quantization before final ONNX deployment.

Yes, you can use MMDetection for both instance and semantic segmentation tasks. The framework provides robust PyTorch-based architectures to train and optimize models for deployment workflows.

You need ONNX to serve computer vision models in production environments because it bridges PyTorch training and runtime servers, enabling hardware-agnostic deployment and TensorRT optimization.

Computer vision training pipelines often fail during dataset preparation due to misaligned annotations or incompatible formats, requiring strict validation before feeding data into Ultralytics or Detectron2.