senior-computer-vision

To deploy a PyTorch object detection model, you export the trained architecture to ONNX format and optimize it for inference environments. This Skill automates building detection pipelines, training custom models, and deploying vision systems across edge and cloud using ONNX and TensorRT.

Yes, you can train a custom YOLOv8 model and prepare it for edge deployment. The Skill supports CNN and Vision Transformer architectures like YOLO, enabling end-to-end pipelines from dataset preparation to optimization and deployment across edge and cloud environments.

For image segmentation tasks, supported computer vision architectures include Mask R-CNN, DETR, and SAM. The Skill applies these Vision Transformer and CNN models to build segmentation pipelines and automate production-grade vision engineering.

You need ONNX and TensorRT toolchains to optimize your computer vision pipeline for production deployment. These dependencies are required alongside PyTorch and major CV frameworks like Ultralytics, Detectron2, and MMDetection to deploy vision systems efficiently.

The best way to build an end-to-end detection pipeline is automating dataset preparation, model training, and optimization. This Skill facilitates production-grade computer vision engineering, applying frameworks like Ultralytics and deploying optimized models via ONNX and TensorRT.

Yes, this Skill supports both CNN and Vision Transformer architectures for production deployment. It applies models like Faster R-CNN, DETR, and SAM to build detection and segmentation pipelines, ensuring vision systems are optimized for edge and cloud environments.