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Tonybleything76
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senior-computer-vision

Develop and deploy computer vision models using YOLO, ONNX, and TensorRT.

Updated Mar 4, 2026
Search Tags:#deep learning#AI#model deployment#computer vision#object detection#image segmentation
One-click install
npx skills add https://github.com/Tonybleything76/more-claude-skills --skill senior-computer-vision-tonybleything76
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: senior-computer-vision
Source: https://github.com/Tonybleything76/more-claude-skills/tree/main/engineering-team/senior-computer-vision
Command: npx skills add https://github.com/Tonybleything76/more-claude-skills --skill senior-computer-vision-tonybleything76

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires torch, torchvision, ultralytics, onnx, tensorrt, opencv-python, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This skill addresses the complex challenges of building, optimizing, and deploying computer vision models for tasks like object detection and image segmentation.

Core Features & Use Cases

  • Model Development: Guides through selecting and training state-of-the-art architectures (YOLO, Faster R-CNN, SAM).
  • Performance Optimization: Provides workflows for exporting models to ONNX, TensorRT, and CoreML, including quantization.
  • Use Case: Deploy a real-time object detection system for autonomous vehicles by training a YOLOv8 model, optimizing it with TensorRT, and integrating it into the vehicle's software stack.

Quick Start

Use the senior computer vision skill to generate a training configuration for YOLOv8 detection models.

Frequently Asked Questions about senior-computer-vision

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I export and optimize a YOLOv8 model with TensorRT for production deployment?▼

To optimize YOLOv8 models for production deployment, you can export them to ONNX and subsequently convert them to TensorRT. This workflow includes quantization steps to accelerate visual AI inference on compatible hardware.

What computer vision architectures are supported for object detection and image segmentation?▼

Supported computer vision architectures include CNN and Vision Transformer models such as YOLO, Faster R-CNN, DETR, Mask R-CNN, and SAM. These cover advanced object detection and image segmentation tasks.

Can I use OpenCV with TensorRT models for real-time visual AI inference?▼

Yes, OpenCV can be integrated with TensorRT models to process video streams and perform real-time visual AI inference. The skill provides workflows for deploying optimized models into software stacks using these dependencies.

What is the best way to train an object detection model for autonomous vehicle systems?▼

Training an object detection system for autonomous vehicles involves selecting an architecture like YOLOv8, generating a training configuration, and optimizing the resulting model. The skill provides end-to-end guidance for this entire process.

Do I need PyTorch and torchvision installed to build computer vision models here?▼

Yes, PyTorch and torchvision are required dependencies for building and training computer vision models. The skill utilizes these frameworks alongside ultralytics to develop object detection and image segmentation systems.

When should I choose ONNX export over direct TensorRT deployment for my vision model?▼

ONNX export serves as an intermediate representation before converting to TensorRT for hardware-specific acceleration. You should use ONNX when you need framework interoperability or plan to deploy across multiple inference engines.

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