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senior-computer-vision

Design, train, and deploy computer vision models with PyTorch and ONNX.

Updated Apr 2, 2026
Search Tags:#deployment#computer vision#object detection#video analysis#model optimization#semantic segmentation#instance segmentation
One-click install
npx skills add https://github.com/4lerman/text_evaluator --skill senior-computer-vision-4lerman
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: senior-computer-vision
Source: https://github.com/4lerman/text_evaluator/tree/main/.agents/skills/engineering-skills/senior-computer-vision
Command: npx skills add https://github.com/4lerman/text_evaluator --skill senior-computer-vision-4lerman

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires pytorch, torchvision, ultralytics, detectron2, mmdetection, onnx, tensorrt, openvino, coreml, and includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill unit helps engineers build, optimize, and deploy advanced computer vision systems, covering a wide range of applications like object detection, image segmentation, and visual AI systems.

Core Features & Use Cases

  • Object Detection: Utilizes YOLO, Faster R-CNN, and DETR architectures for real-time object detection and tracking.
  • Instance Segmentation: Offers Mask R-CNN, YOLACT, and SAM for high-quality instance segmentation.
  • Semantic Segmentation: Employs DeepLabV3+, SegFormer, and SAM for semantic segmentation tasks.
  • Video Analysis: Provides tools for object tracking, action recognition, and 3D vision tasks.
  • Production Deployment: Facilitates deployment with ONNX, TensorRT, OpenVINO, CoreML, and others.
  • Use Case: Imagine you are developing a system for autonomous vehicles. This Skill unit can guide you through the process of selecting the right architecture, optimizing your model for real-time inference, and deploying it on edge devices.

Quick Start

To get started with object detection, run the following command:

python scripts/vision_model_trainer.py models/ --task detection --arch yolov8

Frequently Asked Questions about senior-computer-vision

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

FAQPage Schema
How do I optimize and deploy computer vision models for real-time inference?▼

To optimize and deploy computer vision models for real-time inference, you can utilize this Skill's scripts to convert trained PyTorch architectures into optimized formats using TensorRT, ONNX, OpenVINO, and CoreML for edge devices.

What's the best way to train YOLO or Faster R-CNN for object detection?▼

The best way to train YOLO or Faster R-CNN for object detection is using provided training scripts, such as running the vision model trainer with specific task and architecture arguments to leverage Ultralytics and MMDetection frameworks.

Can I use Mask R-CNN and SAM for instance segmentation tasks?▼

Yes, you can use Mask R-CNN, YOLACT, and SAM for instance segmentation tasks. This Skill provides comprehensive guides and scripts to implement these architectures for high-quality image segmentation using PyTorch and Detectron2.

Does this support semantic segmentation with DeepLabV3+ and SegFormer?▼

Yes, this supports semantic segmentation with DeepLabV3+ and SegFormer. It employs these architectures alongside SAM to perform detailed semantic segmentation tasks for advanced computer vision systems.

How does video analysis work for object tracking and action recognition?▼

Video analysis for object tracking and action recognition works by applying advanced computer vision architectures to sequential frames. This Skill provides specialized tools to process video streams for tracking and 3D vision tasks.

Do I need PyTorch and Detectron2 to build advanced computer vision systems?▼

Yes, you need PyTorch and Detectron2 alongside torchvision, Ultralytics, and MMDetection to build advanced computer vision systems. This Skill requires knowledge of these deep learning frameworks for model design and training.

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