Verified:GitHub

senior-computer-vision

Automate computer vision model development and deployment for detection and segmentation.

Updated Jun 2, 2026

Search Tags:#production-deployment #model-training #object-detection #computer-vision #dataset-pipeline #image-segmentation

Ready to run

npx skills add https://github.com/ano4l/SiteRent --skill senior-computer-vision-ano4l

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: senior-computer-vision
Source: https://github.com/ano4l/SiteRent/tree/main/skills/senior-computer-vision
Command: npx skills add https://github.com/ano4l/SiteRent --skill senior-computer-vision-ano4l

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires yaml, torch, onnx, onnxruntime, and includes scripts (resource) and references (resource) components.

What problem does it solve?

Automates end-to-end computer vision model development and deployment.

Core Features & Use Cases

Object detection and instance segmentation model development, benchmarking, and deployment.
Production-ready workflows using YOLO, DETR, Mask R-CNN, and SAM with cross-framework support.
Example: Build a scalable CV pipeline from data prep to model export and monitoring.

Quick Start

Run the vision_model_trainer.py, inference_optimizer.py, and dataset_pipeline_builder.py to train, optimize, and assemble your CV model workflow.

Frequently Asked Questions about senior-computer-vision

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

FAQPage Schema

How do I build an end-to-end computer vision pipeline for object detection?▼

Build an end-to-end computer vision pipeline by automating dataset preparation, training configuration generation, and model benchmarking. This workflow supports object detection and image segmentation tasks using frameworks like YOLO, Detectron2, and MMDetection.

Can I deploy PyTorch computer vision models using ONNX?▼

Yes, you can deploy PyTorch computer vision models using ONNX. The workflow supports cross-framework deployment via ONNX and onnxruntime, allowing you to export trained models and optimize inference for production environments.

Does this workflow support instance segmentation with Mask R-CNN and SAM?▼

Yes, this workflow supports instance segmentation with Mask R-CNN and SAM. It provides production-ready workflows for both object detection and instance segmentation model development, benchmarking, and deployment.

What is the best way to automate dataset preparation for image segmentation?▼

The best way to automate dataset preparation for image segmentation is using a dedicated dataset pipeline builder script. It assembles your computer vision model workflow by structuring training data inputs before model training and analysis.

How do I optimize inference for production computer vision models?▼

You optimize inference for production computer vision models by running an inference optimizer script. This analyzes your trained model and prepares it for cross-framework deployment using ONNX and onnxruntime to ensure scalable performance.

Do I need PyTorch and YAML to configure computer vision model training?▼

Yes, you need PyTorch and YAML to configure computer vision model training. The workflow generates training configurations in YAML and relies on PyTorch to execute the model training scripts for your object detection or segmentation tasks.