Senior Computer Vision
Computer vision engineering skill for object detection, image segmentation, and visual AI systems.
Before & after
Same prompt, same agent — the only difference is the skill.
Object detection is a solid use case for computer vision. There are several approaches you could take. Deep learning frameworks like PyTorch are popular. You might want to look at using pre-trained models since training from scratch can be expensive. YOLO is a common choice, and there are also models like Faster R-CNN. For your product images, you could fine-tune an existing model. This typically involves getting labeled data, setting up a training pipeline, and then evaluating performance. Would you like help thinking through the setup process?
ARCHITECTURE RECOMMENDATION MODEL SELECTION • Framework: PyTorch + Ultralytics (YOLO v8) • Reasoning: Real-time inference (60+ fps), accurate for product shapes, lower compute cost than Faster R-CNN • Alternative: Faster R-CNN if accuracy over speed matters more TRAINING PIPELINE OUTLINE 1. Prepare dataset (minimum 1000 labeled product images) - Use Roboflow or similar for annotation if raw data unlabeled - Split: 70% train, 15% val, 15% test 2. Fine-tune YOLO v8 on products (starting from COCO-pretrained weights) - Epochs: 50 (with early stopping) - Batch size: 16 - Data augmentation: rotation, brightness, slight scaling 3. Evaluate: mAP50 target minimum 0.85 for production PRODUCTION DEPLOYMENT • Docker container with PyTorch ONNX-optimized model • API endpoint (FastAPI): POST image → JSON bounding boxes • Latency target: 100-200ms per image • GPU: single A100 or 2x A10 handles typical e-commerce throughput COMON PITFALLS • Poor lighting consistency between training and catalog = accuracy drop • Class imbalance (many T-shirts, few rare items) → use weighted sampling • Small products lose detail → increase input resolution to 1024x1024
About this skill
name: senior-computer-vision description: Use when Computer vision engineering skill for object detection, image segmentation, and visual AI systems.
Senior Computer Vision
Computer vision engineering skill for object detection, image segmentation, and visual AI systems. Covers CNN and Vision Transformer architectures, YOLO/Faster R-CNN/DETR detection, Mask R-CNN/SAM segmentation, and production deployment with ONNX/TensorRT. Includes PyTorch, torchvision, Ultralytics, Detectron2, and MMDetection frameworks. Use when building detection pipelines, training custom models, optimizing inference, or deploying vision systems.
What you get
- Public GitHub repo (alirezarezvani/claude-skills)
- the senior-computer-vision skill folder with SKILL.md. Part of a 337-skill / 30-agent / 70-command install.
Customize your output
- Fork the repo and adapt the skill's instructions and references to your workflow.
Example output
Activates automatically when your request matches Senior Computer Vision; chains with the other skills, agents, and commands in the collection.
Best for
Creators, builders, and teams using Claude Code.
SKILL.md preview
---
name: senior-computer-vision
description: Use this skill when building, training, or deploying computer vision systems such as object detection, image segmentation, or other visual AI pipelines.
version: 1.0.0
category: Development / Engineering
author: AgentVolt
license: proprietary
tags:
- development
- engineering
---
# Senior Computer Vision
Provides senior-level computer vision engineering guidance covering architecture selection, training, and production deployment of detection and segmentation systems.
## When to use
… (sign up to view the full skill)More development skills
View all Development skills →Generate
Scaffold code, tests, and boilerplate from a short spec.
Performance Profiler
Systematic performance profiling for Node.js, Python, and Go applications.
Threat Detection
Use when hunting for threats in an environment, analyzing IOCs, or detecting behavioral anomalies in telemetry.
Agent Harness
Turns a domain folder of skills into a bounded agentic loop: compile a goal into a task plan, execute with the domain's tools, verify every task, retry within a budget.