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.
Profile →npx skills add alirezarezvani/claude-skills --skill senior-computer-vision --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Senior Computer Vision Engineer Production computer vision engineering skill for object detection, image segmentation, and visual AI system deployment. ## Table of Contents - [Quick Start](#quick-start) - [Core Expertise](#core-expertise) - [Tech Stack](#tech-stack) - [Workflow 1: Object Detection Pipeline](#workflow-1-object-detection-pipeline) - [Workflow 2: Model Optimization and Deployment](#workflow-2-model-optimization-and-deployment) - [Workflow 3: Custom Dataset Preparation](#workflow-3-custom-dataset-preparation) - [Architecture Selection Guide](#architecture-selection-guide) - [Reference Documentation](#reference-documentation) ## Quick Start ```bash # Generate training configuration for YOLO or Faster R-CNN python scripts/vision_model_trainer.py models/ --task detection --arch yolov8 # Analyze model for optimization opportunities (quantization, pruning) python scripts/inference_optimizer.py model.pt --target onnx --benchmark # Build dataset pipeline with augmentations python scripts/dataset_pipeline_builder.py images/ --format coco --augment ``` ## Core Expertise This skill provides guidance on: - **Object Detection**: YOLO family (v5-v11), Faster R-CNN, DETR, RT-DETR
- Table of Contents
- Quick Start
- Core Expertise
- Tech Stack
- Workflow 1: Object Detection Pipeline
- Step 1: Define Detection Requirements
- Step 2: Select Detection Architecture
- Step 3: Prepare Dataset
- Step 4: Configure Training
- Step 5: Train and Validate
- Step 6: Evaluate Results
- Workflow 2: Model Optimization and Deployment
- Step 1: Benchmark Baseline Performance
- Step 2: Select Optimization Strategy
Generate training configuration for YOLO or Faster R-CNN
python scripts/vision_model_trainer.py models/ --task detection --arch yolov8
Analyze model for optimization opportunities (quantization, pruning)
python scripts/inference_optimizer.py model.pt --target onnx --benchmark
Build dataset pipeline with augmentations
python scripts/dataset_pipeline_builder.py images/ --format coco --augment
COCO format (recommended)
python scripts/dataset_pipeline_builder.py data/images/ \
Verify dataset
python -c "from pycocotools.coco import COCO; coco = COCO('data/coco/train.json'); print(f'Images: {len(coco.imgs)}, Categories: {len(coco.cats)}')"What does the senior-computer-vision skill do?
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.
How do I install it?
Run `npx skills add alirezarezvani/claude-skills --skill senior-computer-vision --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From alirezarezvani/claude-skills, a repository with 23,791 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.