If you're building object detection or segmentation systems, this gives you training configs for YOLO, Detectron2, and MMDetection, plus optimization workflows for ONNX, TensorRT, and OpenVINO. The three workflows cover detection pipeline setup (architecture selection, dataset prep, training), production optimization (benchmarking, quantization, runtime conversion with actual speedup expectations), and dataset preparation with format conversion. Useful when you need to go from raw images to a deployed model and want the boilerplate decisions made for you. The optimization workflow is the strong part, walking through FP32 to FP16 to INT8 with calibration steps and realistic performance targets.
npx -y skills add borghei/claude-skills --skill senior-computer-vision --agent claude-codeInstalls to .claude/skills
Select a file.
erichowens/some_claude_skills
github/awesome-copilot