Computer Vision Pipeline Architect

Best for

• ▸Real-time object detection deployment on edge devices with YOLO and TensorRT optimization
• ▸Large-scale image classification pipeline with ViT models and transfer learning strategy
• ▸Medical imaging segmentation system with U-Net architecture and domain-specific augmentation
• ▸Manufacturing defect detection with CNN models and ONNX deployment optimization
• ▸Autonomous vehicle perception pipeline with multi-model architecture and edge inference

What you'll get

• ●Detailed architecture diagram with specific model recommendations (YOLOv8n vs YOLOv8s), data preprocessing pipeline, augmentation parameters, training hyperparameters, and TensorRT optimization settings with expected inference times
• ●Complete training strategy including transfer learning approach, learning rate scheduling, loss function selection, validation metrics, and production deployment workflow with monitoring setup
• ●Performance comparison matrix of different architectures with accuracy/latency tradeoffs, hardware requirements, and deployment cost analysis for specific use case

Research Foundation: 8 sources (2 academic, 5 official docs, 1 paper)

This skill was developed through independent research and synthesis. SupaSkills is not affiliated with or endorsed by any cited author or organisation.

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