5.2. Implement Custom Vision Models

💡 First Principle: Custom Vision bridges the gap between powerful pre-built models and your specific domain needs. When Azure AI Vision's general models don't recognize your products, equipment, or defects, Custom Vision lets you train a model on your own images. The trade-off: you gain specificity but take on the responsibility of training data quality.

When to use Custom Vision vs. Azure AI Vision:

• Azure AI Vision: General objects, scenes, text, faces
• Custom Vision: Your specific products, brand logos, defect types, domain-specific categories

What breaks without proper training data: Garbage in, garbage out. A model trained on 5 blurry images per class will perform poorly. You need 15+ high-quality, varied images per class for production-quality models.

Project types: Choose Classification (label the whole image) or Object Detection (locate and label objects within the image) based on whether position matters.