You have a factory that produces cardboard packaging for food products.

The model must use object detection because the solution needs more than an overall classification. It must identify individual defects, provide their locations, and count individual product samples. Object-detection predictions contain bounding-box coordinates in addition to labels and confidence scores, allowing the application to determine where a defect occurred and whether exactly four product instances are present.

The appropriate domain is General (compact) . The packaging and products are organization-specific and do not map to a more specialized object-detection domain. More importantly, a compact domain supports model export for edge/local inference . This matters because the factory has intermittent internet connectivity; the exported model can execute locally rather than depending continuously on a hosted Azure prediction endpoint.

Microsoft documents compact domains as optimized for deployment outside the Custom Vision hosted service. Exportable formats can include ONNX, TensorFlow, TensorFlow Lite, and other target-specific formats depending on project type and domain.

An ordinary General-domain model is intended for Azure-hosted prediction and cannot satisfy the same offline export requirement. Image classification is also unsuitable because it cannot return defect locations or count independently detected products.

Study Guide alignment: identify objects/components/regions and deploy vision functionality appropriate for edge scenarios . Microsoft Learn

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