Custom Vision

Teams typically use Custom Vision by starting with a small set of real images from their own environment, then turning those examples into a model that can be called from an app or workflow. Photos can come from a phone, a production camera, a warehouse scanner, or uploaded files from a website. After images are labeled to match the business categories that matter, the model is trained and tested, then improved by adding more samples from the situations where results are uncertain.

In day-to-day use, the trained model is placed behind a simple API call so software can make decisions from new images. A web form can auto-sort user uploads, a mobile app can confirm the right item is in view, and an internal tool can flag mismatched packaging or missing components. In quality checks, staff can capture a photo and get an immediate prediction that routes the item to the correct next step. In inventory or logistics, incoming images can be tagged automatically so records update without manual data entry.

Iteration is part of the workflow: when the service returns low confidence or incorrect predictions, those images are saved, labeled, and added back into training to reduce future errors. This fits into continuous improvement cycles where teams retrain on new product versions, seasonal packaging changes, or new defect types. Project management and model updates are handled through the Custom Vision portal, and support is available via [email protected].