Custom Vision

Train custom vision models with your labeled images and use them via REST APIs
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Custom Vision is a Microsoft Cognitive Service that helps you build tailored computer vision models using your own images, without having to create an entire machine learning pipeline from scratch. Instead of relying only on generic image recognition, you can train a model to recognize the exact objects or categories that matter to your business—your products, parts, packaging, equipment, or any domain-specific visual labels.

The workflow is straightforward: you collect and upload images, assign labels to them, and train a model directly in the Custom Vision service. After training, you can evaluate performance and iterate by adding more examples where the model struggles. Once you’re satisfied, you can use simple REST API calls to analyze new images. The service returns predictions (tags and confidence scores) so your application can automatically classify images or detect items based on the custom model you trained.

Custom Vision is designed for practical integration into apps and services. Because it’s API-driven, teams can connect it to websites, mobile apps, internal tools, or automated processes that need fast visual understanding. It is well suited for teams that want modern vision capabilities while keeping control over the labels, training data, and the specific recognition task.

Support is available through Microsoft’s Custom Vision team at [email protected], and users can sign in and manage projects at https://www.customvision.ai/account/signin.

Review summary

Features

  • Train custom image classification models with labeled images
  • Build custom object detection models for locating items in images
  • REST API access for tagging/classifying images with trained models
  • Iterative improvement by retraining with additional labeled examples
  • Easy project management and model training through the Custom Vision portal

How It’s Used

  • Object detection in images (e.g., finding specific items or components)
  • Image classification using custom labels (e.g., categorizing products or conditions)
  • Automated visual inspection workflows using domain-specific recognition
  • Content tagging and organization based on organization-defined categories

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