Creating and using adapters - Amazon Rekognition

Creating and using adapters

Adapters are modular components that can be added to the existing Rekognition deep learning model, extending its capabilities for the tasks it’s trained on. By training a deep learning model with adapters, you can achieve better accuracy for image analysis tasks related to your specific use case.

To create and use an adapter, you must provide training and testing data to Rekognition. You can accomplish this in one of two different ways:

  • Bulk analysis and verification - You can create a training dataset by bulk analyzing images that Rekognition will analyze and assign labels to. You can then review the generated annotations for your images and verify or correct the predictions. For more information on how the Bulk analysis of images works, see Bulk analysis.

  • Manual annotation - With this approach you create your training data by uploading and annotating images. You create your test data by either uploading and annotating images or by auto-splitting.

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