Use Amazon Augmented AI with Amazon Rekognition - Amazon SageMaker

Use Amazon Augmented AI with Amazon Rekognition

Amazon Rekognition makes it easy to add image analysis to your applications. The Amazon Rekognition DetectModerationLabels API operation is directly integrated with Amazon A2I so that you can easily create a human loop to review unsafe images, such as explicit adult or violent content. You can use DetectModerationLabels to configure a human loop using a flow definition ARN. This enables Amazon A2I to analyze predictions made by Amazon Rekognition and send results to a human for review to ensure they meet the conditions set in your flow definition.

The following image depicts the Amazon A2I built-in workflow with Amazon Rekognition. On the left, the resources that are required to create an Amazon Rekognition human review workflow are depicted: and Amazon S3 bucket, activation conditions, a worker task template, and a work team. These resources are used to create a human review workflow, or flow definition. An arrow points right to the next step in the workflow: using Amazon Rekognition to configure a human loop with the human review workflow. A second arrow points right from this step to the step in which activation conditions specified in the human review workflow are met. This initiates the creation of a human loop. On the right of the image, the human loop is depicted in three steps: 1) the worker UI and tools are generated and the task is made available to workers, 2) workers review input data, and finally, 3) results are saved in Amazon S3.

Use Amazon Augmented AI with Amazon Rekognition

You can set the following activation conditions when using the Amazon Rekognition task type:

  • Initiate human review for labels identified by Amazon Rekognition based on the label confidence score.

  • Randomly send a sample of images to humans for review.

You can set these activation conditions using the Amazon SageMaker console when you create a human review workflow, or by creating a JSON for human loop activation conditions and specifying this as input in the HumanLoopActivationConditions parameter of the CreateFlowDefinition API operation. To learn how specify activation conditions in JSON format, see JSON Schema for Human Loop Activation Conditions in Amazon Augmented AI and Use Human Loop Activation Conditions JSON Schema with Amazon Rekognition.


When using Augmented AI with Amazon Rekognition, create Augmented AI resources in the same AWS Region you use to call DetectModerationLabels.

Get Started: Integrate a Human Review into an Amazon Rekognition Image Moderation Job

To integrate a human review into an Amazon Rekognition, see the following topics:

After you've created your flow definition, see Using Augmented AI with Amazon Rekognition to learn how to integrate your flow definition into your Amazon Rekognition task.

End-to-end Demo Using Amazon Rekognition and Amazon A2I

For an end-to-end example that demonstrates how to use Amazon Rekognition with Amazon A2I using the console, see Tutorial: Get Started in the Amazon A2I Console.

To learn how to use the Amazon A2I API to create and start a human review, you can use Amazon Augmented AI (Amazon A2I) integration with Amazon Rekognition [Example] in a SageMaker notebook instance. To get started, see Use SageMaker Notebook Instance with Amazon A2I Jupyter Notebook.

A2I Rekognition Worker Console Preview

When they're assigned a review task in an Amazon Rekognition workflow, workers might see a user interface similar to the following:

You can customize this interface in the SageMaker console when you create your human review definition, or by creating and using a custom template. To learn more, see Create and Manage Worker Task Templates.