Amazon Rekognition - AWS Prescriptive Guidance

Amazon Rekognition

For image classification in the visible spectrum, models are frequently created with transfer learning and fine-tuning from a pretrained neural network. You can automate the task of network selection and training by using the Amazon Rekognition service.

Amazon Rekognition provides a standard set of classification labels. A label is an object or concept (including scenes and actions) found in an image or video based on its contents. For example, an image of people on a tropical beach may contain labels such as Palm Tree (object), Beach (scene), Running (action), and Outdoors (concept). For more information about the labels supported by Amazon Rekognition, see Detecting objects and concepts in the service documentation.

For tasks that require the standard labels in Amazon Rekognition, testing this service is worthwhile. If Amazon Rekognition can meet your requirements, the model selection, training, and maintenance are abstracted. It provides a pretrained service for inference, and AWS handles maintenance of the service. Obtaining predictions from Amazon Rekognition is straight-forward.

The following are the advantages of Amazon Rekognition:

  • Immediately available and scalable

  • No training or configuration required

  • Supports multi-label classification

The following are the disadvantages of Amazon Rekognition:

  • Fixed set of predicted classes

  • Inference units offer chunks of capacity, and the smallest unit might be costly for small throughput

For more information, see the following: