Amazon Rekognition is a service that makes it easy to add machine learning-based visual analysis to your applications. With Rekognition, you can identify objects, people, text, scenes, and activities in images and videos, as well as detect any inappropriate content. Amazon Rekognition also provides highly accurate facial analysis and facial recognition. You can use these capabilities to build innovative applications that supercharge your image analysis workflow.
Transfer learning is a machine learning technique that allows you to reuse a pre-trained model on a new dataset. This can be incredibly useful if you don’t have the time or resources to train a model from scratch.
So, how good is Amazon Rekognition for transfer learning? In general, Amazon Rekognition does a pretty good job with transfer learning. However, there are a few things to keep in mind. First, the pre-trained models that Rekognition uses are not public. This means that you can’t inspect them or fine-tune them to your specific needs. Second, Rekognition only supports a limited number of transfer learning tasks. Currently, you can only use Rekognition for image classification and object detection.
Overall, Amazon Rekognition is a good option for transfer learning if you’re looking for a quick and easy way to get started with visual recognition. However, if you need more control over your models or want to use transfer learning for more advanced tasks, you may want to consider other options.