Book Image

Computer Vision on AWS

By : Lauren Mullennex, Nate Bachmeier, Jay Rao
Book Image

Computer Vision on AWS

By: Lauren Mullennex, Nate Bachmeier, Jay Rao

Overview of this book

Computer vision (CV) is a field of artificial intelligence that helps transform visual data into actionable insights to solve a wide range of business challenges. This book provides prescriptive guidance to anyone looking to learn how to approach CV problems for quickly building and deploying production-ready models. You’ll begin by exploring the applications of CV and the features of Amazon Rekognition and Amazon Lookout for Vision. The book will then walk you through real-world use cases such as identity verification, real-time video analysis, content moderation, and detecting manufacturing defects that’ll enable you to understand how to implement AWS AI/ML services. As you make progress, you'll also use Amazon SageMaker for data annotation, training, and deploying CV models. In the concluding chapters, you'll work with practical code examples, and discover best practices and design principles for scaling, reducing cost, improving the security posture, and mitigating bias of CV workloads. By the end of this AWS book, you'll be able to accelerate your business outcomes by building and implementing CV into your production environments with the help of AWS AI/ML services.
Table of Contents (21 chapters)
Part 1: Introduction to CV on AWS and Amazon Rekognition
Part 2: Applying CV to Real-World Use Cases
Part 3: CV at the edge
Part 4: Building CV Solutions with Amazon SageMaker
Part 5: Best Practices for Production-Ready CV Workloads

Creating a model using Amazon Lookout for Vision

In this section, we will learn how to choose the type of model you need for your use case, how to create training and test datasets, start model training, improve your model, and analyze images using the trained model.

Choosing the model type based on your business goals

Similar to Rekognition Custom Labels, Lookout for Vision provides a couple of choices in choosing the model type: image classification and image segmentation:

  • The image classification model: This type of model predicts whether the image contains an anomaly or not. It will not provide other information, such as the location of the anomaly or the type of anomaly. An example of this is if you want to know whether a capsule shell is mashed or cracked on an assembly line. An image classification model would be a good choice for this use case. As capsule shells are relatively cheap, you may not want to investigate the location of mashing or cracks.
  • The image...