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)
1
Part 1: Introduction to CV on AWS and Amazon Rekognition
5
Part 2: Applying CV to Real-World Use Cases
9
Part 3: CV at the edge
12
Part 4: Building CV Solutions with Amazon SageMaker
15
Part 5: Best Practices for Production-Ready CV Workloads

Validating that the model works

Once the model training finishes, you will see the performance of the trained model in the Evaluation tab. As you can see in the screenshot here, our trained model has really good precision and recall and a good F1 score for the packt label (with just eight training images):

Figure 3.13: Evaluating model performance

Figure 3.13: Evaluating model performance

If you’d like to review how the model performed on your test dataset, select on View test results.

Step 1 – Starting your model

The next step is to start the model so that we can start using it to detect Packt’s logo in images. You can go to the Use model tab and select on Start:

Figure 3.14: Starting the model using the Rekognition Custom Labels console

Figure 3.14: Starting the model using the Rekognition Custom Labels console

You will also need to select the number of inference units when you start your model— you will be charged for each inference unit.

Important note

A higher number of inference units will increase...