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

Validating it works

Once you see the status model changed to Training complete, select on the model name (Model 1). You will see performance metrics and an overview of the test results. As you can see in the following screenshot, our trained model has a really good F1 score, precision, and recall:

Figure 7.12: Evaluating the model performance

Figure 7.12: Evaluating the model performance

Step 1 – trial detection

As we previously discussed, Lookout for Vision allows you to create trial detection tasks and verify results. Afterward, if needed, you can retrain your model to improve the results.

Let’s upload images for trial detection from the book’s GitHub repository. You can complete this step using the following command:

$ aws s3 sync 07_LookoutForVision/trial_detection s3://cv-on-aws-book-xxxx/chapter_07/trial_detection --region us-east-2

Select on Trial detections on the pane on the left, and then Select Run trial detection:

Figure 7.13: Run trial detection (1)

Figure 7.13...