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

Importing the labeled data with Rekognition Custom Labels

Now we have labeled data, we can use it to train machine learning models with SageMaker or Rekognition Custom Labels. We will navigate to the Rekognition Custom Labels console (https://us-east-2.console.aws.amazon.com/rekognition/custom-labels#/).

Step 1 – create the project

Navigate to the Projects panel on the left sidebar and select on Create project. Give it a name, such as logo-detection.

Step 2 – create training and test datasets

Next, we need to create a dataset. Select on Create dataset. Select Start with a single dataset and then select Import images labeled by SageMaker Ground Truth under Training dataset details.

Figure 9.16: Creating a dataset in the Rekognition Custom Labels console

Figure 9.16: Creating a dataset in the Rekognition Custom Labels console

Provide the location of output manifest file, such as s3://cv-on-aws-book-xxxx/chapter_09/output_data/packt-logo-labeling/manifests/output/output.manifest, and select Create Dataset...