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Machine Learning with Core ML

You're reading from   Machine Learning with Core ML An iOS developer's guide to implementing machine learning in mobile apps

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Product type Paperback
Published in Jun 2018
Publisher Packt
ISBN-13 9781788838290
Length 378 pages
Edition 1st Edition
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Author (1):
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Joshua Newnham Joshua Newnham
Author Profile Icon Joshua Newnham
Joshua Newnham
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Table of Contents (12) Chapters Close

Preface 1. Introduction to Machine Learning 2. Introduction to Apple Core ML FREE CHAPTER 3. Recognizing Objects in the World 4. Emotion Detection with CNNs 5. Locating Objects in the World 6. Creating Art with Style Transfer 7. Assisted Drawing with CNNs 8. Assisted Drawing with RNNs 9. Object Segmentation Using CNNs 10. An Introduction to Create ML 11. Other Books You May Enjoy

Data to drive the desired effect – action shots


Now would be a good time to introduce the photo effect we want to create in this chapter. The effect, as I know it, is called an action shot. It's essentially a still photograph that shows someone (or something) in motion, probably best illustrated with an image - like the one shown here: 

 

Note

As previously mentioned, the model we used in this chapter performs binary (or single-class) classification. This simplification, using a binary classifier instead of a multi-class classifier, has been driven by the intended use that is just segmenting people from the background. Similar to any software project, you should strive for simplicity where you can.

To extract people, we need a model to learn how to recognize people and their associated pixels. For this, we need a dataset consisting of images of people and corresponding images with those pixels of the persons labeled—and lots of them. Unlike datasets for classification, datasets for object segmentation...

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