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Hands-On Computer Vision with TensorFlow 2

You're reading from   Hands-On Computer Vision with TensorFlow 2 Leverage deep learning to create powerful image processing apps with TensorFlow 2.0 and Keras

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Product type Paperback
Published in May 2019
Publisher Packt
ISBN-13 9781788830645
Length 372 pages
Edition 1st Edition
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Authors (2):
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Eliot Andres Eliot Andres
Author Profile Icon Eliot Andres
Eliot Andres
Benjamin Planche Benjamin Planche
Author Profile Icon Benjamin Planche
Benjamin Planche
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Table of Contents (16) Chapters Close

Preface 1. Section 1: TensorFlow 2 and Deep Learning Applied to Computer Vision FREE CHAPTER
2. Computer Vision and Neural Networks 3. TensorFlow Basics and Training a Model 4. Modern Neural Networks 5. Section 2: State-of-the-Art Solutions for Classic Recognition Problems
6. Influential Classification Tools 7. Object Detection Models 8. Enhancing and Segmenting Images 9. Section 3: Advanced Concepts and New Frontiers of Computer Vision
10. Training on Complex and Scarce Datasets 11. Video and Recurrent Neural Networks 12. Optimizing Models and Deploying on Mobile Devices 13. Migrating from TensorFlow 1 to TensorFlow 2 14. Assessments 15. Other Books You May Enjoy

Running on iOS devices using Core ML

With the release of its latest devices, Apple is putting the emphasis on machine learning. They designed a custom chip—the neural engine. This can achieve fast deep learning operations while maintaining a low power usage. To fully benefit from this chip, developers must use a set of official APIs called Core ML (refer to the documentation at https://developer.apple.com/documentation/coreml).

To use an existing model with Core ML, developers need to convert it to the .mlmodel format. Thankfully, Apple provides Python tools to convert from Keras or TensorFlow.

In addition to speed and energy efficiency, one of the strengths of Core ML is its integration with other iOS APIs. Powerful native methods exist for augmented reality, face detection, object tracking, and much more.

While TensorFlow Lite supports iOS, as of now, we still recommend using Core ML. This allows faster inference time and broader feature compatibility.
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