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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

On-device computer vision particularities

When running computer vision models on mobile devices, the focus switches from raw performance metrics to user experience. On mobile phones, this means minimizing battery and disk usage: we don't want to drain the phone's battery in minutes or fill up all the available space on the device. When running on mobile, it is recommended to use smaller models. As they contain fewer parameters, they use less disk space. Moreover, as they require fewer operations, this leads to reduced battery usage.

Another particularity of mobile phones is orientation. In training datasets, most pictures are provided with the correct orientation. While we sometimes change this orientation during data augmentation, the images are rarely upside down or completely sideways. However, there are many ways to hold a mobile phone. For this reason, we must monitor the device's orientation to make sure that we are feeding the model with images that are correctly...

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