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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

Replacing large convolutions with multiple smaller ones

The authors began with a simple observation—a stack of two convolutions with 3 × 3 kernels has the same receptive field as a convolution with 5 × 5 kernels (refer to Chapter 3, Modern Neural Networks, for the effective receptive field (ERF) formula).

Similarly, three consecutive 3 × 3 convolutions result in a 7 × 7 receptive field, and five 3 × 3 operations result in an 11 × 11 receptive field. Therefore, while AlexNet has large filters (up to 11 × 11), the VGG network contains more numerous but smaller convolutions for a larger ERF. The benefits of this change are twofold:

  • It decreases the number of parameters: Indeed, the N filters of an 11 × 11 convolution layer imply 11 × 11 × D × N = 121DN values to train just for their kernels (for an input of depth D), while five 3 × 3 convolutions have a total of 1 ×...
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