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Hands-On Deep Learning Architectures with Python

You're reading from  Hands-On Deep Learning Architectures with Python

Product type Book
Published in Apr 2019
Publisher Packt
ISBN-13 9781788998086
Pages 316 pages
Edition 1st Edition
Languages
Authors (2):
Yuxi (Hayden) Liu Yuxi (Hayden) Liu
Profile icon Yuxi (Hayden) Liu
Saransh Mehta Saransh Mehta
Profile icon Saransh Mehta
View More author details
Toc

Table of Contents (15) Chapters close

Preface 1. Section 1: The Elements of Deep Learning
2. Getting Started with Deep Learning 3. Deep Feedforward Networks 4. Restricted Boltzmann Machines and Autoencoders 5. Section 2: Convolutional Neural Networks
6. CNN Architecture 7. Mobile Neural Networks and CNNs 8. Section 3: Sequence Modeling
9. Recurrent Neural Networks 10. Section 4: Generative Adversarial Networks (GANs)
11. Generative Adversarial Networks 12. Section 5: The Future of Deep Learning and Advanced Artificial Intelligence
13. New Trends of Deep Learning 14. Other Books You May Enjoy

SSD MobileNetV2

The makers of MobileNetV2 also made real-time object detection possible for mobile devices. They introduced a combination of the SSD Object Detector and MobileNetV2, which is called SSDLite. Remember that in Chapter 4CNN Architecture, we used ssd_mobilenetv2 for object detection. It is the same as SSDLite. The reason for choosing SSD is quite simple. SSD is built independent of the base network and hence the convolutions are replaced by depth-wise separable convolution. The first layer of SSDLite is attached to the expansion of layer 15 of MobileNetV2. Replacing standard convolutions with depth-wise separable convolution significantly reduces the number of parameters that are required by the network for object detection.

The following table shows a comparison of the number of parameters and multiplication operations required by the original SSD network...

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