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Advanced Deep Learning with TensorFlow 2 and Keras

You're reading from   Advanced Deep Learning with TensorFlow 2 and Keras Apply DL, GANs, VAEs, deep RL, unsupervised learning, object detection and segmentation, and more

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Product type Paperback
Published in Feb 2020
Publisher Packt
ISBN-13 9781838821654
Length 512 pages
Edition 2nd Edition
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Author (1):
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Rowel Atienza Rowel Atienza
Author Profile Icon Rowel Atienza
Rowel Atienza
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Table of Contents (16) Chapters Close

Preface 1. Introducing Advanced Deep Learning with Keras 2. Deep Neural Networks FREE CHAPTER 3. Autoencoders 4. Generative Adversarial Networks (GANs) 5. Improved GANs 6. Disentangled Representation GANs 7. Cross-Domain GANs 8. Variational Autoencoders (VAEs) 9. Deep Reinforcement Learning 10. Policy Gradient Methods 11. Object Detection 12. Semantic Segmentation 13. Unsupervised Learning Using Mutual Information 14. Other Books You May Enjoy
15. Index

6. SSD model architecture in Keras

Unlike the code examples in the previous chapters, the tf.keras implementation of SSD is more involved. In comparison to other tf.keras implementations of SSD, the code example presented in this chapter focuses on explaining the key concepts of multi-scale object detection. Some parts of the code implementation can be further optimized such as caching of ground truth anchor boxes classes, offsets, and masks. In our example, the ground truth values are computed by a thread every time an image is loaded from the filesystem.

Figure 11.6.1 shows an overview of code blocks that comprise the tf.keras implementation of SSD. An SSD object in ssd-11.6.1.py builds, trains, and evaluates an SSD model. It sits on top of SSD model creator with the help of model.py and resnet.py and a multi-threaded data generator in data_generator.py. SSD model implements the SSD architecture as shown in Figure 11.5.1. The implementation of each major block will be discussed...

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