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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

1. Object detection

In object detection, the objective is to localize and identify an object in an image. Figure 11.1.1 shows object detection where the target is a Soda can. Localization means that the bounding box of the object must be estimated. Using upper left corner pixel and lower right corner pixel coordinates is a common convention that is used to describe a bounding box. In Figure 11.1.1, the upper left corner pixel has coordinates. (xmin,ymin), while the lower right corner pixel has coordinates (xmax,ymax).The pixel coordinate system has the origin (0,0) at the upper left corner pixel of the entire image.

While performing localization, detection must also identify the object. Identification is the classic recognition or classification task in computer vision. At the minimum, object detection must identify if a bounding box belongs to a known object or to the background. An object detection network can be trained to detect one specific object only, like the Soda can...

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