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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

A fast object detection algorithm – YOLO

While the acronym may make you smile, YOLO is one of the fastest object detection algorithms available. The latest version, YOLOv3, can run at more than 170 frames per second (FPS) on a modern GPU for an image size of 256 × 256. In this section, we will introduce the theoretical concept behind its architecture.

Introducing YOLO

First released in 2015, YOLO outperformed almost all other object detection architectures, both in terms of speed and accuracy. Since then, the architecture has been improved several times. In this chapter, we will draw our knowledge from the following three papers:

  • You Only Look Once: Unified, real-time object detection (2015), Joseph Redmon, Santosh...
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