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TensorFlow 2.0 Computer Vision Cookbook

You're reading from   TensorFlow 2.0 Computer Vision Cookbook Implement machine learning solutions to overcome various computer vision challenges

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Product type Paperback
Published in Feb 2021
Publisher Packt
ISBN-13 9781838829131
Length 542 pages
Edition 1st Edition
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Author (1):
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Jesús Martínez Jesús Martínez
Author Profile Icon Jesús Martínez
Jesús Martínez
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Table of Contents (14) Chapters Close

Preface 1. Chapter 1: Getting Started with TensorFlow 2.x for Computer Vision 2. Chapter 2: Performing Image Classification FREE CHAPTER 3. Chapter 3: Harnessing the Power of Pre-Trained Networks with Transfer Learning 4. Chapter 4: Enhancing and Styling Images with DeepDream, Neural Style Transfer, and Image Super-Resolution 5. Chapter 5: Reducing Noise with Autoencoders 6. Chapter 6: Generative Models and Adversarial Attacks 7. Chapter 7: Captioning Images with CNNs and RNNs 8. Chapter 8: Fine-Grained Understanding of Images through Segmentation 9. Chapter 9: Localizing Elements in Images with Object Detection 10. Chapter 10: Applying the Power of Deep Learning to Videos 11. Chapter 11: Streamlining Network Implementation with AutoML 12. Chapter 12: Boosting Performance 13. Other Books You May Enjoy

Chapter 9: Localizing Elements in Images with Object Detection

Object detection is one of the most common yet challenging tasks in computer vision. It's a natural evolution of image classification, where our goal is to work out what is in an image. On the other hand, object detection is not only concerned with the content of an image but also with the location of elements of interest in a digital image.

As with many other well-known tasks in computer vision, object detection has long been addressed with a wide array of techniques, ranging from naïve solutions (such as object matching) to machine learning-based ones (such as Haar Cascades). Nonetheless, the most effective detectors nowadays are powered by deep learning.

Implementing state-of-the-art object detectors (such as You Only Look Once (YOLO) and Fast Region-based Convolutional Neural Network (Fast R-CNN) from scratch is a very challenging task. However, there are many pre-trained solutions we can leverage, not...

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