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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

Translating images with Pix2Pix

One of the most interesting applications of GANs is image-to-image translation, which, as the name suggests, consists of translating the content from one image domain to another (for instance, sketches to photos, black and white images to RGB, and Google Maps to satellite views, among others).

In this recipe, we'll implement a fairly complex conditional adversarial network known as Pix2Pix. We'll focus solely on the practical aspects of the solution, but if you want to get familiar with the literature, check out the See also section at the end of the recipe.

Getting ready

We'll use the cityscapes dataset, which is available here: https://people.eecs.berkeley.edu/~tinghuiz/projects/pix2pix/datasets/cityscapes.tar.gz. Download it and decompress it in a location of your choosing. For the purposes of this tutorial, we will assume that it's placed in the ~/.keras/datasets directory, under the name cityscapes. To display a progress...

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