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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

Improving image resolution with deep learning

Convolutional Neural Networks (CNNs) can also be used to improve the resolution of low-quality images. Historically, we can achieve this by using interpolation techniques, example-based approaches, or low- to high-resolution mappings that must be learned.

As we'll see in this recipe, we can obtain better results faster by using an end-to-end deep learning-based approach.

Sound interesting? Let's get to it!

Getting ready

We will need Pillow in this recipe, which you can install with the following command:

$> pip install Pillow

In this recipe, we are using the Dog and Cat Detection dataset, which is hosted on Kaggle: https://www.kaggle.com/andrewmvd/dog-and-cat-detection. In order to download it, you'll need to sign in on the website or sign up. Once you're logged in, save it in a place of your preference as dogscats.zip. Finally, decompress it in a folder named dogscats. From now on, we'll...

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