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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

Creating a basic image classifier

We'll close this chapter by implementing an image classifier on Fashion-MNIST, a popular alternative to mnist. This will help us consolidate the knowledge we've acquired from the previous recipes. If, at any point, you need more details on a particular step, please refer to the previous recipes.

Getting ready

I encourage you to complete the five previous recipes before tackling this one since our goal is to come full circle with the lessons we've learned throughout this chapter. Also, make sure you have Pillow and pydot on your system. You can install them using pip:

$> pip install Pillow pydot

Finally, we'll use the tensorflow_docs package to plot the loss and accuracy curves of the model. You can install this library with the following command:

$> pip install git+https://github.com/tensorflow/docs

How to do it…

Follow these steps to complete this recipe:

  1. Import the necessary packages:
    import...
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