Book Image

TensorFlow 2.0 Computer Vision Cookbook

By : Jesús Martínez
Book Image

TensorFlow 2.0 Computer Vision Cookbook

By: Jesús Martínez

Overview of this book

Computer vision is a scientific field that enables machines to identify and process digital images and videos. This book focuses on independent recipes to help you perform various computer vision tasks using TensorFlow. The book begins by taking you through the basics of deep learning for computer vision, along with covering TensorFlow 2.x’s key features, such as the Keras and tf.data.Dataset APIs. You’ll then learn about the ins and outs of common computer vision tasks, such as image classification, transfer learning, image enhancing and styling, and object detection. The book also covers autoencoders in domains such as inverse image search indexes and image denoising, while offering insights into various architectures used in the recipes, such as convolutional neural networks (CNNs), region-based CNNs (R-CNNs), VGGNet, and You Only Look Once (YOLO). Moving on, you’ll discover tips and tricks to solve any problems faced while building various computer vision applications. Finally, you’ll delve into more advanced topics such as Generative Adversarial Networks (GANs), video processing, and AutoML, concluding with a section focused on techniques to help you boost the performance of your networks. By the end of this TensorFlow book, you’ll be able to confidently tackle a wide range of computer vision problems using TensorFlow 2.x.
Table of Contents (14 chapters)

Technical requirements

One of the first things you'll notice is that AutoML is very resource-intensive, so accessing a GPU is a must if you want to replicate and extend the recipes we'll discuss in this chapter. Also, because we'll be using AutoKeras in all the examples provided, install it as follows:

$> pip install git+https://github.com/keras-team/[email protected] autokeras pydot graphviz

The AutoKeras version we'll be using in this chapter only works with TensorFlow 2.3, so ensure you have it installed as well (if you prefer, you can create a different environment altogether). In the Getting ready section of each recipe, you'll find any preparatory information needed. As usual, the code shown in this chapter is available at https://github.com/PacktPublishing/Tensorflow-2.0-Computer-Vision-Cookbook/tree/master/ch11.

Check out the following link to see the Code in Action video:

https://bit.ly/2Na6XRz.