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

Product type Paperback

Published in Feb 2021

Publisher Packt

ISBN-13 9781838829131

Length 542 pages

Edition 1st Edition

Languages

Python

Tools

OpenCV

Concepts

Computer Vision

Author (1):

Jesús Martínez

View More author details

Table of Contents (14) Chapters

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

Leave a review - let other readers know what you think

Chapter 1: Getting Started with TensorFlow 2.x for Computer Vision

One of the greatest features of TensorFlow 2.x is that it finally incorporates Keras as its high-level API. Why is this so important? While it's true that Keras and TensorFlow have had very good compatibility for a while, they have remained separate libraries with different development cycles, which causes frequent compatibility issues. Now that the relationship between these two immensely popular tools is official, they'll grow in the same direction, following a single roadmap and making the interoperability between them completely seamless. In the end, Keras is TensorFlow and TensorFlow is Keras.

Perhaps the biggest advantage of this merger is that by using Keras' high-level features, we are not sacrificing performance by any means. Simply put, Keras code is production-ready!

Unless the requirements of a particular project demand otherwise, in the vast majority of the recipes in this book, we...