Book Image

Hands-On Convolutional Neural Networks with TensorFlow

By : Iffat Zafar, Giounona Tzanidou, Richard Burton, Nimesh Patel, Leonardo Araujo
Book Image

Hands-On Convolutional Neural Networks with TensorFlow

By: Iffat Zafar, Giounona Tzanidou, Richard Burton, Nimesh Patel, Leonardo Araujo

Overview of this book

Convolutional Neural Networks (CNN) are one of the most popular architectures used in computer vision apps. This book is an introduction to CNNs through solving real-world problems in deep learning while teaching you their implementation in popular Python library - TensorFlow. By the end of the book, you will be training CNNs in no time! We start with an overview of popular machine learning and deep learning models, and then get you set up with a TensorFlow development environment. This environment is the basis for implementing and training deep learning models in later chapters. Then, you will use Convolutional Neural Networks to work on problems such as image classification, object detection, and semantic segmentation. After that, you will use transfer learning to see how these models can solve other deep learning problems. You will also get a taste of implementing generative models such as autoencoders and generative adversarial networks. Later on, you will see useful tips on machine learning best practices and troubleshooting. Finally, you will learn how to apply your models on large datasets of millions of images.
Table of Contents (17 chapters)
Title Page
Copyright and Credits
Packt Upsell

Chapter 4. Object Detection and Segmentation

From the previous chapter we know that Image classification only really deals with the case when we have a single instance of a class in an input image. Even then it only provides a coarse output for us, letting us know what object is present in an image but not where it is. A more interesting scenario is when we want to find where all instances of a class, or even multiple different classes, are located in an input image.

To deal with this more challenging problem, object detection and segmentation come into the picture. These are areas of computer vision that until recently were very challenging. However, applying convolutional neural networks to these problems has gained a lot of attention in recent years and consequently, for the most part, these problems can now be considered solved. In this chapter we will see how CNNs have managed to tackle these difficult tasks so well.

The following image shows the differences between different solutions...