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

Instance segmentation

Instance segmentation is the last thing we will look at in this chapter. In many ways, it can be thought of as a fusion of object detection and semantic segmentation. However, it is definitely a step up in difficulty compared to those two problems.

With instance segmentation, the idea is to find every occurrence, what is called an instance, of a desired object or objects within an image. Once these are found, we want to segment off each instance from the other, even if they belong to the same class of objects. In other words, labels are both class-aware (such as car, sign, or person) and instance-aware (such as car 1, car 2, or car 3).


The result of instance segmentation will look something like this:

The similarity between this and semantic segmentation is clear; we still label pixels according to what object they belong to. However, while semantic segmentation has no knowledge of how many times a certain object occurs within an image instance, segmentation does.