Book Image

Hands-On Computer Vision with TensorFlow 2

By : Benjamin Planche, Eliot Andres
Book Image

Hands-On Computer Vision with TensorFlow 2

By: Benjamin Planche, Eliot Andres

Overview of this book

Computer vision solutions are becoming increasingly common, making their way into fields such as health, automobile, social media, and robotics. This book will help you explore TensorFlow 2, the brand new version of Google's open source framework for machine learning. You will understand how to benefit from using convolutional neural networks (CNNs) for visual tasks. Hands-On Computer Vision with TensorFlow 2 starts with the fundamentals of computer vision and deep learning, teaching you how to build a neural network from scratch. You will discover the features that have made TensorFlow the most widely used AI library, along with its intuitive Keras interface. You'll then move on to building, training, and deploying CNNs efficiently. Complete with concrete code examples, the book demonstrates how to classify images with modern solutions, such as Inception and ResNet, and extract specific content using You Only Look Once (YOLO), Mask R-CNN, and U-Net. You will also build generative adversarial networks (GANs) and variational autoencoders (VAEs) to create and edit images, and long short-term memory networks (LSTMs) to analyze videos. In the process, you will acquire advanced insights into transfer learning, data augmentation, domain adaptation, and mobile and web deployment, among other key concepts. By the end of the book, you will have both the theoretical understanding and practical skills to solve advanced computer vision problems with TensorFlow 2.0.
Table of Contents (16 chapters)
Free Chapter
Section 1: TensorFlow 2 and Deep Learning Applied to Computer Vision
Section 2: State-of-the-Art Solutions for Classic Recognition Problems
Section 3: Advanced Concepts and New Frontiers of Computer Vision

Enhancing and Segmenting Images

We have just learned how to create neural networks that output predictions that are more complex than just a single class. In this chapter, we will push this concept further and introduce encoders-decoders, which are models used to edit or generate full images. We will present how encoder-decoder networks can be applied to a wide range of applications, from image denoising to object and instance segmentation. This chapter comes with several concrete examples, such as the application of encoders-decoders to semantic segmentation for self-driving cars.

The following topics will be covered in this chapter:

  • What encoders-decoders are, and how they are trained for pixel-level prediction
  • Which novel layers they use to output high-dimensional data (unpooling, transposed, and atrous convolutions)
  • How the FCN and U-Net architectures are tackling semantic...