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

Understanding advanced CNN architectures

Research in computer vision has been moving forward both through incremental contributions and large innovative leaps. Challenges organized by researchers and companies, inviting experts to submit new solutions in order to best solve a predefined task, have been playing a key role in triggering such instrumental contributions. The ImageNet classification contest (ImageNet Large Scale Visual Recognition Challenge (ILSVRC); see Chapter 1, Computer Vision and Neural Networks) is a perfect example. With its millions of images split into 1,000 fine-grained classes, it still represents a great challenge for daring researchers, even after the significant and symbolic victory of AlexNet in 2012.

In this section, we will present some of the classic deep learning methods that followed AlexNet in tackling ILSVRC, covering the reasons leading to their...