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.

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Table of Contents (16 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Section 1: TensorFlow 2 and Deep Learning Applied to Computer Vision
Section 1: TensorFlow 2 and Deep Learning Applied to Computer Vision
2
Computer Vision and Neural Networks
Computer Vision and Neural Networks
Technical requirements
Computer vision in the wild
A brief history of computer vision
Getting started with neural networks
Summary
Questions
Further reading
3
TensorFlow Basics and Training a Model
TensorFlow Basics and Training a Model
Technical requirements
Getting started with TensorFlow 2 and Keras
TensorFlow 2 and Keras in detail
The TensorFlow ecosystem
Summary
Questions
4
Modern Neural Networks
Modern Neural Networks
Technical requirements
Discovering convolutional neural networks
Refining the training process
Summary
Questions
Further reading
5
Section 2: State-of-the-Art Solutions for Classic Recognition Problems
Section 2: State-of-the-Art Solutions for Classic Recognition Problems
6
Influential Classification Tools
Influential Classification Tools
Technical requirements
Understanding advanced CNN architectures
Leveraging transfer learning
Summary
Questions
Further reading
7
Object Detection Models
Object Detection Models
Technical requirements
Introducing object detection
A fast object detection algorithm – YOLO
Faster R-CNN – a powerful object detection model
Summary
Questions
Further reading
8
Enhancing and Segmenting Images
Enhancing and Segmenting Images
Technical requirements
Transforming images with encoders-decoders
Understanding semantic segmentation
Summary
Questions
Further reading
9
Section 3: Advanced Concepts and New Frontiers of Computer Vision
Section 3: Advanced Concepts and New Frontiers of Computer Vision
10
Training on Complex and Scarce Datasets
Training on Complex and Scarce Datasets
Technical requirements
Efficient data serving
How to deal with data scarcity
Summary
Questions
Further reading
11
Video and Recurrent Neural Networks
Video and Recurrent Neural Networks
Technical requirements
Introducing RNNs
Classifying videos
Summary
Questions
Further reading
12
Optimizing Models and Deploying on Mobile Devices
Optimizing Models and Deploying on Mobile Devices
Technical requirements
Optimizing computational and disk footprints
On-device machine learning
Example app – recognizing facial expressions
Summary
Questions
13
Migrating from TensorFlow 1 to TensorFlow 2
Migrating from TensorFlow 1 to TensorFlow 2
Automatic migration
Migrating TensorFlow 1 code
References
14
Assessments
Assessments
Answers
15
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Faster R-CNN – a powerful object detection model

The main benefit of YOLO is its speed. While it can achieve very good results, it is now outperformed by more complex networks. Faster Region with Convolutional Neural Networks (Faster R-CNN) is considered state of the art at the time of writing. It is also quite fast, reaching 4-5 FPS on a modern GPU. In this section, we will explore its architecture.

The Faster R-CNN architecture was engineered over several years of research. More precisely, it was built incrementally from two architectures—R-CNN and Fast R-CNN. In this section, we will focus on the latest architecture, Faster R-CNN:

  • Faster R-CNN: towards real-time object detection with region proposal networks (2015), Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun

This paper draws a lot of knowledge from the two previous designs. Therefore, some of the...

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