TensorFlow 2.0 Computer Vision Cookbook

Implement machine learning solutions to overcome various computer vision challenges

Jesus Martinez 지음

Packt(GCO Science) 2021년 02월 26일 출간

작품소개

책소개

Get well versed with state-of-the-art techniques to tailor training processes and boost the performance of computer vision models using machine learning and deep learning techniques

▶Book Description
Computer vision is a scientific field that enables machines to identify and process digital images and videos. This book focuses on independent recipes to help you perform various computer vision tasks using TensorFlow.

The book begins by taking you through the basics of deep learning for computer vision, along with covering TensorFlow 2.x's key features, such as the Keras and tf.data.Dataset APIs. You'll then learn about the ins and outs of common computer vision tasks, such as image classification, transfer learning, image enhancing and styling, and object detection. The book also covers autoencoders in domains such as inverse image search indexes and image denoising, while offering insights into various architectures used in the recipes, such as convolutional neural networks (CNNs), region-based CNNs (R-CNNs), VGGNet, and You Only Look Once (YOLO).

Moving on, you'll discover tips and tricks to solve any problems faced while building various computer vision applications. Finally, you'll delve into more advanced topics such as Generative Adversarial Networks (GANs), video processing, and AutoML, concluding with a section focused on techniques to help you boost the performance of your networks.

By the end of this TensorFlow book, you'll be able to confidently tackle a wide range of computer vision problems using TensorFlow 2.x.

▶What You Will Learn
-Understand how to detect objects using state-of-the-art models such as YOLOv3
-Use AutoML to predict gender and age from images
-Segment images using different approaches such as FCNs and generative models
-Learn how to improve your network's performance using rank-N accuracy, label smoothing, and test time augmentation
-Enable machines to recognize people's emotions in videos and real-time streams
-Access and reuse adv

목차

▶TABLE of CONTENTS
-Chapter 1: Getting Started with TensorFlow 2.x for Computer Vision
-Chapter 2: Performing Image Classification
-Chapter 3: Harnessing the Power of Pre-Trained Networks with Transfer Learning
-Chapter 4: Enhancing and Styling Images with DeepDream, Neural Style Transfer, and Image Super-Resolution
-Chapter 5: Reducing Noise with Autoencoders
-Chapter 6: Generative Models and Adversarial Attacks
-Chapter 7: Captioning Images with CNNs and RNNs
-Chapter 8: Fine-Grained Understanding of Images through Segmentation
-Chapter 9: Localizing Elements in Images with Object Detection
-Chapter 10: Applying the Power of Deep Learning to Videos
-Chapter 11: Streamlining Network Implementation with AutoML
-Chapter 12: Boosting Performance

책 속으로

▶What this book covers
- Chapter 1, Getting Started with TensorFlow 2.x for Computer Vision, serves as an overview of basic deep learning concepts, as well as being a first look at some important TensorFlow 2.x features, such as the Keras and tf.data.Dataset APIs. It also teaches you about common and necessary tasks such as saving and loading a model and visualizing a network architecture. It ends with the implementation of a simple image classifier.

- Chapter 2, Performing Image Classification, goes in-depth about the most common application of deep neural networks to computer vision: image classification. It explores the common varieties of classification, such as binary and multiclass classification, and then transitions to examples of multilabel classification and out-of-the-box solutions using transfer learning and TensorFlow Hub.

- Chapter 3, Harnessing the Power of Pre-Trained Networks with Transfer Learning, focuses on transfer learning, a powerful technique to reuse networks pre-trained on massive datasets to increase development productivity and the performance of deep learningpowered computer vision applications. This chapter starts by seeing you use pre-trained networks as feature extractors. Then, you will learn how to combine deep learning with traditional machine learning algorithms through a procedure called incremental learning. Finally, the chapter closes with two examples of fine-tuning: the first using the Keras API and the second relying on TensorFlow Hub.

- Chapter 4, Enhancing and Styling Images with DeepDream, Neural Style Transfer, and Image Super-Resolution, focuses on fun and less conventional applications of deep neural networks in computer vision, namely DeepDream, neural style transfer, and image superresolution.

- Chapter 5, Reducing Noise with Autoencoders, goes over autoencoders, a composite architecture used in domains such as image restoration, inverse image search indexes, and image denoising. It starts by introducing the dens

출판사 서평

▶ Preface
The release of TensorFlow 2.x in 2019 was one of the biggest and most anticipated events in the deep learning and artificial intelligence arena, because it brought with it long-overdue improvements to this popular and relevant framework, mainly focused on simplicity and ease of use.

The adoption of Keras as the official TensorFlow high-level API, the ability to switch back and forth between eager and graph-based execution (thanks to tf.function), and the ability to create complex data pipelines with tf.data are just a few of the great additions that TensorFlow 2.x brings to the table.

In this book, you will discover a vast amount of recipes that will teach you how to take advantage of these advancements in the context of deep learning applied to computer vision. We will cover a wide gamut of applications, ranging from image classification to more challenging ones, such as object detection, image segmentation, and Automated Machine Learning (AutoML).

By the end of this book, you’ll be prepared and confident enough to tackle any computer vision problem that comes your way with the invaluable help of TensorFlow 2.x!

작가정보

저자(글) Jesus Martinez

Jesus Martinez is the founder of the computer vision e-learning site DataSmarts. He is a computer vision expert and has worked on a wide range of projects in the field, such as a piece of people-counting software fed with images coming from an RGB camera and a depth sensor, using OpenCV and TensorFlow. He developed a self-driving car in a simulation, using a convolutional neural network created with TensorFlow, that worked solely with visual inputs. Also, he implemented a pipeline that uses several advanced computer vision techniques to track lane lines on the road, as well as providing extra information such as curvature degree.