TensorFlow 2.0 Computer Vision Cookbook: Implement machine learning solutions to overcome various computer vision challenges
by Jesús Martínez
Publisher finelybook 出版社: Packt Publishing (February 26,2021)
Language 语言: English
Print Length 页数: 542 pages
ISBN-10: 183882913X
ISBN-13: 9781838829131
Book Description
By finelybook
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
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 advanced TensorFlow Hub models to perform image classification and object detection
Generate captions for images using CNNs and RNNs
