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Hands-On Computer Vision with TensorFlow 2

Hands-On Computer Vision with TensorFlow 2

By : Benjamin Planche, Eliot Andres
3.3 (12)
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Hands-On Computer Vision with TensorFlow 2

Hands-On Computer Vision with TensorFlow 2

3.3 (12)
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)
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1
Section 1: TensorFlow 2 and Deep Learning Applied to Computer Vision
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Computer Vision and Neural Networks
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Section 2: State-of-the-Art Solutions for Classic Recognition Problems
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Influential Classification Tools
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Section 3: Advanced Concepts and New Frontiers of Computer Vision
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Training on Complex and Scarce Datasets
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Assessments
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Other Books You May Enjoy
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Chapter 9

  1. When measuring a model's inference speed, should you measure with single or multiple images?

Multiple images should be used to avoid measure bias.

  1. Is a model with float32 weights larger or smaller than one with float16 weights?

Float16 weights use about half the space of float32 weights. On compatible devices, they can also be faster.

  1. On iOS devices, should you use Core ML or TensorFlow Lite? What about Android devices?

On iOS devices, we recommend using Core ML where possible as it is available natively and is tightly integrated with the hardware. On Android devices, TensorFlow Lite should be used as there is no alternative.

  1. What are the benefits and limitations of running a model in the browser?

It does not require any installation on the user side and does not require computing power on the server side, making the application almost infinitely scalable.

  1. What is the most important requirement for embedded devices running deep learning algorithms?

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