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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
2
Computer Vision and Neural Networks
5
Section 2: State-of-the-Art Solutions for Classic Recognition Problems
6
Influential Classification Tools
9
Section 3: Advanced Concepts and New Frontiers of Computer Vision
10
Training on Complex and Scarce Datasets

Optimizing post-processing

As we saw previously in the book, most models require post-processing operations. If implemented using the wrong tools, post-processing can take a lot of time. While most post-processing happens on the CPU, it is sometimes possible to run some operations on the GPU.

Using tracing tools, we can analyze the time taken by post-processing to optimize it. Non-Maximum Suppression (NMS) is an operation that can take a lot of time if not implemented correctly (refer to Chapter 5, Object Detection Models):

Figure 9-3: Evolution of NMS computing time with the number of boxes

Notice in the preceding diagram that the slow implementation takes linear computing time, while the fast implementation is almost constant. Though four milliseconds may seem quite low, keep in mind that some models can return an even larger number of boxes, resulting in a post-processing time.

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