Book Image

Mastering Computer Vision with TensorFlow 2.x

By : Krishnendu Kar
Book Image

Mastering Computer Vision with TensorFlow 2.x

By: Krishnendu Kar

Overview of this book

Computer vision allows machines to gain human-level understanding to visualize, process, and analyze images and videos. This book focuses on using TensorFlow to help you learn advanced computer vision tasks such as image acquisition, processing, and analysis. You'll start with the key principles of computer vision and deep learning to build a solid foundation, before covering neural network architectures and understanding how they work rather than using them as a black box. Next, you'll explore architectures such as VGG, ResNet, Inception, R-CNN, SSD, YOLO, and MobileNet. As you advance, you'll learn to use visual search methods using transfer learning. You'll also cover advanced computer vision concepts such as semantic segmentation, image inpainting with GAN's, object tracking, video segmentation, and action recognition. Later, the book focuses on how machine learning and deep learning concepts can be used to perform tasks such as edge detection and face recognition. You'll then discover how to develop powerful neural network models on your PC and on various cloud platforms. Finally, you'll learn to perform model optimization methods to deploy models on edge devices for real-time inference. By the end of this book, you'll have a solid understanding of computer vision and be able to confidently develop models to automate tasks.
Table of Contents (18 chapters)
1
Section 1: Introduction to Computer Vision and Neural Networks
6
Section 2: Advanced Concepts of Computer Vision with TensorFlow
11
Section 3: Advanced Implementation of Computer Vision with TensorFlow
14
Section 4: TensorFlow Implementation at the Edge and on the Cloud

Overview of ResNet

ResNet, introduced by Kaiminh He, Xiangyu Zhang, Shaoquing Ren, and Jian Sun in the paper titled Deep Residual Learning for Image Recognition, was developed to address the accuracy degradation problem of deep neural networks with an increase in depth. This degradation is not caused by overfitting, but results from the fact that after some critical depth, the output looses the information of the input, so the correlation between the input and output starts diverging resulting in an increase in inaccuracy. The paper can be found at https://arxiv.org/abs/1512.03385.

ResNet-34 achieved a top-five validation error of 5.71%, better than BN-inception and VGG. ResNet-152 achieves a top-five validation error of 4.49%. An ensemble of six models with different depths achieved a top-five validation error of 3.57%, and won first place in ILSVRC-2015. ILSVRC stands for the...