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

Summary

In this chapter, we learned how to use TensorFlow 2.0 and Google Colab to train a neural network to perform a lot of complex image manipulation tasks such as semantic segmentation, image inpainting, generating an artificial image, and neural style transfer. We learned about the functionality of generative and discriminator networks, as well as how neural networks can be trained simultaneously in a balanced fashion to create fake output images. We also learned how to use Atrous convolution, spatial pooling, and encoder-decoder networks to develop semantic segmentation. Finally, we used Google Colab to train a neural network to perform neural style transfer.

In the next chapter, we will use a neural network for activity recognition.