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

Object Detection Using YOLO

In the previous chapter, we discussed, in detail, the various neural network image classification and object detection architectures that utilize multiple steps for the object detection, classification, and refinement of a bounding box. In this chapter, we will be introducing two single-stage, fast object detection methods—You Only Look Once (YOLO) and RetinaNet. We will be discussing the architectures of each model and then perform inference in real images and videos using YOLO v3. We will show you how to optimize configuration parameters and train your own custom images using YOLO v3.

The topics covered in this chapter are as follows:

  • An overview of YOLO
  • An introduction to Darknet for object detection
  • Real-time prediction using Darknet and Tiny Darknet
  • Comparing YOLOs – YOLO versus YOLO v2 versus YOLO v3
  • When to train a model?
  • Training...