In Chapter 7, Object Detection Using YOLO, we learned about YOLO object detection, and then, in the previous two chapters, we learned about action recognition and image in-painting. This chapter marks the beginning of the end-to-end (E2E) object detection framework by developing a solid foundation of data ingestion and training pipeline followed by model development. Here, we will gain a deep insight into the various object detection models, such as R-CNN, single-shot detector (SSD), region-based fully convolutional networks (R-FCNs), and Mask R-CNN, and perform hands-on exercises using Google Cloud and Google Colab notebooks. We will also carry out a detailed exercise on how to train your own custom image to develop an object detection model using a TensorFlow object detection API. We will end the chapter with a deep overview of various...
Mastering Computer Vision with TensorFlow 2.x
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Mastering Computer Vision with TensorFlow 2.x
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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.
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Current Title:
Mastering Computer Vision with TensorFlow 2.x
Table of Contents (18 chapters)
Preface
Section 1: Introduction to Computer Vision and Neural Networks
Free Chapter
Computer Vision and TensorFlow Fundamentals
Content Recognition Using Local Binary Patterns
Facial Detection Using OpenCV and CNN
Deep Learning on Images
Section 2: Advanced Concepts of Computer Vision with TensorFlow
Neural Network Architecture and Models
Visual Search Using Transfer Learning
Object Detection Using YOLO
Semantic Segmentation and Neural Style Transfer
Section 3: Advanced Implementation of Computer Vision with TensorFlow
Action Recognition Using Multitask Deep Learning
Object Detection Using R-CNN, SSD, and R-FCN
Section 4: TensorFlow Implementation at the Edge and on the Cloud
Deep Learning on Edge Devices with CPU/GPU Optimization
Cloud Computing Platform for Computer Vision
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