Akshay Kulkarni - Computer Vision Projects with PyTorch: Design and Develop Production-Grade Models

This Apress imprint is published by the registered company APress Media, LLC, part of Springer Nature.

The registered company address is: 1 New York Plaza, New York, NY 10004, U.S.A.

This book explores various popular methodologies in the field of computer vision in order to unravel its mysteries. We use the PyTorch framework, because it's used by researchers, developers, and beginners to leverage the power of deep learning. This book explores multiple computer vision problems and shows you how to solve them. You can expect an introduction to some of the most critical challenges with hands-on code in PyTorch, which is suitable for beginner and intermediate Python users, along with various methodologies used to solve those business problems.

Production-grade code related to important concepts we present over the course of the book will help you get started quickly. These code snippets can be run on local systems, with or without GPUs (Graphics Processing Units) or on a cloud platform.

Well introduce you to the concepts of image processing in stages, starting with the basic concepts of computer vision in the first chapter. Well also delve into the field of deep learning and explain how models are developed for vision-related tasks. Youll get a quick introduction to PyTorch to prepare you for the example business challenges well be presenting later in the book. We explore concepts of the revolutionary convolutional neural networks, as well as architectures such as VGG, ResNet, YOLO, Inception, R-CNN, and many others.

The book dives deep into business problems related to image classification, object detection, and segmentation. We explore the concepts of super-resolution and GAN architectures, which are used in many industries. You learn about image similarity and pose estimation, which help with unsupervised problem sets. There are topics related to video analytics, which will help you develop the mindset of using the image and time-based concepts of frames. Adding to the list, the book ends by discussing how these deep learning models can be explained to your business partners. This book aims to be a complete suite for those pursuing computer vision business problems.