Book Image

Modern Computer Vision with PyTorch

By : V Kishore Ayyadevara, Yeshwanth Reddy
Book Image

Modern Computer Vision with PyTorch

By: V Kishore Ayyadevara, Yeshwanth Reddy

Overview of this book

Deep learning is the driving force behind many recent advances in various computer vision (CV) applications. This book takes a hands-on approach to help you to solve over 50 CV problems using PyTorch1.x on real-world datasets. You’ll start by building a neural network (NN) from scratch using NumPy and PyTorch and discover best practices for tweaking its hyperparameters. You’ll then perform image classification using convolutional neural networks and transfer learning and understand how they work. As you progress, you’ll implement multiple use cases of 2D and 3D multi-object detection, segmentation, human-pose-estimation by learning about the R-CNN family, SSD, YOLO, U-Net architectures, and the Detectron2 platform. The book will also guide you in performing facial expression swapping, generating new faces, and manipulating facial expressions as you explore autoencoders and modern generative adversarial networks. You’ll learn how to combine CV with NLP techniques, such as LSTM and transformer, and RL techniques, such as Deep Q-learning, to implement OCR, image captioning, object detection, and a self-driving car agent. Finally, you'll move your NN model to production on the AWS Cloud. By the end of this book, you’ll be able to leverage modern NN architectures to solve over 50 real-world CV problems confidently.
Table of Contents (25 chapters)
Section 1 - Fundamentals of Deep Learning for Computer Vision
Section 2 - Object Classification and Detection
Section 3 - Image Manipulation
Section 4 - Combining Computer Vision with Other Techniques

Representing an image

A digital image file (typically associated with the extension "JPEG" or "PNG") is comprised of an array of pixels. A pixel is the smallest constituting element of an image. In a grayscale image, each pixel is a scalar (single) value between 0 and 255 0 is black, 255 is white, and anything in between is gray (the smaller the pixel value, the darker the pixel is). On the other hand, the pixels in color images are three-dimensional vectors that correspond to the scalar values that can be found in its red, green, and blue channels.

An image has height x width x c pixels, where height is the number of rows of pixels, width is the number of columns of pixels, and c is the number of channels. c is 3 for color images (one channel each for the red, green, and blue intensities of the image) and 1 for grayscale images. An example grayscale image containing 3 x 3 pixels and their corresponding scalar values is shown here:

Again, a pixel value of...