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

PyTorch tensors

Tensors are the fundamental data types of PyTorch. A tensor is a multi-dimensional matrix similar to NumPy's ndarrays:

  • A scalar can be represented as a zero-dimensional tensor.
  • A vector can be represented as a one-dimensional tensor.
  • A two-dimensional matrix can be represented as a two-dimensional tensor.
  • A multi-dimensional matrix can be represented as a multi-dimensional tensor.

Pictorially, the tensors look as follows:

For instance, we can consider a color image as a three-dimensional tensor of pixel values, since a color image consists of height x width x 3 pixels – where the three channels correspond to the RGB channels. Similarly, a grayscale image can be considered a two-dimensional tensor as it consists of height x width pixels.

By the end of this section, we will learn why tensors are useful and how to initialize them, as well as perform various operations on top of tensors. This will serve as a base for when we study leveraging tensors to build...