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

Building a neural network using PyTorch

In the previous chapter, we learned about building a neural network from scratch, where the components of a neural network are as follows:

  • The number of hidden layers
  • The number of units in a hidden layer
  • Activation functions performed at the various layers
  • The loss function that we try to optimize for
  • The learning rate associated with the neural network
  • The batch size of data leveraged to build the neural network
  • The number of epochs of forward and back-propagation

However, for all of these, we built them from scratch using NumPy arrays in Python. In this section, we will learn about implementing all of these using PyTorch on a toy dataset. Note that we will leverage our learning so far regarding initializing tensor objects, performing various operations on top of them, and calculating the gradient values to update weights when building a neural network using PyTorch.

Note that, in this chapter, to gain the intuition of performing various operations...