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

Understanding the impact of data augmentation and batch normalization

One clever way of improving the accuracy of models is by leveraging data augmentation. We have already seen this in Chapter 4, Introducing Convolutional Neural Networks, where we used data augmentation to improve the accuracy of classification on a translated image. In the real world, you would encounter images that have different properties – for example, some images might be much brighter, some might contain objects of interest near the edges, and some images might be more jittery than others. In this section, we will learn about how the usage of data augmentation can help in improving the accuracy of a model. Furthermore, we will learn about how data augmentation can practically be a pseudo-regularizer for our models.

To understand the impact of data augmentation and batch normalization, we will go through a dataset of recognizing traffic signs. We will evaluate three scenarios:

  • No batch normalization/data...