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

Practical aspects to take care of during model implementation

So far, we have seen the various ways of building an image classification model. In this section, we will learn about some of the practical considerations that need to be taken care of when building models. The ones we will discuss in this chapter are as follows:

  • Dealing with imbalanced data
  • The size of an object within an image when performing classification
  • The difference between training and validation images
  • The number of convolutional and pooling layers in a network
  • Image sizes to train on GPUs
  • Leveraging OpenCV utilities

Dealing with imbalanced data

Imagine a scenario where you are trying to predict an object that occurs very rarely within our dataset – let's say in 1% of the total images. For example, this can be the task of predicting whether an X-ray image suggests a rare lung infection.

How do we measure the accuracy of the model that is trained to predict the rare lung infection? If we simply predict...