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
Combining Computer Vision and NLP Techniques

In the previous chapter, we learned about leveraging novel architectures when there are a minimal number of data points. In this chapter, we will switch gears and learn about how a Convolutional Neural Network (CNN) can be used in conjunction with algorithms in the broad family of Recurrent Neural Networks (RNNs), which are heavily used (as of the time of writing this book) in Natural Language Processing (NLP) to develop solutions that leverage both computer vision and NLP.

To understand combining CNNs and RNNs, we will first learn about how RNNs work and their variants – primarily Long Short-Term Memory (LSTM) – to understand how they are applied to predict annotations given an image as input. After that, we will learn about another important loss function, called the Connectionist Temporal Classification (CTC) loss function...