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

Leveraging the Pix2Pix GAN

Imagine a scenario where we have pairs of images that are related to each other (for example, an image of edges of an object as input and an actual image of the object as output). The challenge given is that we want to generate an image given the input image of the edges of an object. In a traditional setting, this would have been a simple mapping of input to output and hence a supervised learning problem. However, imagine that you are working with a creative team that is trying to come up with a fresh look for products. In such a scenario, supervised learning does not help as much – as it learns only from history. A GAN comes in handy here because it will ensure that the generated image looks realistic enough and leaves room for experimentation (as we are interested in checking whether the generated image seems like one of the classes of interest or not).

In this section, we will learn about the architecture to generate the image of a shoe from a hand...