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

Super-resolution GAN

In the previous section, we saw a scenario where we leveraged the pre-trained StyleGAN to generate images in a given style. In this section, we will take it a step further and learn about leveraging pre-trained models to perform image super-resolution. We will gain an understanding of the architecture of the Super-resolution GAN model before implementing it on images.

First, we will understand the reason why a GAN is a good solution for the task of super-resolution. Imagine a scenario where you are given an image and asked to increase its resolution. Intuitively, you would consider various interpolation techniques to perform super-resolution. Here's a sample low-resolution image along with the outputs of various techniques (image source:

From the preceding image, we can see that traditional interpolation techniques such as bicubic interpolation do not help as much when reconstructing the image from a low resolution (a 4X...