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)
1
Section 1 - Fundamentals of Deep Learning for Computer Vision
5
Section 2 - Object Classification and Detection
13
Section 3 - Image Manipulation
17
Section 4 - Combining Computer Vision with Other Techniques

Leveraging StyleGAN on custom images

Let's first understand a few historical developments prior to the invention of StyleGAN. As we know, generating fake faces from the previous chapter involved the usage of GANs. The biggest problem that research faced was that the images that could be generated were small (typically 64 x 64). And any effort to generate images of a larger size caused the generators or discriminators to fall into local minima that would stop training and generate gibberish. One of the major leaps in generating high-quality images involved a research paper called ProGAN (short for Progressive GAN), which involved a clever trick.

The size of both the generator and discriminator is progressively increased. In the first step, you create a generator and discriminator to generate 4 x 4 images from a latent vector. After this, additional convolution (and upscaling) layers are added to the trained generator and discriminator, which will be responsible for accepting the...