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

Using DCGANs to generate face images

In the previous section, we learned about generating images using GANs. However, we have already seen that Convolutional Neural Networks (CNNs) perform better in the context of images when compared to vanilla neural networks. In this section, we will learn about generating images using Deep Convolutional Generative Adversarial Networks (DCGANs), which use convolution and pooling operations in the model.

First, let's understand the technique we will leverage to generate an image using a set of 100 random numbers. We will first convert noise into a shape of batch size x 100 x 1 x 1. The reason for appending additional channel information in DCGANs and not doing it in the GAN section is that we will leverage CNNs in this section, which requires inputs in the form of batch size x channels x height x width.

Next, we convert the generated noise into an image by leveraging

As we learned in Chapter 9, Image Segmentation, ConvTranspose2d...