Book Image

Modern Computer Vision with PyTorch

By : V Kishore Ayyadevara, Yeshwanth Reddy
5 (1)
Book Image

Modern Computer Vision with PyTorch

5 (1)
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
Image Generation Using GANs

In the previous chapter, we learned about manipulating an image using neural style transfer and super-imposed the expression in one image on another. However, what if we give the network a bunch of images and ask it to come up with an entirely new image, all on its own?

Generative Adversarial Network (GAN) is a step toward achieving the feat of generating an image given a collection of images. In this chapter, we will start by learning about the idea behind what makes GANs work, before building one from scratch. GANs are a vast field that is expanding as we write this book. This chapter will lay the foundation of GANs through three variants of GANs, while we will learn about more advanced GANs and their applications in the next chapter.

In this chapter, we will explore the following topics:

  • Introducing GANs
  • Using GANs to generate handwritten digits...