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 CycleGAN

Imagine a scenario where we ask you to perform image translation from one class to another, but not give the input and the corresponding output images to train the model. However, we give you the images of both classes in two distinct folders. CycleGAN comes in handy in such a scenario.

In this section, we will learn how to train CycleGAN to convert the image of an apple into the image of an orange and vice versa. The Cycle in CycleGAN refers to the fact that we are translating (converting) an image from one class to another and back to the original class.

At a high level, we will have three separate loss values in this architecture (more detail is provided here):

  • Discriminator loss: This ensures that the object class is modified while training the model (as seen in the previous section).
  • Cycle loss: The loss of recycling an image from the generated image to the original to ensure that the surrounding pixels are not changed.
  • Identity loss: The loss when an image of...