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

Implementing conditional GANs

Imagine a scenario where we want to generate an image of a class of our interest; for example, an image of a cat or an image of a dog, or an image of a man with spectacles. How do we specify that we want to generate an image of interest to us? Conditional GANs come to the rescue in this scenario.

For now, let's assume that we have the images of male and female faces only along with their corresponding labels. In this section, we will learn about generating images of a specified class of interest from random noise.

The strategy we adopt is as follows:

  1. Specify the label of the image we want to generate as a one-hot-encoded version.
  2. Pass the label through an embedding layer to generate a multi-dimensional representation of each class.
  3. Generate random noise and concatenate with the embedding layer generated in the previous step.
  4. Train the model just like we did in the previous sections, but this time with the noise vector concatenated with the embedding...