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

Understanding variational autoencoders

So far, we have seen a scenario where we can group similar images into clusters. Furthermore, we have learned that when we take embeddings of images that fall in a given cluster, we can re-construct (decode) them. However, what if an embedding (a latent vector) falls in between two clusters? There is no guarantee that we would generate realistic images. Variational autoencoders come in handy in such a scenario.

Before we dive into building a variational autoencoder, let's explore the limitations of generating images from embeddings that do not fall into a cluster (or in the middle of different clusters). First, we generate images by sampling vectors:

The following code is a continuation of the code built in the previous section, Understanding convolutional autoencoders, and is available as conv_auto_encoder.ipynb in the chapter11 folder of this book's GitHub repository -
  1. Calculate the latent vectors (embeddings...