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 convolutional autoencoders

In the previous section, we learned about autoencoders and implemented them in PyTorch. While we have implemented them, one convenience that we had through the dataset was that each image has only 1 channel (each image was represented as a black and white image) and the images are relatively small (28 x 28). Hence the network flattened the input and was able to train on 784 (28*28) input values to predict 784 output values. However, in reality, we will encounter images that have 3 channels and are much bigger than a 28 x 28 image.

In this section, we will learn about implementing a convolutional autoencoder that is able to work on multi-dimensional input images. However, for the purpose of comparison with vanilla autoencoders, we will work on the same MNIST dataset that we worked on in the previous section, but modify the network in such a way that we now build a convolutional autoencoder and not a vanilla autoencoder.

A convolutional autoencoder...