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 autoencoders

So far, in the previous chapters, we have learned about classifying images by training a model based on the input image and its corresponding label. Now let's imagine a scenario where we need to cluster images based on their similarity and with the constraint of not having their corresponding labels. Autoencoders come in handy to identify and group similar images.

An autoencoder takes an image as input, stores it in a lower dimension, and tries to reproduce the same image as output, hence the term auto (which stands for being able to reproduce the input). However, if we just reproduce the input in the output, we would not need a network, but a simple multiplication of the input by 1 would do. The differentiating aspect of an autoencoder is that it encodes the information present in an image in a lower dimension and then reproduces the image, hence the term encoder (which stands for representing the information of an image in a lower dimension). This way...