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 region proposals

Imagine a hypothetical scenario where the image of interest contains a person and sky in the background. Furthermore, for this scenario, let's assume that there is little change in pixel intensity of the background (sky) and that there is a considerable change in pixel intensity of the foreground (the person).

Just from the preceding description itself, we can conclude that there are two primary regions here – one is of the person and the other is of the sky. Furthermore, within the region of the image of a person, the pixels corresponding to hair will have a different intensity to the pixels corresponding to the face, establishing that there can be multiple sub-regions within a region.

Region proposal is a technique that helps in identifying islands of regions where the pixels are similar to one another.

Generating a region proposal comes in handy for object detection where we have to identify the locations of objects present in the image....