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 IoU

Imagine a scenario where we came up with a prediction of a bounding box for an object. How do we measure the accuracy of our prediction? The concept of Intersection over Union (IoU) comes in handy in such a scenario.

Intersection within the term Intersection over Union measures how overlapping the predicted and actual bounding boxes are, while Union measures the overall space possible for overlap. IoU is the ratio of the overlapping region between the two bounding boxes over the combined region of both the bounding boxes.

This can be represented in a diagram as follows:

In the preceding diagram of two bounding boxes (rectangles), let's consider the left bounding box as the ground truth and the right bounding box as the predicted location of the object. IoU as a metric is the ratio of the overlapping region over the combined region between the two bounding boxes.

In the following diagram, you can observe the variation in the IoU metric as the overlap between bounding...