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

Creating a bounding box ground truth for training

We have learned that object detection gives us the output where a bounding box surrounds the object of interest in an image. For us to build an algorithm that detects the bounding box surrounding the object in an image, we would have to create the input-output combinations, where the input is the image and the output is the bounding boxes surrounding the objects in the given image, and the classes corresponding to the objects.

Note that when we detect the bounding box, we are detecting the pixel locations of the four corners of the bounding box surrounding the image.

To train a model that provides the bounding box, we need the image, and also the corresponding bounding box coordinates of all the objects in an image. In this section, we will learn about one way to create the training dataset, where the image is the input and the corresponding bounding boxes and classes of objects are stored in an XML file as output. We will use the ybat...