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

3D object detection with point clouds

So far, we have learned how to predict a bounding rectangle on 2D images using algorithms that have the core underlying concept of anchor boxes. We will now learn how the same concept can be extended to predict 3D bounding boxes around objects.

In a self-driving car, tasks such as pedestrian/obstacle detection and route planning cannot happen without knowing the environment. Predicting 3D object locations along with their orientations becomes an important task. Not only is the 2D bounding box around obstacles important, but also knowing the distance from the object, height, width, and orientation of the obstacle are critical to navigating safely in the 3D world.

In this section, we will learn how YOLO is used to predict the 3D orientation and position of cars and pedestrians on a real-world dataset.

The instructions for downloading the data, training, and testing sets are all given in this GitHub repo: