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

Detecting the number plate of a car

Imagine a scenario where we ask you to identify the location of a number plate in the image of a car. One way we have learned how to do this in the chapters on object detection is to come up with anchor box-based techniques to identify the location of the number plate. This would require us to train the model on a few hundred images before we leverage the model.

However, there is a cascade classifier that is readily available as a pre-trained file that we can use to identify the location of the number plate in an image of a car. A classifier is a cascade classifier if it consists of several simpler classifiers (stages) that are applied subsequently to a region of interest until at some stage, the candidate region is rejected or all the stages are passed. These are analogous to convolution kernels that we have learned how to use so far. Instead of having a deep neural network that learns kernels from other kernels, this is a list of kernels that have...