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

Components of modern object detection algorithms

The drawback of the R-CNN and Fast R-CNN techniques is that they have two disjointed networks – one to identify the regions that likely contain an object and the other to make corrections to the bounding box where an object is identified. Furthermore, both the models require as many forward propagations as there are region proposals. Modern object detection algorithms focus heavily on training a single neural network and have the capability to detect all objects in one forward pass. In the subsequent sections, we will learn about the various components of a typical modern object detection algorithm:

  • Anchor boxes
  • Region proposal network (RPN)
  • Region of interest pooling

Anchor boxes

So far, we have had region proposals coming from the selectivesearch method. Anchor boxes come in as a handy replacement for selective search – we will learn how they replace selectivesearch-based region proposals in this section.

Typically, a...