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

Drawing bounding boxes around words in an image

Imagine a scenario where you are building a model that performs word transcription from the image of a document. The first step would be to identify the location of words within the image. Primarily, there are two ways of identifying words within an image:

  • Using deep learning techniques such as CRAFT, EAST, and more
  • Using OpenCV-based techniques

In this section, we will learn about how machine-printed words can be identified in a clean image without leveraging deep learning. As the contrast between the background and foreground is high, you do not need an overkill solution such as YOLO to identify the location of individual words. Using OpenCV is going to be especially handy in these scenarios because we can arrive at a solution with very limited computational resources and, consequently, even the inference time will be very small. The only drawback is that the accuracy may not be 100%, but that is also subject to how clean the scanned...