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

Training R-CNN-based custom object detectors

R-CNN stands for Region-based Convolutional Neural Network. Region-based within R-CNN stands for the region proposals. Region proposals are used to identify objects within an image. Note that R-CNN assists in identifying both the objects present in the image and the location of objects within the image.

In the following sections, we will learn about the working details of R-CNN before training it on our custom dataset.

Working details of R-CNN

Let's get an idea of R-CNN-based object detection at a high level using the following diagram:

Image source:

We perform the following steps when leveraging the R-CNN technique for object detection:

  1. Extract region proposals from an image:
  • Ensure that we extract a high number of proposals to not miss out on any potential object within the image.
  1. Resize (warp) all the extracted regions to get images of the same size.
  2. Pass the resized region proposals through...