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
Image Segmentation

In the previous chapter, we learned about detecting objects present in images, along with the classes that correspond to the detected objects. In this chapter, we will go one step further by not only drawing a bounding box around the object but also by identifying the exact pixels that contain an object. In addition to that, by the end of this chapter, we will be able to single out instances/objects that belong to the same class.

In this chapter, we will learn about semantic segmentation and instance segmentation by taking a look at the U-Net and Mask R-CNN architectures. Specifically, we will cover the following topics:

  • Exploring the U-Net architecture
  • Implementing semantic segmentation using U-Net
  • Exploring the Mask R-CNN architecture
  • Implementing instance segmentation using Mask R-CNN

A succinct image of what we are trying to achieve through image segmentation...