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)
1
Section 1 - Fundamentals of Deep Learning for Computer Vision
5
Section 2 - Object Classification and Detection
13
Section 3 - Image Manipulation
17
Section 4 - Combining Computer Vision with Other Techniques

Implementing facial key point detection

So far, we have learned about predicting classes that are binary (cats versus dogs) or are multi-label (fashionMNIST). Let's now learn a regression problem and, in so doing, a task where we are predicting not one but several continuous outputs. Imagine a scenario where you are asked to predict the key points present on an image of a face, for example, the location of the eyes, nose, and chin. In this scenario, we need to employ a new strategy to build a model to detect the key points.

Before we dive further, let's understand what we are trying to achieve through the following image:

As you can observe in the preceding image, facial key points denote the markings of various key points on the image that contains a face.

To solve this problem, we would have to solve a few problems first:

  • Images can be of different shapes:
  • This warrants an adjustment in the key point locations while adjusting images to bring them all to a standard image...