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 colorization

Imagine a scenario where you are given a bunch of black-and-white images and are asked to turn them into color images. How would you solve this problem? One way to solve this is by using a pseudo-supervised pipeline where we take a raw image, convert it into black and white, and treat them as input-output pairs. We will demonstrate this by leveraging the CIFAR-10 dataset to perform colorization on images.

The strategy that we will adopt as we code up the image colorization network is as follows:

  1. Take the original color image in the training dataset and convert it into grayscale to fetch the input (grayscale) and output (original colored image) combination.
  2. Normalize the input and output.
  3. Build a U-Net architecture.
  4. Train the model over increasing epochs.

With the preceding strategy in place, let's go ahead and code up the model as follows:

The following code is available as Image colorization.ipynb in the Chapter 10 folder of this book's GitHub repository...