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

Using GANs to generate handwritten digits

To generate images of handwritten digits, we will leverage the same network as we learned about in the previous section. The strategy we will adopt is as follows:

  1. Import MNIST data.
  2. Initialize random noise.
  3. Define the generator model.
  4. Define the discriminator model.
  5. Train the two models alternately.
  6. Let the model train until the generator and discriminator losses are largely the same.

Let's execute each of the preceding steps in the following code:

The following code is available as Handwritten_digit_generation_using_GAN.ipynb in the Chapter12 folder in this book's GitHub repository - The code is moderately lengthy. We strongly recommend you to execute the notebook in GitHub to reproduce results while you understand the steps to perform and explanation of various code components from text.
  1. Import the relevant packages and define the device:
!pip install -q torch_snippets
from torch_snippets import *