Book Image

Modern Computer Vision with PyTorch

By : V Kishore Ayyadevara, Yeshwanth Reddy
5 (1)
Book Image

Modern Computer Vision with PyTorch

5 (1)
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

Understanding VGG16 architecture

VGG stands for Visual Geometry Group, which is based out of the University of Oxford, and 16 stands for the number of layers in the model. The VGG16 model is trained to classify objects in the ImageNet competition and stood as the runner-up architecture in 2014. The reason we are studying this architecture instead of the winning architecture (GoogleNet) is because of its simplicity and a larger acceptance in the vision community by using it in several other tasks. Let's understand the architecture of VGG16 along with how a VGG16 pre-trained model is accessible and represented in PyTorch.

The code for this section is available as VGG_architecture.ipynb in the Chapter05 folder of this book's GitHub repository -
  1. Install the required packages:
import torchvision
import torch.nn as nn
import torch
import torch.nn.functional as F
from torchvision import transforms,models,datasets
!pip install torch_summary
from torchsummary...