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 - https://tinyurl.com/mcvp-packt

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...