Convolutional Neural Networks (CNNs) have become a popular choice for image classification tasks due to their ability to automatically learn and extract features from images. One of the most successful CNN architectures is the VGG (Visual Geometry Group) network, which achieved top results in the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) in 2014. In this article, we will explore the VGG network in depth and implement it using PyTorch.

Understanding the VGG Network

The VGG network was developed by the Visual Geometry Group at the University of Oxford. The architecture of the VGG network is characterized by its use of very small (3×3) convolutional filters, which is different from other popular architectures such as AlexNet and LeNet that use larger filters. The use of small filters allows the network to stack more layers while keeping the number of parameters manageable.

The original VGG network consists of 16-19 layers, including 13 convolutional layers and 3-4 fully connected layers. The network takes an input image of size 224x224x3 and outputs a probability distribution over 1000 classes in the ImageNet dataset. The architecture of the VGG network can be summarized as follows:

Layer	Filter Size/Stride	Number of Filters
Input	–	–
Conv1	3×3/1	64
Conv2	3×3/1	64
MaxPool	2×2/2	–
Conv3	3×3/1	128
Conv4	3×3/1	128
MaxPool	2×2/2	–
Conv5	3×3/1	256
Conv6	3×3/1	256
Conv7	3×3/1	256
MaxPool	2×2/2	–
Conv8	3×3/1	512
Conv9	3×3/1	512
Conv10	3×3/1	512
MaxPool	2×2/2	–
Conv11	3×3/1	512
Conv12	3×3/1	512
Conv13	3×3/1	512
MaxPool	2×2/2	–
FC1	–	4096
FC2	–	4096
FC3	–	1000
Output	–	–

As can be seen from the table, the network consists of alternating convolutional layers and max pooling layers, followed by three fully connected layers. The convolutional layers are designed to extract features from the input image, while the max pooling layers are used to reduce the spatial dimensions of the feature maps. The fully connected layers at the end of the network are used to classify the input image into one of 1000 classes in the ImageNet dataset.

Implementation of the VGG Network in PyTorch

Now that we have a good understanding of the VGG network, let’s implement it using PyTorch. We will use the torchvision package in PyTorch, which provides pre-trained models for popular architectures including VGG. First, we will import the necessary libraries:

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.models as models

Next, we will load the pre-trained VGG-16 model from torchvision:

vgg = models.vgg16(pretrained=True)

This will download the pre-trained VGG-16 model and load it into memory. We can now access the layers of the model using the features and classifier attributes of the model:

print(vgg.features)
print(vgg.classifier)

This will print out the layers of the VGG-16 model. We can see that the features attribute contains the convolutional and max pooling layers, while the classifier attribute contains the fully connected layers.

To use the VGG-16 model for a new classification task, we need to replace the last fully connected layer with a new layer that outputs the number of classes in our dataset. We can do this as follows:

num_classes = 10
vgg.classifier[-1] = nn.Linear(4096, num_classes)

This will replace the last fully connected layer of the VGG-16 model with a new layer that outputs num_classes number of classes.

Next, we need to train the VGG-16 model on our dataset. We can do this using the standard PyTorch training loop:

criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(vgg.parameters(), lr=0.001, momentum=0.9)

for epoch in range(num_epochs):
    for i, (inputs, labels) in enumerate(train_loader):
        optimizer.zero_grad()
        outputs = vgg(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()

This will train the VGG-16 model on our dataset using the cross entropy loss and stochastic gradient descent optimizer.

Conclusion

In this article, we have explored the VGG network in depth and implemented it using PyTorch. The VGG network is a powerful convolutional neural network architecture that has achieved top results in the ImageNet Large Scale Visual Recognition Challenge. By understanding the architecture of the VGG network and implementing it in PyTorch, we can use it for a variety of image classification tasks.

FAQs

1. What is the difference between VGG and other convolutional neural network architectures?
   • VGG uses small (3×3) convolutional filters while other architectures use larger filters.
2. How many layers does the VGG network have?
   • The original VGG network has 16-19 layers.
3. What is the purpose of the max pooling layers in the VGG network?
   • The max pooling layers are used to reduce the spatial dimensions of the feature maps.
4. How can I use the pre-trained VGG model for my own classification task?
   • You can replace the last fully connected layer with a new layer that outputs the number of classes in your dataset.
5. What optimizer and loss function were used to train the VGG model?
   • Stochastic gradient descent was used as the optimizer and cross entropy loss was used as the loss function.