Training a higher resolution VGG Model

I would like to train VGG with a higher resolution, say 448 instead of the standard 224 training image.

First, how is the size for the fully connected layer calculated?

Then I was wondering, is it possible to interpolate the standard 224 weights to 448 so I can start with interpolated weights instead of a blank slate?

I’m just getting started with pytorch so any examples would be greatly appreciated.

  • The input size of fully connected is calculated as the output image size(H x W) and number of channels (n_C) of the last pool layer. (eg. 7 x 7 x 512 )

import torch
import torch.nn as nn
from torchvision.models import vgg19

class VGG(nn.Module):

    def __init__(self, num_classes=1000):
        super(VGG, self).__init__()
        self.model = vgg19(pretrained=False) # pretrained=False just for debug reasons
        self.model.classifier = nn.Sequential(
            nn.Linear(512 * 7 * 7, 4096),
            nn.Linear(4096, 4096),
            nn.Linear(4096, num_classes),

    def forward(self, x):
        x = self.model(x)
        return x

model = VGG()
x = torch.randn((1, 3, 448, 448))