How can i modify the feature map size parameter in ResNET50?

[J_Johnson]

You can specify any model inside of the class as the parent. And then modify whichever layer you want in the init. See here:

How can I replace the forward method of a predefined torchvision model with my customized forward function?

You could derive a custom class using the resnet class as its parent: import torchvision.models as models from torchvision.models.resnet import ResNet, BasicBlock class MyResNet18(ResNet): def __init__(self): super(MyResNet18, self).__init__(BasicBlock, [2, 2, 2, 2]) def forward(self, x): # change forward here x = self.conv1(x) return x model = MyResNet18() # if you need pretrained weights model.load_state_dict(models.resnet18(pretrained=True)…