Input size for EfficientNet versions from torchvision.models

Hi guys!
I’m doing some experiments with the EfficientNet as a backbone.
I’m using the pre-trained EfficientNet models from torchvision.models.
As I found from the paper and the docs of Keras, the EfficientNet variants have different input sizes as below.


Is it true for the models in Pytorch?
If I want to keep the same input size for all the EfficientNet variants, will it affect the performance of the models?

Yes, the torchvision EfficienNet models also expect a different input shape as described in the docs:

The sizes of the EfficientNet models depend on the variant. For the exact input sizes check here

where the link points to:

    elif args.model.startswith('efficientnet_'):
        sizes = {
            'b0': (256, 224), 'b1': (256, 240), 'b2': (288, 288), 'b3': (320, 300),
            'b4': (384, 380), 'b5': (489, 456), 'b6': (561, 528), 'b7': (633, 600),
        }