I don’t think you could use ResNets for your use case, as the first conv layer would already reduce the spatial size (layer definition here 1):
conv1 = nn.Conv2d(3, 6, kernel_size=7, stride=2, padding=3, bias=False)
x = torch.randn(1, 3, 224, 224)
out = conv1(x)
out.shape
> torch.Size([1, 6, 112, 112])

I would guess that the majority of CNNs are downsampling the spatial size in the feature extractor stage and don’t know a specific model, which would keep the spatial size throughout the model.