Hi,
I am using the Imagenet Pretrained Resnet 18 model and according to torchvision.models — PyTorch 1.7.0 documentation 3 the images that are fed into the model have to be 224x224. The problem is that my input image is much larger, for example, 2500x2500 or any other arbitrary resolution. I am looking for a way to feed in my images and possibly have a first convolution layer that would “map” from my image space to that of the Imagenet pretrained one. Almost like a downsampling of sorts, but a convolution layer. What this would result in is all the layers having the pretrained weights, except the first one, which would come from my input image.
Does this sound like something tenable? What would be a way to proceed here?
Thank you for the help.