Thanks @jeremy. Taking your responses together with the fact that we freeze the convolutional layers when we change the image sizes with
set_data, train them, and then unfreeze the layers and then train again, suggest that we are still worried about the impact of different image sizes on the weights in the convolutional layer, even though these images are significantly different from ImageNet.
Is the thinking behind the freezing and unfreezing when we change sizes that when you change the sizes of the images, the weights in the fully connected layers, although they aren’t random anymore, really should be tuned to the new image sizes before we unfreeze and train the convolutional layers on the new image sizes? Is this something you just learned from trial and error or is there a theory you can articulate behind this?
I get that you shouldn’t unfreeze the convolutional layers when the fully-connected layers are initially random, but I guess I’m having trouble getting comfortable extending that insight to when we’ve already trained the fully connected layers, albeit on differently sized images.