Recently I just started to use the pre-trained model for transfer learning but some few silly things became so annoying to me.
I have a dataset where image resolution is about 180 by 180 ( training set ) and various sizes images in a test set. However, to reduce computing cost I resize them below 100, such as 32 or 48 or 77 etc. Now, according to the keras documentation some well known trained model can be used only if it got the desired image size. Now, my problem I faced is, though a pre-trained model named ResNet50 or MobileNet said that the minimum image size can be 32 in the Keras documentation but while training it tells that, the size should be 128 or something that is larger than 100.
The error showed:ValueError: If imagenet weights are being loaded, the input must have a static square shape (one of ....)
Now according to the showing error, it tells that, if I load weights I must give the image with the desired resolution. But If this true, then how should I use pre-trained weights for fine tuning for some specific image dataset which may low in resolution?
I think I’m surely missing some key points in Transfer Learning. So quick intuitive information will highly be appreciated. Thanks.