Transfer learning(VGG, ResNet) with different image sizes

Question

I wish to use transfer learning to process images, and my images have different sizes. I think in general convolutional layers can take variable input size, but fully connected layers can only take input of specific size. However, the Keras implementation of VGG-16 or ResNet50 can take any image size larger than 32x32, although they do have fully connected layers. I wonder how it is done to get fix fully connected layer size for different image dimensions?

Thanks very much!

Answer 1

What you are saying is misleading, you can build a VGG/ResNet Keras model with any input image size larger than 32x32, but once the model is built, you can't change the input size, and that is usually the problem. So the model cannot really take variable sized images.