I am trying to write a Siamese network of two embedding networks that share weights. I found several examples online for MNIST and other datasets of small images, but my images are much larger (around 500x1000). Please bear with me because I’m very confused, and I have several questions here!
How should I modify the embedding networks to take in larger images? Should I use a model made for larger images, like ResNet?
1a) And if I do that, should I resize my images to fit ResNet’s input size? Or is there a way to modify ResNet to work with larger images?
If I use a pretrained network, how should I modify it so that it still works with my dataset? (Do I train starting from the pretrained weights? Or do I just freeze those weights and work with that?
Do I need to remove the output layer of the model in order to get embeddings?
How would I incorporate all these things into my Siamese network code?