I am new to few-shot learning, and I wanted to get a hands-on understanding of it, using Reptile algorithm, applied to my custom dataset.
My custom dataset has 30 categories, with 5 images per category, so this would be a 30 way 5 shot.
Given a new image, I wish to be able to classify it into one of 30 categories. I changed
train_shots = 5,
classes = 30 in the linked example, and got the training output as
batch 0: train=0.050000 test=0.050000 batch 1: train=0.050000 test=0.050000
Should the custom dataset be used as a validation set, with mini-ImageNet as a training dataset, so that the knowledge is transferred? Or can I use only a custom dataset with only $30*5=150$ images for training?