In few-shot classification, should I use my custom dataset as the validation dataset and mini-ImageNet as the training dataset?

Question

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?