This repository provides code for the prototypical networks paper (NeurIPS 2017), implementing an embedding-based few-shot classification method. The approach learns a metric space where class prototypes are computed from support examples, enabling classification from few labeled samples. It includes training and evaluation scripts with support for GPU acceleration and train/validation workflows.