This repository implements Relational Knowledge Distillation, a technique for transferring structured knowledge from a larger teacher neural network to a smaller student network. The code supports metric learning experiments using architectures like ResNet, with distance-weighted sampling and triplet loss for training. It includes scripts for training teacher embeddings (e.g., ResNet50), evaluating performance, and distilling knowledge to student networks (e.g., ResNet18) for computer vision tasks like CUB-200.