Deep-Metric-Learning-Baselines

Why we included this project

Training image embeddings for retrieval or verification usually comes down to picking a loss and a sampling strategy and seeing how they interact. This repository collects the classic deep metric learning losses, triplet, margin, ProxyNCA, and N-pair, along with the sampling methods that pair with them, and runs them on standard fine-grained benchmarks like CUB-200, CARS196, and Stanford Online Products. The code is kept deliberately small, a single training script, a losses module, and dataloaders for the common datasets, so you can trace how a method is actually implemented instead of hunting through a large framework. That makes it useful for researchers who want a clean baseline to compare against and for students trying to understand how metric learning is wired up in PyTorch. One honest caveat: this is a research codebase rather than a maintained production library, and the authors point to a newer repository with the full evaluation suite.