RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy

TL;DR

RamanBench is a comprehensive, reproducible benchmark that unifies diverse Raman spectroscopy datasets, evaluates multiple models, and aims to accelerate ML progress in molecular analysis applications.

Contribution

It introduces the first large-scale, standardized Raman spectroscopy benchmark with 74 datasets, evaluation protocols, and a live leaderboard to foster community-driven advancements.

Findings

TFM models outperform classical and Raman-specific methods

Time-series models are competitive with specialized approaches

No current method generalizes well across all datasets

Abstract

Machine Learning (ML) has transformed many scientific fields, yet key applications still lack standardized benchmarks. Raman spectroscopy, a widely used technique for non-invasive molecular analysis, is one such field where progress is limited by fragmented datasets, inconsistent evaluation, and models that fail to capture the structure of spectral data. We introduce RamanBench, the first large-scale, fully reproducible benchmark for ML on Raman spectroscopy, consisting of streamlined data access, evaluation protocols and code, as well as a live leaderboard. It unifies 74 datasets (including 16 first released with this benchmark) across four domains, comprising 325,668 spectra and spanning classification and regression tasks under diverse experimental conditions. We benchmark 28 models under a standardized protocol, including classical methods (e.g., PLS), Raman-specific (e.g., RamanNet), Tabular Foundation Model (TFM) (e.g., TabPFN), and time-series approaches (e.g., ROCKET). TFM consistently outperform domain-specific and gradient boosting baselines, while time-series models remain competitive. However, no method generalizes across datasets, revealing a fundamental gap. Therefore, we invite the community to contribute new approaches to our living benchmark, with the potential to accelerate advances in critical applications such as medical diagnostics, biological research, and materials science.