RamanBench：拉曼光谱机器学习的大规模基准
RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy
AI总结：
针对拉曼光谱机器学习缺乏标准化基准的问题，提出RamanBench，统一74个数据集、325,668条光谱，评估28个模型，发现表格基础模型表现最佳但跨数据集泛化不足，邀请社区贡献新方法。
摘要：
机器学习（ML）已变革众多科学领域，但关键应用仍缺乏标准化基准。拉曼光谱是一种广泛用于非侵入性分子分析的技术，其进展受限于数据集碎片化、评估不一致以及无法捕捉光谱数据结构的模型。我们推出RamanBench，这是首个用于拉曼光谱机器学习的大规模、完全可复现的基准，包含简化的数据访问、评估协议和代码，以及实时排行榜。它统一了四个领域的74个数据集（其中16个随本基准首次发布），涵盖325,668条光谱，并在多样实验条件下覆盖分类和回归任务。我们在标准化协议下对28个模型进行基准测试，包括经典方法（如PLS）、拉曼专用方法（如RamanNet）、表格基础模型（TFM）（如TabPFN）以及时间序列方法（如ROCKET）。TFM持续优于领域专用和梯度提升基线，而时间序列模型保持竞争力。然而，没有方法能跨数据集泛化，揭示了根本性差距。因此，我们邀请社区为这一活基准贡献新方法，有望加速医学诊断、生物学研究和材料科学等关键应用的进展。
英文摘要：
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 (https://pypi.org/project/raman-data/), evaluation protocols and code (https://pypi.org/project/raman-bench/), as well as a live leaderboard (https://huggingface.co/spaces/HTW-KI-Werkstatt/RamanBench). It unifies 77 datasets (including 17 first released with this benchmark) across four domains, comprising 325,862 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 models (TFMs; e.g., TabPFN), and time-series approaches (e.g., ROCKET). TFMs consistently outperform domain-specific and gradient boosting baselines, while time-series models remain competitive. However, no single method dominates across these heterogeneous datasets, revealing that Raman spectroscopy remains an open challenge for machine learning. 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.
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