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raman-bench

A benchmark for machine learning on Raman spectroscopy

Chart: model performance on RamanBench against the model's release date, from PLS to TabPFN

The Python package behind RamanBench, our NeurIPS 2026 benchmark. It loads 74 Raman datasets with fixed splits and scores your model against 28 baselines, without re-running them.

RamanBench collects 74 public Raman datasets with 163 prediction targets from four application domains (material science, biological, medical, chemical) behind one evaluation protocol. The package ships the results of 28 baseline models, from classical chemometrics and gradient boosting to deep networks built for spectra and tabular foundation models.

With pip install raman-bench you can load every dataset with its fixed train/test split (through raman-data), read the precomputed baseline results, which works offline, and score your own model against them. Any scikit-learn-compatible estimator works, whichever library it comes from. pip install "raman-bench[models]" adds the built-in Raman-specific architectures and tabular foundation models.

Results can be submitted to the live leaderboard on Hugging Face. The benchmark and its results are described in our NeurIPS 2026 paper.

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