# Benchmarking Deep Learning Models for Raman Spectroscopy Across Open-Source Datasets

arXiv: https://arxiv.org/abs/2601.16107 · HTML: https://mlsanity.com/paper/2601.16107/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 61 references that are in the map.
- First posted on arXiv 22 Jan 2026.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Deep Convolutional Neural Networks for Raman Spectrum Recognition: A Unified Solution](https://mlsanity.com/paper/1708.09022/) (2017)
- [Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning](https://mlsanity.com/paper/1901.07666/) (2019)
- [RamanNet: A generalized neural network architecture for Raman Spectrum Analysis](https://mlsanity.com/paper/2201.09737/) (2022)
- [Robust Classification of High-Dimensional Spectroscopy Data Using Deep Learning and Data Synthesis](https://mlsanity.com/paper/2003.11842/) (2020)
- [A Self-supervised Learning Method for Raman Spectroscopy based on Masked Autoencoders](https://mlsanity.com/paper/2504.16130/) (2025)

## Built on by (most cited first, 1 of 1)

- [RamanBench: A Large-Scale Benchmark for Machine Learning on Raman Spectroscopy](https://mlsanity.com/paper/2605.02003/) (2026)

## Abstract (arXiv)

Deep learning classifiers for Raman spectroscopy are increasingly reported to outperform classical chemometric approaches. However, their evaluations are often conducted in isolation or compared against traditional machine learning methods or trivially adapted vision-based architectures that were not originally proposed for Raman spectroscopy. As a result, direct comparisons between existing deep learning models developed specifically for Raman spectral analysis on shared open-source datasets remain scarce. In this work, we focus on supervised Raman spectra classification where each spectrum is assigned to a predefined material, bacterial/yeast isolate, drug treatment or pharmaceutical compound. To the best of our knowledge, this study presents one of the first benchmarks comparing three or more published Raman-specific deep learning classifiers across multiple open-source Raman datasets. We evaluate five representative Deep Learning (DL) architectures along with two conventional Machine Learning (ML) methods under a unified training and hyperparameter tuning protocol across three open-source Raman datasets selected to support standard evaluation, fine-tuning, and explicit distribution-shift testing. In this comparative study, we primarily focus on classification because the selected open-source datasets provide classification annotations, while annotations for complete structure elucidation are not available. We report classification accuracies and macro-averaged F1 scores to provide a fair and reproducible comparison of the supervised ML and DL models for Raman spectra based classification.