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. To the best of our knowledge, this study presents one of the first systematic benchmarks comparing three or more published Raman-specific deep learning classifiers across multiple open-source Raman datasets. We evaluate five representative deep learning architectures 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. We report classification accuracies and macro-averaged F1 scores to provide a fair and reproducible comparison of deep learning models for Raman spectra based classification.

翻译：针对拉曼光谱的深度学习分类器被越来越多地报道其性能优于经典化学计量学方法。然而，这些评估通常孤立进行，或仅与传统机器学习方法或未经专门设计、仅简单适配的视觉架构进行比较。因此，在共享开源数据集上，对专门为拉曼光谱分析开发的现有深度学习模型进行直接比较的研究仍然匮乏。据我们所知，本研究首次系统性地在多个开源拉曼数据集上对三个或更多已发表的拉曼专用深度学习分类器进行了基准测试。我们在三个精选的开源拉曼数据集上，采用统一的训练与超参数调优协议，评估了五种代表性深度学习架构，这些数据集旨在支持标准评估、微调及显式分布偏移测试。我们报告了分类准确率与宏平均F1分数，以提供基于拉曼光谱分类的深度学习模型公平且可复现的比较。