# SmellNet: A Large-scale Dataset for Real-world Smell Recognition

arXiv: https://arxiv.org/abs/2506.00239 · HTML: https://mlsanity.com/paper/2506.00239/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 56 references that are in the map.
- First posted on arXiv 30 May 2025.

## Builds on (most cited first, 6 of 6)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [ChemBERTa: Large-Scale Self-Supervised Pretraining for Molecular Property Prediction](https://mlsanity.com/paper/2010.09885/) (2020)
- [Vision-Language Pre-training: Basics, Recent Advances, and Future Trends](https://mlsanity.com/paper/2210.09263/) (2022)
- [Foundations and Trends in Multimodal Machine Learning: Principles, Challenges, and Open Questions](https://mlsanity.com/paper/2209.03430/) (2022)
- [Machine Learning for Scent: Learning Generalizable Perceptual Representations of Small Molecules](https://mlsanity.com/paper/1910.10685/) (2019)
- [Multidimensional analysis using sensor arrays with deep learning for high-precision and high-accuracy diagnosis](https://mlsanity.com/paper/2211.17139/) (2022)

## Built on by (most cited first, 9 of 9)

- [Uncertainty Quantification for Multimodal Large Language Models with Incoherence-adjusted Semantic Volume](https://mlsanity.com/paper/2602.24195/) (2026)
- [New York Smells: A Large Multimodal Dataset for Olfaction](https://mlsanity.com/paper/2511.20544/) (2025)
- [AromaGen: Interactive Generation of Rich Olfactory Experiences with Multimodal Language Models](https://mlsanity.com/paper/2604.01650/) (2026)
- [See & Sniff: Learning Visuo-Olfactory Representations](https://mlsanity.com/paper/2606.27307/) (2026)
- [A Vision for Multisensory Intelligence: Sensing, Science, and Synergy](https://mlsanity.com/paper/2601.04563/) (2026)
- [SCENT: Aligning Mass Spectra with Molecular Structure for Olfactory Perception](https://mlsanity.com/paper/2605.27009/) (2026)
- [What Images Cannot Say: Language-Guided Olfactory Representation Learning](https://mlsanity.com/paper/2607.06402/) (2026)
- [Physics Closure Matters for Machine Olfaction: A Maxwell-Stefan Graph Solver for Identifiable Dynamic Gas Unmixing](https://mlsanity.com/paper/2607.18544/) (2026)
- [LDAC-Net: A Learnable Multi-Lag Differencing Attention-Convolution Network for Drift-Robust Recognition with Low-Cost MOX Gas Sensors](https://mlsanity.com/paper/2608.25646/) (2026)

## Abstract (arXiv)

The ability of AI to sense and identify various substances based on their smell alone can have profound impacts on allergen detection (e.g. smelling gluten or peanuts in a cake), monitoring the manufacturing process, and sensing hormones that indicate emotional states, stress levels, and diseases. Despite these broad impacts, there are few standardized datasets, and therefore little progress, for training and evaluating AI systems' ability to `smell' in the real-world. In this paper, we use small gas and chemical sensors to create SmellNet, a comparatively large dataset for sensor-based machine olfaction that digitizes a diverse range of smells in the natural world. SmellNet contains about 828,000 time-series data points across 50 substances, spanning nuts, spices, herbs, fruits, and vegetables, and 43 mixtures among them with fixed ingredient volumetric ratios, with 68 hours of data collected. Using SmellNet, we developed ScentFormer, a Transformer-based architecture combining temporal differencing and sliding-window augmentation for smell data. For the SmellNet-Base classification tasks, ScentFormer achieves 63.3% Top-1 accuracy with GC-MS supervision, and for the SmellNet-Mixture distribution prediction tasks, ScentFormer achieves 50.2% Top-1@0.1 on the test-seen split. ScentFormer's ability to generalize across conditions and capture transient chemical dynamics demonstrates the promise of temporal modeling in sensor-based olfactory AI. SmellNet and ScentFormer lay the groundwork for sensor-based olfactory applications across healthcare, food and beverage, environmental monitoring, manufacturing, and entertainment.