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

TL;DR

This paper introduces SmellNet, a large-scale dataset of sensor-based smell data, and ScentFormer, a Transformer model for smell classification and mixture prediction, advancing AI's olfactory capabilities.

Contribution

The creation of SmellNet dataset with 828,000 data points across 50 substances and the development of ScentFormer, a Transformer-based architecture for smell recognition, are novel contributions.

Findings

ScentFormer achieves 63.3% Top-1 accuracy on classification tasks.

ScentFormer achieves 50.2% Top-1@0.1 on mixture prediction.

Temporal modeling enhances sensor-based olfactory AI performance.

Abstract

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.