Datasets:
File size: 4,842 Bytes
1c54ab0 3dc105d 1c54ab0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 | ---
pretty_name: SmellNet
license: mit
task_categories:
- tabular-classification
tags:
- olfaction
- smell
- sensor
- time-series
- chemistry
- gcms
configs:
- config_name: base_data
default: true
data_files: "base_data/**/*.csv"
- config_name: gcms_processed
data_files:
- "gcms_processed/*.csv"
- "gcms_processed/*.parquet"
- config_name: mixture_training_seen
data_files: "mixture_data/training_seen/**/*.csv"
- config_name: mixture_training_new
data_files: "mixture_data/training_new/**/*.csv"
- config_name: mixture_test_seen
data_files: "mixture_data/test_seen/**/*.csv"
- config_name: mixture_test_unseen
data_files: "mixture_data/test_unseen/**/*.csv"
- config_name: mixture_index
data_files:
- "mixture_data/train_index_seen.csv"
- "mixture_data/test_index_seen.csv"
- "mixture_data/test_index_unseen.csv"
---
# SmellNet
SmellNet is a comparatively large dataset for **sensor-based machine olfaction**: real-world smell measurements collected from a compact array of low-cost metal-oxide (MOX) gas sensors.
The dataset supports:
- **single-substance recognition** from multichannel sensor time series
- **mixture distribution prediction** from odor mixtures
- **cross-modal learning** with paired GC-MS-derived chemistry priors
- research on **temporal modeling** for smell sensing
The accompanying paper introduces **ScentFormer**, a Transformer-based model for smell recognition and mixture prediction.
## Highlights
- ~**828,000** time-series readings
- **50** base substances across nuts, spices, herbs, fruits, and vegetables
- **43** mixtures with fixed ingredient volumetric ratios
- ~**68 hours** of controlled acquisition
- headline results from the paper:
- **63.3% Top-1 accuracy** on **SmellNet-Base** with GC-MS supervision
- **50.2% Top-1@0.1** on **seen mixtures**
## Repository structure
This Hugging Face repo contains the dataset assets used in the SmellNet project.
- `base_data/`
Curated base-substance sensor data used for the main paper experiments.
This corresponds to the **SmellNet-Base** task.
- `mixture_data/`
Mixture sensor data for predicting normalized ingredient ratios over 12 odorants.
This corresponds to the **SmellNet-Mixture** task.
- `gcms_processed/`
Processed GC-MS-derived features and chemistry priors used for cross-modal learning.
- `gcms_data/`
Raw or intermediate GC-MS-related files used to construct chemistry-aware representations.
## Tasks
### 1. SmellNet-Base
The base task focuses on **single-substance classification** from multichannel sensor time series.
- 50 substances
- 5 broad categories:
- nuts
- spices
- herbs
- fruits
- vegetables
- controlled repeated sensing sessions across different days
### 2. SmellNet-Mixture
The mixture task focuses on **mixture composition prediction** from sensor time series.
- 12 odorants
- 43 mixture configurations
- prediction target is a normalized ingredient-ratio vector
## Sensor setup
The SmellNet paper uses low-cost portable MOX sensors.
For the base-substance setting, the reported experiments use **6 sensor channels**.
For the mixture setting, the reported experiments use **4 Grove channels** collected at a higher sampling rate.
These signals are best interpreted as **relative sensor responses and temporal dynamics**, rather than calibrated absolute chemical concentrations.
## What is included here
This dataset repo is meant to host the **data assets**.
For:
- training code
- preprocessing code
- modeling code
- analysis scripts
- paper figures
please see the companion GitHub repository:
**GitHub:** `https://github.com/MIT-MI/SmellNet`
## Intended use
SmellNet is intended for research on:
- machine olfaction
- temporal representation learning
- multimodal learning with chemistry priors
- mixture prediction
- low-cost sensor-based perception
Potential applications include food analysis, environmental monitoring, healthcare-related sensing research, and interactive multimodal systems.
## Notes and limitations
- Data were collected in a **controlled environment**, so real-world generalization to unseen environments may be harder.
- Sensor channels are **manufacturer-labeled responses** and are not direct measurements of pure analytes in food headspace.
- GC-MS information is used as an **ingredient-level chemistry prior**, not as a per-sample test-time input.
- Performance on **unseen mixtures** remains significantly harder than on seen mixtures.
## Citation
If you use this dataset, please cite:
```bibtex
@inproceedings{feng2026smellnet,
title={SmellNet: A Large-Scale Dataset for Real-World Smell Recognition},
author={Feng, Dewei and Dai, Wei and Li, Carol and Pernigo, Alistair and Liang, Paul Pu},
booktitle={International Conference on Learning Representations (ICLR)},
year={2026}
} |