---
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}
}