| --- |
| 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} |
| } |
