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README.md

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-license: mit
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+---
+pretty_name: SmellNet
+license: mit
+---
+
+# 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}
+}