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

[2506.00239] SmellNet: A Large-scale Dataset for Real-world Smell Recognition Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Artificial Intelligence arXiv:2506.00239 (cs) [Submitted on 30 May 2025 ( v1 ), last revised 13 Apr 2026 (this version, v5)] Title: SmellNet: A Large-scale Dataset for Real-world Smell Recognition Authors: Dewei Feng , Wei Dai , Carol Li , Alistair Pernigo , Yunge Wen , Paul Pu Liang View a PDF of the paper titled SmellNet: A Large-scale Dataset for Real-world Smell Recognition, by Dewei Feng and 5 other authors View PDF HTML (experimental) 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. Comments: Accepted to ICLR 2026; published as a conference paper at ICLR 2026. 32 pages; 21 figures Subjects: Artificial Intelligence (cs.AI) Cite as: arXiv:2506.00239 [cs.AI] (or arXiv:2506.00239v5 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2506.00239 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Dewei Feng [ view email ] [v1] Fri, 30 May 2025 21:15:25 UTC (30,528 KB) [v2] Sat, 11 Oct 2025 00:40:53 UTC (19,309 KB) [v3] Sun, 14 Dec 2025 06:52:37 UTC (19,305 KB) [v4] Thu, 18 Dec 2025 20:24:32 UTC (19,305 KB) [v5] Mon, 13 Apr 2026 18:12:04 UTC (17,705 KB) Full-text links: Access Paper: View a PDF of the paper titled SmellNet: A Large-scale Dataset for Real-world Smell Recognition, by Dewei Feng and 5 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.AI < prev | next > new | recent | 2025-06 Change to browse by: cs References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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