Wake Vision: A Tailored Dataset and Benchmark Suite for TinyML Computer Vision Applications

[2405.00892] Wake Vision: A Tailored Dataset and Benchmark Suite for TinyML Computer Vision Applications Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computer Vision and Pattern Recognition arXiv:2405.00892 (cs) [Submitted on 1 May 2024 ( v1 ), last revised 30 Apr 2026 (this version, v6)] Title: Wake Vision: A Tailored Dataset and Benchmark Suite for TinyML Computer Vision Applications Authors: Colby Banbury , Emil Njor , Andrea Mattia Garavagno , Mark Mazumder , Matthew Stewart , Pete Warden , Manjunath Kudlur , Nat Jeffries , Xenofon Fafoutis , Vijay Janapa Reddi View a PDF of the paper titled Wake Vision: A Tailored Dataset and Benchmark Suite for TinyML Computer Vision Applications, by Colby Banbury and 9 other authors View PDF HTML (experimental) Abstract: Tiny machine learning (TinyML) co-locates models with sensors on microcontrollers, where small models (which are disproportionately sensitive to label noise) and bespoke binary tasks (which lack standard benchmarks) make general-purpose dataset practices a poor fit. Visual Wake Words (VWW), the prior standard TinyML person detection benchmark, contains roughly 123K images and has an estimated label error rate of 7.8%, which limits its usefulness for production-grade systems. Manual labeling, however, is prohibitively expensive for the scale and diversity of TinyML use cases. We address this gap with the Wake Vision pipeline, an automated method for generating and curating large-scale binary classification datasets for TinyML. We use data-centric TinyML for the dataset construction, curation, and lifecycle methods that produce the large, well-curated datasets these systems require. The pipeline combines label fusion across image-level and bounding-box sources, confidence-, area-, and depiction-aware filtering, label correction on the evaluation splits, and automatic generation of fine-grained benchmark subsets. Applying it to person detection, we release Wake Vision, a dataset of almost 6M images (close to 100x more person images than VWW) with a manually relabeled validation and test set at a 2.2% label error rate. Models trained on Wake Vision improve test accuracy by up to 6.6% over VWW across MobileNetV2, MCUNet, MicroNets, and ColabNAS architectures, and match or exceed VWW-trained models on 13 of 16 fine-grained subsets covering perceived gender, perceived age, distance, lighting, and depictions. The advantage holds under distribution shift on three out-of-distribution datasets covering driving and overhead-surveillance imagery. All artifacts are released under CC-BY 4.0 through TensorFlow Datasets and Hugging Face. Subjects: Computer Vision and Pattern Recognition (cs.CV) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2405.00892 [cs.CV] (or arXiv:2405.00892v6 [cs.CV] for this version) https://doi.org/10.48550/arXiv.2405.00892 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Colby Banbury [ view email ] [v1] Wed, 1 May 2024 22:33:45 UTC (8,947 KB) [v2] Thu, 6 Jun 2024 16:21:08 UTC (9,637 KB) [v3] Wed, 27 Nov 2024 19:01:52 UTC (10,685 KB) [v4] Mon, 9 Dec 2024 17:35:55 UTC (10,685 KB) [v5] Mon, 2 Jun 2025 17:23:34 UTC (11,603 KB) [v6] Thu, 30 Apr 2026 18:09:59 UTC (27,880 KB) Full-text links: Access Paper: View a PDF of the paper titled Wake Vision: A Tailored Dataset and Benchmark Suite for TinyML Computer Vision Applications, by Colby Banbury and 9 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CV < prev | next > new | recent | 2024-05 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... 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