Wake Vision: A Tailored Dataset and Benchmark Suite for TinyML Computer Vision Applications
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866914522096205824 |
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| author | Banbury, Colby Njor, Emil Garavagno, Andrea Mattia Mazumder, Mark Stewart, Matthew Warden, Pete Kudlur, Manjunath Jeffries, Nat Fafoutis, Xenofon Reddi, Vijay Janapa |
| author_facet | Banbury, Colby Njor, Emil Garavagno, Andrea Mattia Mazumder, Mark Stewart, Matthew Warden, Pete Kudlur, Manjunath Jeffries, Nat Fafoutis, Xenofon Reddi, Vijay Janapa |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2405_00892 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Wake Vision: A Tailored Dataset and Benchmark Suite for TinyML Computer Vision Applications Banbury, Colby Njor, Emil Garavagno, Andrea Mattia Mazumder, Mark Stewart, Matthew Warden, Pete Kudlur, Manjunath Jeffries, Nat Fafoutis, Xenofon Reddi, Vijay Janapa Computer Vision and Pattern Recognition Artificial Intelligence 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. |
| title | Wake Vision: A Tailored Dataset and Benchmark Suite for TinyML Computer Vision Applications |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2405.00892 |