Tiny machine learning (TinyML) for low-power devices lacks robust datasets for development. We present Wake Vision, a large-scale dataset for person detection that contains over 6 million quality-filtered images. We provide two variants: Wake Vision (Large) and Wake Vision (Quality), leveraging the large variant for pretraining and knowledge distillation, while the higher-quality labels drive final model performance. The manually labeled validation and test sets reduce error rates from 7.8% to 2.2% compared to previous standards. In addition, we introduce five detailed benchmark sets to evaluate model performance in real-world scenarios, including varying lighting, camera distances, and demographic characteristics. Training with Wake Vision improves accuracy by 1.93% over existing datasets, demonstrating the importance of dataset quality for low-capacity models and dataset size for high-capacity models. The dataset, benchmarks, code, and models are available under the CC-BY 4.0 license, maintained by the Edge AI Foundation.

翻译：面向低功耗设备的微型机器学习（TinyML）领域缺乏用于开发的鲁棒数据集。本文提出Wake Vision，一个用于人体检测的大规模数据集，包含超过600万张经过质量筛选的图像。我们提供了两个变体：Wake Vision（大型版）和Wake Vision（质量版），利用大型版进行预训练与知识蒸馏，而更高质量的标注则用于提升最终模型性能。与先前标准相比，其人工标注的验证集和测试集将错误率从7.8%降至2.2%。此外，我们引入了五个细粒度基准测试集，用于评估模型在真实场景下的性能，涵盖不同光照条件、拍摄距离及人口统计学特征。使用Wake Vision进行训练，其准确率较现有数据集提升1.93%，这证明了数据集质量对低容量模型的重要性以及数据集规模对高容量模型的重要性。该数据集、基准测试、代码与模型均在CC-BY 4.0许可下由Edge AI基金会维护并提供。