📊 Wake Vision是一个包含约600万张图像的大规模数据集，专为微机器学习（TinyML）领域的人检测任务设计，其规模比之前的VWW数据集大了近100倍，为该领域提供了关键的高质量数据支持。
🔍 该数据集提供两种训练集版本：Wake Vision（大型）侧重于数据集的规模，Wake Vision（质量）则侧重于标签的质量，这种设计使得研究人员能够有效地探索数据集大小与质量之间的平衡。
📈 研究表明，对于参数数量较少的微机器学习模型，高质量的数据标签比单纯的大量数据更为重要，Wake Vision通过提供高质量标签，有效地提升了模型的训练效果和泛化能力。
🌐 Wake Vision不仅提供数据集，还提供了细粒度的基准测试，能够评估模型在真实世界应用中的表现，包括检测距离、光照条件、人物的不同描绘方式以及感知的性别和年龄等，有助于早期发现模型的偏见和局限性。
🏆 使用Wake Vision进行训练的模型取得了显著的性能提升，准确率比VWW数据集提高了6.6%，错误率从7.8%降低到2.2%，并在各种真实世界条件下展现出良好的鲁棒性，证明了该数据集的有效性。
Posted by Colby Banbury, Emil Njor, Andrea Mattia Garavagno, Vijay Janapa Reddi – Harvard University
TinyML is an exciting frontier in machine learning, enabling models to run on extremely low-power devices such as microcontrollers and edge devices. However, the growth of this field has been stifled by a lack of tailored large and high-quality datasets. That's where Wake Vision comes in—a new dataset designed to accelerate research and development in TinyML.
The development of TinyML requires compact and efficient models, often only a few hundred kilobytes in size. The applications targeted by standard machine learning datasets, like ImageNet, are not well-suited for these highly constrained models.
Existing datasets for TinyML, like Visual Wake Words (VWW), have laid the groundwork for progress in the field. However, their smaller size and inherent limitations pose challenges for training production-grade models. Wake Vision builds upon this foundation by providing a large, diverse, and high-quality dataset specifically tailored for person detection—the cornerstone vision task for TinyML.
Wake Vision is a new, large-scale dataset with roughly 6 million images, almost 100 times larger than VWW, the previous state-of-the-art dataset for person detection in TinyML. The dataset provides two distinct training sets:
Wake Vision's comprehensive filtering and labeling process significantly enhances the dataset's quality.
In traditional overparameterized models, it is widely believed that data quantity matters more than data quality, as an overparameterized model can adapt to errors in the training data. But according to the image below, TinyML tells a different story:
The figure above shows that high-quality labels (less error) are more beneficial for under-parameterized models than simply having more data. Larger, error-prone datasets can still be valuable when paired with fine-grained techniques.
By providing two versions of the training set, Wake Vision enables researchers to explore the balance between dataset size and quality effectively.
Unlike many open-source datasets, Wake Vision offers fine-grained benchmarks and detailed tests for real-world applications like those shown in the above figure. These enable the evaluation of model performance in real-world scenarios, such as:
These benchmarks give researchers a nuanced understanding of model performance in specific, real-world contexts and help identify potential biases and limitations early in the design phase.
The performance gains achieved using Wake Vision are impressive:
Furthermore, combining the two Wake Vision training sets, using the larger set for pre-training and the quality set for fine-tuning, yields the best results, highlighting the value of both datasets when used in sophisticated training pipelines.
The Wake Vision website features a Leaderboard, providing a dedicated platform to assess and compare the performance of models trained on the Wake Vision dataset.
The leaderboard enables a clear and detailed view of how models perform under various conditions, with performance metrics like accuracy, error rates, and robustness across diverse real-world scenarios. It’s an excellent resource for both seasoned researchers and newcomers looking to improve and validate their approaches.
Explore the leaderboard to see the current rankings, learn from high-performing models, and submit your own to contribute to advancing the state of the art in TinyML person detection.
Wake Vision is available through popular dataset services such as:
With its permissive license (CC-BY 4.0), researchers and practitioners can freely use and adapt Wake Vision for their TinyML projects.
The Wake Vision team has made the dataset, code, and benchmarks publicly available to accelerate TinyML research and enable the development of better, more reliable person detection models for ultra-low-power devices.
To learn more and access the dataset, visit Wake Vision’s website, where you can also check out a leaderboard of top-performing models on the Wake Vision dataset - and see if you can create better performing models!
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