# Person Detection

Published articles for Person Detection.

This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles.

## Introducing Wake Vision: A High-Quality, Large-Scale Dataset for TinyML Computer Vision Applications

DevFeed: [Introducing Wake Vision: A High-Quality, Large-Scale Dataset for TinyML Computer Vision Applications](<https://devfeed.tech/articles/introducing-wake-vision-a-high-quality-large-scale-dataset-for-tinyml-computer-vision-applications-7419.md>)

Original publisher: [Read original article](<https://blog.tensorflow.org/2024/12/introducing-wake-vision-new-dataset-for-person-detection-in-tinyml.html>)

Author: TensorFlow Blog (noreply@blogger.com)

Published: 2024-12-05T17:00:00Z

Content type: article

Language: en

Sources: [The TensorFlow Blog](<https://devfeed.tech/sources/the-tensorflow-blog.md>)

Topics: [Training AI Models](<https://devfeed.tech/topics/training-ai-models.md>), [machine learning overfitting](<https://devfeed.tech/topics/machine-learning-overfitting.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [community](<https://devfeed.tech/tags/community.md>), [computer-vision](<https://devfeed.tech/tags/computer-vision.md>), [data-quality](<https://devfeed.tech/tags/data-quality.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [edge](<https://devfeed.tech/tags/edge.md>), [google-ai](<https://devfeed.tech/tags/google-ai.md>), [litert](<https://devfeed.tech/tags/litert.md>), [machine-learning](<https://devfeed.tech/tags/machine-learning.md>), [on-device-ai](<https://devfeed.tech/tags/on-device-ai.md>), [person-detection](<https://devfeed.tech/tags/person-detection.md>), [research](<https://devfeed.tech/tags/research.md>), [tensorflow-lite](<https://devfeed.tech/tags/tensorflow-lite.md>), [training](<https://devfeed.tech/tags/training.md>)

### AI overview

Wake Vision is a roughly 6-million-image dataset for TinyML person detection. It provides large and quality-focused training sets, emphasizing that accurate labels can be especially valuable for constrained models.

### Source excerpt

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. Why TinyML Needs Better Data 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. What Makes Wake Vision Different? 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 (Large): Prioritizes dataset size. Wake Vision (Quality): Prioritizes label quality. Wake Vision's comprehensive filtering and labeling process significantly enhances the dataset's quality. Why Data Quality Matters for TinyML Models 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 mo