About
Wake Vision is a state-of-the-art person detection dataset specifically created for TinyML applications. It provides a comprehensive collection of high-quality images and precise annotations to train and evaluate machine learning models for efficient person detection on embedded and edge devices. Wake Vision also includes a fine-grain benchmark suite for evaluating the robustness of TinyML models.
The Dataset
Wake Vision is a large, high-quality binary image classifcation dataset for person detection:
- Over 6 million high-quality images
- Two training sets (Large & Quality)
- High quality validation and test sets (~2% Label Error Rate)
Fine-Grain Benchmark Suite
Wake Vision also incorporates a comprehensive fine-grained benchmark to assess fairness and robustness across:
- Perceived gender
- Perceived age
- Subject distance
- Lighting conditions
- Depictions (e.g., drawings, digital renderings)
Access The Dataset
Key Features
TinyML Focus
TinyML relevant usescase and tractable task.
Two Training Sets
One large and one high quality, ideal for data-centric AI research
Diverse Scenarios
Wide range of person detection use cases
High-Quality Test and Val
Manually labeled to ensure reliable evaluation
Example Images
License
The Wake Vision labels are derived from Open Image's annotations which are licensed by Google LLC under CC BY 4.0 license. The images are listed as having a CC BY 2.0 license. Note from Open Images: "while we tried to identify images that are licensed under a Creative Commons Attribution license, we make no representations or warranties regarding the license status of each image and you should verify the license for each image yourself."
Cite
title={Wake Vision: A Large-scale, Diverse Dataset and Benchmark Suite for TinyML Person Detection},
author={Banbury, Colby and Njor, Emil and Stewart, Matthew and Warden, Pete and Kudlur, Manjunath and Jeffries, Nat and Fafoutis, Xenofon and Reddi, Vijay Janapa},
journal={arXiv preprint arXiv:2405.00892},
year={2024}
}
Contact
