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Harvard-Edge/Wake-Vision

Dataset Card for Wake Vision Dataset Description "Wake Vision" is a large, high-quality dataset featuring over 6 million images, significantly exceeding the scale and diversity of current tinyML datasets (100x). This dataset includes images with annotations of whether each image contains a person. Additionally, it incorporates a comprehensive fine-grained benchmark to assess fairness and robustness, covering perceived gender, perceived age, subject distance… See the full description on the dataset page: https://huggingface.co/datasets/Harvard-Edge/Wake-Vision.

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Dataset Card

Dataset Card for Wake Vision

Dataset Description

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"Wake Vision" is a large, high-quality dataset featuring over 6 million images, significantly exceeding the scale and diversity of current tinyML datasets (100x). This dataset includes images with annotations of whether each image contains a person. Additionally, it incorporates a comprehensive fine-grained benchmark to assess fairness and robustness, covering perceived gender, perceived age, subject distance, lighting conditions, and depictions.

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."

  • —License: [CC-BY 4.0]

Dataset Sources

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  • —Train (Large) Dataset: https://huggingface.co/datasets/Harvard-Edge/Wake-Vision-Train-Large
  • —Website: https://wakevision.ai/
  • —Repository: https://github.com/colbybanbury/WakeVisionQuickstart
  • —Paper: https://arxiv.org/abs/2405.00892

Citation

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BibTeX:

bibtex
@misc{banbury2024wake,
      title={Wake Vision: A Large-scale, Diverse Dataset and Benchmark Suite for TinyML Person Detection}, 
      author={Colby Banbury and Emil Njor and Matthew Stewart and Pete Warden and Manjunath Kudlur and Nat Jeffries and Xenofon Fafoutis and Vijay Janapa Reddi},
      year={2024},
      eprint={2405.00892},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

Dataset Card Contact

For questions about the dataset or image takedown requests, contact wakevision@edgeaifoundation.org