- 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, 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."
Homepage: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi%3A10.7910%2FDVN%2F1HOPXC
Source code:
tfds.datasets.wake_vision.Builder
Versions:
1.0.0
(default): Initial TensorFlow Datasets release. Note that this is based on the 2.0 version of Wake Vision on Harvard Dataverse.
Download size:
Unknown size
Dataset size:
239.25 GiB
Auto-cached (documentation): No
Splits:
Split | Examples |
---|---|
'test' |
55,763 |
'train_large' |
5,760,428 |
'train_quality' |
1,248,230 |
'validation' |
18,582 |
- Feature structure:
FeaturesDict({
'age_unknown': ClassLabel(shape=(), dtype=int64, num_classes=2),
'body_part': ClassLabel(shape=(), dtype=int64, num_classes=2),
'bright': ClassLabel(shape=(), dtype=int64, num_classes=2),
'dark': ClassLabel(shape=(), dtype=int64, num_classes=2),
'depiction': ClassLabel(shape=(), dtype=int64, num_classes=2),
'far': ClassLabel(shape=(), dtype=int64, num_classes=2),
'filename': Text(shape=(), dtype=string),
'gender_unknown': ClassLabel(shape=(), dtype=int64, num_classes=2),
'image': Image(shape=(None, None, 3), dtype=uint8),
'medium_distance': ClassLabel(shape=(), dtype=int64, num_classes=2),
'middle_age': ClassLabel(shape=(), dtype=int64, num_classes=2),
'near': ClassLabel(shape=(), dtype=int64, num_classes=2),
'non-person_depiction': ClassLabel(shape=(), dtype=int64, num_classes=2),
'non-person_non-depiction': ClassLabel(shape=(), dtype=int64, num_classes=2),
'normal_lighting': ClassLabel(shape=(), dtype=int64, num_classes=2),
'older': ClassLabel(shape=(), dtype=int64, num_classes=2),
'person': ClassLabel(shape=(), dtype=int64, num_classes=2),
'person_depiction': ClassLabel(shape=(), dtype=int64, num_classes=2),
'predominantly_female': ClassLabel(shape=(), dtype=int64, num_classes=2),
'predominantly_male': ClassLabel(shape=(), dtype=int64, num_classes=2),
'young': ClassLabel(shape=(), dtype=int64, num_classes=2),
})
- Feature documentation:
Feature | Class | Shape | Dtype | Description |
---|---|---|---|---|
FeaturesDict | ||||
age_unknown | ClassLabel | int64 | ||
body_part | ClassLabel | int64 | ||
bright | ClassLabel | int64 | ||
dark | ClassLabel | int64 | ||
depiction | ClassLabel | int64 | ||
far | ClassLabel | int64 | ||
filename | Text | string | ||
gender_unknown | ClassLabel | int64 | ||
image | Image | (None, None, 3) | uint8 | |
medium_distance | ClassLabel | int64 | ||
middle_age | ClassLabel | int64 | ||
near | ClassLabel | int64 | ||
non-person_depiction | ClassLabel | int64 | ||
non-person_non-depiction | ClassLabel | int64 | ||
normal_lighting | ClassLabel | int64 | ||
older | ClassLabel | int64 | ||
person | ClassLabel | int64 | ||
person_depiction | ClassLabel | int64 | ||
predominantly_female | ClassLabel | int64 | ||
predominantly_male | ClassLabel | int64 | ||
young | ClassLabel | int64 |
Supervised keys (See
as_supervised
doc):('image', 'person')
Figure (tfds.show_examples):
Examples (tfds.as_dataframe): Missing.
Citation:
@article{banbury2024wake,
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}
}