Datasets:
license: cc-by-4.0
dataset_info:
features:
- name: age_unknown
dtype:
class_label:
names:
'0': 'No'
'1': 'Yes'
- name: body_part
dtype:
class_label:
names:
'0': 'No'
'1': 'Yes'
- name: bright
dtype:
class_label:
names:
'0': 'No'
'1': 'Yes'
- name: dark
dtype:
class_label:
names:
'0': 'No'
'1': 'Yes'
- name: depiction
dtype:
class_label:
names:
'0': 'No'
'1': 'Yes'
- name: far
dtype:
class_label:
names:
'0': 'No'
'1': 'Yes'
- name: filename
dtype: string
- name: gender_unknown
dtype:
class_label:
names:
'0': 'No'
'1': 'Yes'
- name: image
dtype: image
- name: medium_distance
dtype:
class_label:
names:
'0': 'No'
'1': 'Yes'
- name: middle_age
dtype:
class_label:
names:
'0': 'No'
'1': 'Yes'
- name: near
dtype:
class_label:
names:
'0': 'No'
'1': 'Yes'
- name: non-person_depiction
dtype:
class_label:
names:
'0': 'No'
'1': 'Yes'
- name: non-person_non-depiction
dtype:
class_label:
names:
'0': 'No'
'1': 'Yes'
- name: normal_lighting
dtype:
class_label:
names:
'0': 'No'
'1': 'Yes'
- name: older
dtype:
class_label:
names:
'0': 'No'
'1': 'Yes'
- name: person
dtype:
class_label:
names:
'0': 'No'
'1': 'Yes'
- name: person_depiction
dtype:
class_label:
names:
'0': 'No'
'1': 'Yes'
- name: predominantly_female
dtype:
class_label:
names:
'0': 'No'
'1': 'Yes'
- name: predominantly_male
dtype:
class_label:
names:
'0': 'No'
'1': 'Yes'
- name: young
dtype:
class_label:
names:
'0': 'No'
'1': 'Yes'
splits:
- name: test
num_bytes: 15119280526
num_examples: 53304
- name: validation
num_bytes: 5013154770.625
num_examples: 17627
download_size: 20127967346
dataset_size: 20132435296.625
configs:
- config_name: default
data_files:
- split: train_quality
path: data/train_quality*
- split: test
path: data/test-*
- split: validation
path: data/validation-*
task_categories:
- image-classification
pretty_name: Wake Vision
size_categories:
- 1M<n<10M
Dataset Card for Wake Vision
Dataset Details
Dataset Description
Paper abstract:
Abstract. Machine learning applications on extremely low-power de- vices, commonly referred to as tiny machine learning (TinyML), promises a smarter and more connected world. However, the advancement of cur- rent TinyML research is hindered by the limited size and quality of per- tinent datasets. To address this challenge, we introduce Wake Vision, a large-scale, diverse dataset tailored for person detection—the canonical task for TinyML visual sensing. Wake Vision comprises over 6 million images, which is a hundredfold increase compared to the previous stan- dard, and has undergone thorough quality filtering. Using Wake Vision for training results in a 2.41% increase in accuracy compared to the estab- lished benchmark. Alongside the dataset, we provide a collection of five detailed benchmark sets that assess model performance on specific seg- ments of the test data, such as varying lighting conditions, distances from the camera, and demographic characteristics of subjects. These novel fine-grained benchmarks facilitate the evaluation of model quality in chal- lenging real-world scenarios that are often ignored when focusing solely on overall accuracy. Through an evaluation of a MobileNetV2 TinyML model on the benchmarks, we show that the input resolution plays a more crucial role than the model width in detecting distant subjects and that the impact of quantization on model robustness is minimal, thanks to the dataset quality. These findings underscore the importance of a de- tailed evaluation to identify essential factors for model development. The dataset, benchmark suite, code, and models are publicly available under the CC-BY 4.0 license, enabling their use for commercial use cases
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Recommendations
Citation
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}
}
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