README.md

---
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

<!-- Provide a longer summary of what this dataset is. -->

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

- **Curated by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]

### Dataset Sources [optional]

<!-- Provide the basic links for the dataset. -->

- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses

<!-- Address questions around how the dataset is intended to be used. -->

### Direct Use

<!-- This section describes suitable use cases for the dataset. -->

[More Information Needed]

### Out-of-Scope Use

<!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->

[More Information Needed]

## Dataset Structure

<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->

[More Information Needed]

## Dataset Creation

### Curation Rationale

<!-- Motivation for the creation of this dataset. -->

[More Information Needed]

### Source Data

<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->

#### Data Collection and Processing

<!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->

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#### Who are the source data producers?

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### Annotations [optional]

<!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->

#### Annotation process

<!-- This section describes the annotation process such as annotation tools used in the process, the amount of data annotated, annotation guidelines provided to the annotators, interannotator statistics, annotation validation, etc. -->

[More Information Needed]

#### Who are the annotators?

<!-- This section describes the people or systems who created the annotations. -->

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#### Personal and Sensitive Information

<!-- State whether the dataset contains data that might be considered personal, sensitive, or private (e.g., data that reveals addresses, uniquely identifiable names or aliases, racial or ethnic origins, sexual orientations, religious beliefs, political opinions, financial or health data, etc.). If efforts were made to anonymize the data, describe the anonymization process. -->

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## Bias, Risks, and Limitations

<!-- This section is meant to convey both technical and sociotechnical limitations. -->

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### Recommendations

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## Citation

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

**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}
}
```

[More Information Needed]

## Dataset Card Contact

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