Datasets:

Modalities:
Image
Text
Formats:
parquet
ArXiv:
Libraries:
Datasets
Dask
License:
colbybanbury commited on
Commit
4a88d87
·
verified ·
1 Parent(s): f531831

Update README.md

Browse files
Files changed (1) hide show
  1. README.md +12 -120
README.md CHANGED
@@ -147,131 +147,26 @@ size_categories:
147
 
148
  # Dataset Card for Wake Vision
149
 
150
-
151
- ## Dataset Details
152
-
153
  ### Dataset Description
154
 
155
  <!-- Provide a longer summary of what this dataset is. -->
156
 
157
- Paper abstract:
158
-
159
- >Abstract. Machine learning applications on extremely low-power de-
160
- vices, commonly referred to as tiny machine learning (TinyML), promises
161
- a smarter and more connected world. However, the advancement of cur-
162
- rent TinyML research is hindered by the limited size and quality of per-
163
- tinent datasets. To address this challenge, we introduce Wake Vision, a
164
- large-scale, diverse dataset tailored for person detection—the canonical
165
- task for TinyML visual sensing. Wake Vision comprises over 6 million
166
- images, which is a hundredfold increase compared to the previous stan-
167
- dard, and has undergone thorough quality filtering. Using Wake Vision
168
- for training results in a 2.41% increase in accuracy compared to the estab-
169
- lished benchmark. Alongside the dataset, we provide a collection of five
170
- detailed benchmark sets that assess model performance on specific seg-
171
- ments of the test data, such as varying lighting conditions, distances from
172
- the camera, and demographic characteristics of subjects. These novel
173
- fine-grained benchmarks facilitate the evaluation of model quality in chal-
174
- lenging real-world scenarios that are often ignored when focusing solely
175
- on overall accuracy. Through an evaluation of a MobileNetV2 TinyML
176
- model on the benchmarks, we show that the input resolution plays a
177
- more crucial role than the model width in detecting distant subjects and
178
- that the impact of quantization on model robustness is minimal, thanks
179
- to the dataset quality. These findings underscore the importance of a de-
180
- tailed evaluation to identify essential factors for model development. The
181
- dataset, benchmark suite, code, and models are publicly available under
182
- the CC-BY 4.0 license, enabling their use for commercial use cases
183
 
184
- - **Curated by:** [More Information Needed]
185
- - **Funded by [optional]:** [More Information Needed]
186
- - **Shared by [optional]:** [More Information Needed]
187
- - **Language(s) (NLP):** [More Information Needed]
188
- - **License:** [More Information Needed]
189
 
190
- ### Dataset Sources [optional]
191
 
192
  <!-- Provide the basic links for the dataset. -->
193
 
194
- - **Repository:** [More Information Needed]
195
- - **Paper [optional]:** [More Information Needed]
196
- - **Demo [optional]:** [More Information Needed]
197
-
198
- ## Uses
199
-
200
- <!-- Address questions around how the dataset is intended to be used. -->
201
-
202
- ### Direct Use
203
-
204
- <!-- This section describes suitable use cases for the dataset. -->
205
-
206
- [More Information Needed]
207
-
208
- ### Out-of-Scope Use
209
-
210
- <!-- This section addresses misuse, malicious use, and uses that the dataset will not work well for. -->
211
-
212
- [More Information Needed]
213
-
214
- ## Dataset Structure
215
-
216
- <!-- 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. -->
217
-
218
- [More Information Needed]
219
-
220
- ## Dataset Creation
221
-
222
- ### Curation Rationale
223
-
224
- <!-- Motivation for the creation of this dataset. -->
225
-
226
- [More Information Needed]
227
-
228
- ### Source Data
229
-
230
- <!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->
231
-
232
- #### Data Collection and Processing
233
-
234
- <!-- This section describes the data collection and processing process such as data selection criteria, filtering and normalization methods, tools and libraries used, etc. -->
235
-
236
- [More Information Needed]
237
-
238
- #### Who are the source data producers?
239
-
240
- <!-- This section describes the people or systems who originally created the data. It should also include self-reported demographic or identity information for the source data creators if this information is available. -->
241
-
242
- [More Information Needed]
243
-
244
- ### Annotations [optional]
245
-
246
- <!-- If the dataset contains annotations which are not part of the initial data collection, use this section to describe them. -->
247
-
248
- #### Annotation process
249
-
250
- <!-- 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. -->
251
-
252
- [More Information Needed]
253
-
254
- #### Who are the annotators?
255
-
256
- <!-- This section describes the people or systems who created the annotations. -->
257
-
258
- [More Information Needed]
259
-
260
- #### Personal and Sensitive Information
261
-
262
- <!-- 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. -->
263
-
264
- [More Information Needed]
265
-
266
- ## Bias, Risks, and Limitations
267
-
268
- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
269
-
270
- [More Information Needed]
271
-
272
- ### Recommendations
273
-
274
- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
275
 
276
 
277
  ## Citation
@@ -290,9 +185,6 @@ the CC-BY 4.0 license, enabling their use for commercial use cases
290
  primaryClass={cs.CV}
291
  }
292
  ```
293
-
294
- [More Information Needed]
295
-
296
  ## Dataset Card Contact
297
 
298
- [More Information Needed]
 
147
 
148
  # Dataset Card for Wake Vision
149
 
 
 
 
150
  ### Dataset Description
151
 
152
  <!-- Provide a longer summary of what this dataset is. -->
153
 
154
+ "Wake Vision" is a large, high-quality dataset featuring over 6 million images, significantly exceeding the scale and diversity of
155
+ current tinyML datasets (100x). This dataset includes images with annotations of whether each image contains a person. Additionally,
156
+ it incorporates a comprehensive fine-grained benchmark to assess fairness and robustness, covering perceived gender, perceived age,
157
+ subject distance, lighting conditions, and depictions. Hosted on Harvard Dataverse, it provides images, CSV files, and code to generate
158
+ a Wake Vision TensorFlow Dataset. Annotations are published under a CC BY 4.0 license, and all images are sourced from the Open Images
159
+ v7 dataset under a CC BY 2.0 license.
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
160
 
161
+ - **License:** [CC-BY 4.0]
 
 
 
 
162
 
163
+ ### Dataset Sources
164
 
165
  <!-- Provide the basic links for the dataset. -->
166
 
167
+ - **Website:** https://wakevision.ai/
168
+ - **Repository:** https://github.com/colbybanbury/Wake_Vision_Quickstart
169
+ - **Paper:** https://arxiv.org/abs/2405.00892
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
170
 
171
 
172
  ## Citation
 
185
  primaryClass={cs.CV}
186
  }
187
  ```
 
 
 
188
  ## Dataset Card Contact
189
 
190
+ cbanbury@g.harvard.edu