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--- |
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annotations_creators: |
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- expert-generated |
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language_creators: |
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- found |
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language: |
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- en |
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license: |
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- cc-by-nc-nd-4.0 |
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multilinguality: |
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- monolingual |
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size_categories: |
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- 10K<n<100K |
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source_datasets: |
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- extended|other-wider |
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task_categories: |
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- object-detection |
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task_ids: |
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- face-detection |
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paperswithcode_id: wider-face-1 |
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pretty_name: WIDER FACE |
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--- |
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# Dataset Card for WIDER FACE |
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## Table of Contents |
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- [Table of Contents](#table-of-contents) |
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- [Dataset Description](#dataset-description) |
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- [Dataset Summary](#dataset-summary) |
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- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) |
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- [Languages](#languages) |
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- [Dataset Structure](#dataset-structure) |
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- [Data Instances](#data-instances) |
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- [Data Fields](#data-fields) |
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- [Data Splits](#data-splits) |
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- [Dataset Creation](#dataset-creation) |
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- [Curation Rationale](#curation-rationale) |
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- [Source Data](#source-data) |
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- [Annotations](#annotations) |
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- [Personal and Sensitive Information](#personal-and-sensitive-information) |
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- [Considerations for Using the Data](#considerations-for-using-the-data) |
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- [Social Impact of Dataset](#social-impact-of-dataset) |
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- [Discussion of Biases](#discussion-of-biases) |
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- [Other Known Limitations](#other-known-limitations) |
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- [Additional Information](#additional-information) |
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- [Dataset Curators](#dataset-curators) |
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- [Licensing Information](#licensing-information) |
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- [Citation Information](#citation-information) |
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- [Contributions](#contributions) |
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## Dataset Description |
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- **Homepage:** http://shuoyang1213.me/WIDERFACE/index.html |
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- **Repository:** |
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- **Paper:** [WIDER FACE: A Face Detection Benchmark](https://arxiv.org/abs/1511.06523) |
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- **Leaderboard:** http://shuoyang1213.me/WIDERFACE/WiderFace_Results.html |
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- **Point of Contact:** shuoyang.1213@gmail.com |
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### Dataset Summary |
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WIDER FACE dataset is a face detection benchmark dataset, of which images are |
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selected from the publicly available WIDER dataset. We choose 32,203 images and |
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label 393,703 faces with a high degree of variability in scale, pose and |
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occlusion as depicted in the sample images. WIDER FACE dataset is organized |
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based on 61 event classes. For each event class, we randomly select 40%/10%/50% |
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data as training, validation and testing sets. We adopt the same evaluation |
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metric employed in the PASCAL VOC dataset. Similar to MALF and Caltech datasets, |
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we do not release bounding box ground truth for the test images. Users are |
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required to submit final prediction files, which we shall proceed to evaluate. |
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### Supported Tasks and Leaderboards |
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- `face-detection`: The dataset can be used to train a model for Face Detection. More information on evaluating the model's performance can be found [here](http://shuoyang1213.me/WIDERFACE/WiderFace_Results.html). |
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### Languages |
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English |
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## Dataset Structure |
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### Data Instances |
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A data point comprises an image and its face annotations. |
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``` |
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{ |
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'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=1024x755 at 0x19FA12186D8>, 'faces': { |
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'bbox': [ |
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[178.0, 238.0, 55.0, 73.0], |
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[248.0, 235.0, 59.0, 73.0], |
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[363.0, 157.0, 59.0, 73.0], |
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[468.0, 153.0, 53.0, 72.0], |
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[629.0, 110.0, 56.0, 81.0], |
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[745.0, 138.0, 55.0, 77.0] |
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], |
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'blur': [2, 2, 2, 2, 2, 2], |
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'expression': [0, 0, 0, 0, 0, 0], |
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'illumination': [0, 0, 0, 0, 0, 0], |
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'occlusion': [1, 2, 1, 2, 1, 2], |
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'pose': [0, 0, 0, 0, 0, 0], |
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'invalid': [False, False, False, False, False, False] |
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} |
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} |
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``` |
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### Data Fields |
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- `image`: A `PIL.Image.Image` object containing the image. Note that when accessing the image column: `dataset[0]["image"]` the image file is automatically decoded. Decoding of a large number of image files might take a significant amount of time. Thus it is important to first query the sample index before the `"image"` column, *i.e.* `dataset[0]["image"]` should **always** be preferred over `dataset["image"][0]` |
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- `faces`: a dictionary of face attributes for the faces present on the image |
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- `bbox`: the bounding box of each face (in the [coco](https://albumentations.ai/docs/getting_started/bounding_boxes_augmentation/#coco) format) |
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- `blur`: the blur level of each face, with possible values including `clear` (0), `normal` (1) and `heavy` |
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- `expression`: the facial expression of each face, with possible values including `typical` (0) and `exaggerate` (1) |
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- `illumination`: the lightning condition of each face, with possible values including `normal` (0) and `exaggerate` (1) |
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- `occlusion`: the level of occlusion of each face, with possible values including `no` (0), `partial` (1) and `heavy` (2) |
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- `pose`: the pose of each face, with possible values including `typical` (0) and `atypical` (1) |
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- `invalid`: whether the image is valid or invalid. |
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### Data Splits |
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The data is split into training, validation and testing set. WIDER FACE dataset is organized |
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based on 61 event classes. For each event class, 40%/10%/50% |
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data is randomly selected as training, validation and testing sets. The training set contains 12880 images, the validation set 3226 images and test set 16097 images. |
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## Dataset Creation |
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### Curation Rationale |
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The curators state that the current face detection datasets typically contain a few thousand faces, with limited variations in pose, scale, facial expression, occlusion, and background clutters, |
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making it difficult to assess for real world performance. They argue that the limitations of datasets have partially contributed to the failure of some algorithms in coping |
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with heavy occlusion, small scale, and atypical pose. |
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### Source Data |
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#### Initial Data Collection and Normalization |
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WIDER FACE dataset is a subset of the WIDER dataset. |
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The images in WIDER were collected in the following three steps: 1) Event categories |
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were defined and chosen following the Large Scale Ontology for Multimedia (LSCOM) [22], which provides around 1000 concepts relevant to video event analysis. 2) Images |
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are retrieved using search engines like Google and Bing. For |
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each category, 1000-3000 images were collected. 3) The |
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data were cleaned by manually examining all the images |
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and filtering out images without human face. Then, similar |
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images in each event category were removed to ensure large |
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diversity in face appearance. A total of 32203 images are |
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eventually included in the WIDER FACE dataset. |
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#### Who are the source language producers? |
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The images are selected from publicly available WIDER dataset. |
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### Annotations |
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#### Annotation process |
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The curators label the bounding boxes for all |
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the recognizable faces in the WIDER FACE dataset. The |
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bounding box is required to tightly contain the forehead, |
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chin, and cheek.. If a face is occluded, they still label it with a bounding box but with an estimation on the scale of occlusion. Similar to the PASCAL VOC dataset [6], they assign an ’Ignore’ flag to the face |
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which is very difficult to be recognized due to low resolution and small scale (10 pixels or less). After annotating |
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the face bounding boxes, they further annotate the following |
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attributes: pose (typical, atypical) and occlusion level (partial, heavy). Each annotation is labeled by one annotator |
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and cross-checked by two different people. |
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#### Who are the annotators? |
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Shuo Yang, Ping Luo, Chen Change Loy and Xiaoou Tang. |
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### Personal and Sensitive Information |
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[More Information Needed] |
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## Considerations for Using the Data |
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### Social Impact of Dataset |
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[More Information Needed] |
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### Discussion of Biases |
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[More Information Needed] |
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### Other Known Limitations |
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[More Information Needed] |
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## Additional Information |
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### Dataset Curators |
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Shuo Yang, Ping Luo, Chen Change Loy and Xiaoou Tang |
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### Licensing Information |
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[Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)](https://creativecommons.org/licenses/by-nc-nd/4.0/). |
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### Citation Information |
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``` |
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@inproceedings{yang2016wider, |
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Author = {Yang, Shuo and Luo, Ping and Loy, Chen Change and Tang, Xiaoou}, |
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Booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)}, |
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Title = {WIDER FACE: A Face Detection Benchmark}, |
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Year = {2016}} |
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``` |
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### Contributions |
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Thanks to [@mariosasko](https://github.com/mariosasko) for adding this dataset. |
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