Dataset Viewer
image
imagewidth (px) 178
178
| 5_o_Clock_Shadow
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| Arched_Eyebrows
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| Attractive
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| Bags_Under_Eyes
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| Bald
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| Bangs
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| Big_Lips
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| Big_Nose
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| Black_Hair
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| Blond_Hair
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| Blurry
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| Brown_Hair
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| Bushy_Eyebrows
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| Chubby
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| Double_Chin
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| Eyeglasses
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| Goatee
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| Gray_Hair
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| Heavy_Makeup
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| High_Cheekbones
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| Male
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| Mouth_Slightly_Open
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| Mustache
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| Narrow_Eyes
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| No_Beard
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| Oval_Face
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| Pale_Skin
int64 0
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| Pointy_Nose
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| Receding_Hairline
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| Rosy_Cheeks
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| Sideburns
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| Smiling
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| Straight_Hair
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| Wavy_Hair
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| Wearing_Earrings
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| Wearing_Hat
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| Wearing_Lipstick
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| Wearing_Necklace
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| Wearing_Necktie
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| Young
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| image_hash
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CelebA Female Dataset
Dataset Description
This dataset is a filtered subset of the CelebA dataset (Celebrities Faces Attributes), containing only female faces. The original CelebA dataset is a large-scale face attributes dataset with more than 200,000 celebrity images, each with 40 attribute annotations.
Dataset Creation
This dataset was created by:
- Loading the original CelebA dataset
- Filtering to keep only images labeled as female (based on the "Male" attribute)
- Deduplicating the dataset to remove any potential duplicate images
Intended Uses & Limitations
This dataset is intended for:
- Facial analysis research focusing on female subjects
- Training or fine-tuning image models that need to work specifically with female faces
- Studying facial attributes in a gender-specific context
Limitations:
- The dataset is limited to faces labeled as female in the original CelebA dataset
- Any biases present in the original CelebA dataset may persist in this filtered version
- The gender labels come from the original dataset and may not reflect self-identification
Dataset Structure
The dataset preserves the original structure of CelebA, including:
- Image data
- All 40 original attribute annotations
- File paths and identifiers
Citation
If you use this dataset, please cite both the original CelebA dataset and this filtered version:
@inproceedings{liu2015faceattributes, title = {Deep Learning Face Attributes in the Wild}, author = {Liu, Ziwei and Luo, Ping and Wang, Xiaogang and Tang, Xiaoou}, booktitle = {Proceedings of International Conference on Computer Vision (ICCV)}, month = {December}, year = {2015} }
Ethical Considerations
This gender-filtered dataset should be used with awareness of potential ethical implications:
- Be mindful of reinforcing gender stereotypes or biases
- Consider the impacts of technology built using gender-specific datasets
- Respect privacy and consent considerations relevant to facial images
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