--- license: other license_name: celeba-dataset-release-agreement license_link: LICENSE dataset_info: config_name: img_align+identity+attr features: - name: image dtype: image - name: celeb_id dtype: int64 - name: 5_o_Clock_Shadow dtype: bool - name: Arched_Eyebrows dtype: bool - name: Attractive dtype: bool - name: Bags_Under_Eyes dtype: bool - name: Bald dtype: bool - name: Bangs dtype: bool - name: Big_Lips dtype: bool - name: Big_Nose dtype: bool - name: Black_Hair dtype: bool - name: Blond_Hair dtype: bool - name: Blurry dtype: bool - name: Brown_Hair dtype: bool - name: Bushy_Eyebrows dtype: bool - name: Chubby dtype: bool - name: Double_Chin dtype: bool - name: Eyeglasses dtype: bool - name: Goatee dtype: bool - name: Gray_Hair dtype: bool - name: Heavy_Makeup dtype: bool - name: High_Cheekbones dtype: bool - name: Male dtype: bool - name: Mouth_Slightly_Open dtype: bool - name: Mustache dtype: bool - name: Narrow_Eyes dtype: bool - name: No_Beard dtype: bool - name: Oval_Face dtype: bool - name: Pale_Skin dtype: bool - name: Pointy_Nose dtype: bool - name: Receding_Hairline dtype: bool - name: Rosy_Cheeks dtype: bool - name: Sideburns dtype: bool - name: Smiling dtype: bool - name: Straight_Hair dtype: bool - name: Wavy_Hair dtype: bool - name: Wearing_Earrings dtype: bool - name: Wearing_Hat dtype: bool - name: Wearing_Lipstick dtype: bool - name: Wearing_Necklace dtype: bool - name: Wearing_Necktie dtype: bool - name: Young dtype: bool splits: - name: train num_bytes: 9333552813.19 num_examples: 162770 - name: valid num_bytes: 1138445362.203 num_examples: 19867 - name: test num_bytes: 1204311503.112 num_examples: 19962 download_size: 11734694689 dataset_size: 11676309678.505001 configs: - config_name: img_align+identity+attr data_files: - split: train path: img_align+identity+attr/train-* - split: valid path: img_align+identity+attr/valid-* - split: test path: img_align+identity+attr/test-* default: true --- # Dataset Card for Dataset Name CelebFaces Attributes Dataset (CelebA) is a large-scale face attributes dataset with more than 200K celebrity images, each with 40 attribute annotations. The images in this dataset cover large pose variations and background clutter. CelebA has large diversities, large quantities, and rich annotations, including: * 10,177 number of identities, * 202,599 number of face images, and * 5 landmark locations, 40 binary attributes annotations per image. The dataset can be employed as the training and test sets for the following computer vision tasks: face attribute recognition, face recognition, face detection, landmark (or facial part) localization, and face editing & synthesis. This dataset is used in Federated Learning research because of the possibility of dividing it according to the identities of the celebrities. This repository enables us to use it in this context due to the existence of celebrity id (`celeb_id`) beside the images and attributes. ## Dataset Details This dataset was created using the following data (all of which came from the original source of the dataset): * aligned and cropped images (in PNG format), * celebrities annotations, * list attributes. The dataset was divided according to the split specified by the authors (note the celebrities do not overlap between the splits). ### Dataset Sources - **Website:** https://liuziwei7.github.io/projects/FaceAttributes.html and https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html - **Paper:** [Deep Learning Face Attributes in the Wild](https://arxiv.org/abs/1411.7766) ## Uses In order to prepare the dataset for the FL settings, we recommend using [Flower Dataset](https://flower.ai/docs/datasets/) (flwr-datasets) for the dataset download and partitioning and [Flower](https://flower.ai/docs/framework/) (flwr) for conducting FL experiments. To partition the dataset, do the following. 1. Install the package. ```bash pip install flwr-datasets[vision] ``` 2. Use the HF Dataset under the hood in Flower Datasets. ```python from flwr_datasets import FederatedDataset from flwr_datasets.partitioner import NaturalIdPartitioner fds = FederatedDataset( dataset="flwrlabs/celeba", partitioners={"train": NaturalIdPartitioner(partition_by="celeb_id")} ) partition = fds.load_partition(partition_id=0) ``` E.g., if you are following the LEAF paper, the target is the `Smiling` column. ## Dataset Structure ### Data Instances The first instance of the train split is presented below: ``` {'image': , 'celeb_id': 1, '5_o_Clock_Shadow': True, 'Arched_Eyebrows': False, 'Attractive': False, 'Bags_Under_Eyes': True, 'Bald': False, 'Bangs': False, 'Big_Lips': False, 'Big_Nose': False, 'Black_Hair': False, 'Blond_Hair': True, 'Blurry': False, 'Brown_Hair': True, 'Bushy_Eyebrows': False, 'Chubby': False, 'Double_Chin': False, 'Eyeglasses': False, 'Goatee': False, 'Gray_Hair': False, 'Heavy_Makeup': False, 'High_Cheekbones': True, 'Male': True, 'Mouth_Slightly_Open': True, 'Mustache': False, 'Narrow_Eyes': True, 'No_Beard': True, 'Oval_Face': False, 'Pale_Skin': False, 'Pointy_Nose': True, 'Receding_Hairline': False, 'Rosy_Cheeks': False, 'Sideburns': False, 'Smiling': True, 'Straight_Hair': False, 'Wavy_Hair': False, 'Wearing_Earrings': False, 'Wearing_Hat': False, 'Wearing_Lipstick': False, 'Wearing_Necklace': False, 'Wearing_Necktie': False, 'Young': False} ``` ### Data Splits ```DatasetDict({ train: Dataset({ features: ['image', 'celeb_id', '5_o_Clock_Shadow', 'Arched_Eyebrows', 'Attractive', 'Bags_Under_Eyes', 'Bald', 'Bangs', 'Big_Lips', 'Big_Nose', 'Black_Hair', 'Blond_Hair', 'Blurry', 'Brown_Hair', 'Bushy_Eyebrows', 'Chubby', 'Double_Chin', 'Eyeglasses', 'Goatee', 'Gray_Hair', 'Heavy_Makeup', 'High_Cheekbones', 'Male', 'Mouth_Slightly_Open', 'Mustache', 'Narrow_Eyes', 'No_Beard', 'Oval_Face', 'Pale_Skin', 'Pointy_Nose', 'Receding_Hairline', 'Rosy_Cheeks', 'Sideburns', 'Smiling', 'Straight_Hair', 'Wavy_Hair', 'Wearing_Earrings', 'Wearing_Hat', 'Wearing_Lipstick', 'Wearing_Necklace', 'Wearing_Necktie', 'Young'], num_rows: 162770 }) valid: Dataset({ features: ['image', 'celeb_id', '5_o_Clock_Shadow', 'Arched_Eyebrows', 'Attractive', 'Bags_Under_Eyes', 'Bald', 'Bangs', 'Big_Lips', 'Big_Nose', 'Black_Hair', 'Blond_Hair', 'Blurry', 'Brown_Hair', 'Bushy_Eyebrows', 'Chubby', 'Double_Chin', 'Eyeglasses', 'Goatee', 'Gray_Hair', 'Heavy_Makeup', 'High_Cheekbones', 'Male', 'Mouth_Slightly_Open', 'Mustache', 'Narrow_Eyes', 'No_Beard', 'Oval_Face', 'Pale_Skin', 'Pointy_Nose', 'Receding_Hairline', 'Rosy_Cheeks', 'Sideburns', 'Smiling', 'Straight_Hair', 'Wavy_Hair', 'Wearing_Earrings', 'Wearing_Hat', 'Wearing_Lipstick', 'Wearing_Necklace', 'Wearing_Necktie', 'Young'], num_rows: 19867 }) test: Dataset({ features: ['image', 'celeb_id', '5_o_Clock_Shadow', 'Arched_Eyebrows', 'Attractive', 'Bags_Under_Eyes', 'Bald', 'Bangs', 'Big_Lips', 'Big_Nose', 'Black_Hair', 'Blond_Hair', 'Blurry', 'Brown_Hair', 'Bushy_Eyebrows', 'Chubby', 'Double_Chin', 'Eyeglasses', 'Goatee', 'Gray_Hair', 'Heavy_Makeup', 'High_Cheekbones', 'Male', 'Mouth_Slightly_Open', 'Mustache', 'Narrow_Eyes', 'No_Beard', 'Oval_Face', 'Pale_Skin', 'Pointy_Nose', 'Receding_Hairline', 'Rosy_Cheeks', 'Sideburns', 'Smiling', 'Straight_Hair', 'Wavy_Hair', 'Wearing_Earrings', 'Wearing_Hat', 'Wearing_Lipstick', 'Wearing_Necklace', 'Wearing_Necktie', 'Young'], num_rows: 19962 }) }) ``` ## Citation When working with the CelebA dataset, please cite the original paper. If you're using this dataset with Flower Datasets and Flower, you can cite Flower. **BibTeX:** ``` @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} } ``` ``` @article{DBLP:journals/corr/abs-2007-14390, author = {Daniel J. Beutel and Taner Topal and Akhil Mathur and Xinchi Qiu and Titouan Parcollet and Nicholas D. Lane}, title = {Flower: {A} Friendly Federated Learning Research Framework}, journal = {CoRR}, volume = {abs/2007.14390}, year = {2020}, url = {https://arxiv.org/abs/2007.14390}, eprinttype = {arXiv}, eprint = {2007.14390}, timestamp = {Mon, 03 Aug 2020 14:32:13 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2007-14390.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ``` ## Dataset Card Contact For questions about the dataset, please contact Ziwei Liu and Ping Luo. In case of any doubts about the dataset preparation, please contact [Flower Labs](https://flower.ai/).