datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
celebA_spoof
Dataset Card for "celebA_spoof"
More Information needed
celeba
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… See the full description on the dataset page: https://huggingface.co/datasets/flwrlabs/celeba.CelebA-faces-with-attributesCelebA-facessemasia-celeba
Latents for celeba (timm)
This repository hosts precomputed latent representations (embeddings) extracted from timm image-classification backbones on celeba, released as part of SEMASIA — a large-scale resource for studying semantic communication, cross-model latent space alignment, and explainability.
Each config corresponds to a single model;
only that model's Parquet files are read on load_dataset.
Usage
Load with datasets and convert… See the full description on the dataset page: https://huggingface.co/datasets/spaicom-lab/semasia-celeba.celebamask-hq
Dataset Card for celebamask-hq
This is a FiftyOne dataset with 30000 samples.
Installation
If you haven't already, install FiftyOne:
pip install -U fiftyone
Usage
import fiftyone as fo
from fiftyone.utils.huggingface import load_from_hub
# Load the dataset
# Note: other available arguments include 'max_samples', etc
dataset = load_from_hub("harpreetsahota/celebamask-hq")
# Launch the App
session = fo.launch_app(dataset)
Dataset Details… See the full description on the dataset page: https://huggingface.co/datasets/harpreetsahota/celebamask-hq.celeba-hq-256x256
CelebA-HQ-256x256
CelebA-HQ at 256x256 resolution.
Citation
@article{DBLP:journals/corr/abs-1710-10196,
title={Progressive Growing of GANs for Improved Quality, Stability, and Variation},
author={Tero Karras and Timo Aila and Samuli Laine and Jaakko Lehtinen},
year=2017,
journal={CoRR},
volume={abs/1710.10196}
}
CelebA-attrs
CelebA-128x128
CelebA with attrs at 128x128 resolution.
Dataset Information
The attributes are binary attributes. The dataset is already split into train/test/validation sets.
Citation
@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… See the full description on the dataset page: https://huggingface.co/datasets/tpremoli/CelebA-attrs.celeba-hq
Dataset Card for "celeba-hq"
More Information needed
DigiCam-CelebA-26KData is measured at 30 cm, as shown below.
After downloading and installing LenslessPiCam, the simulated PSF can be obtained and compared with the measured one with the following command:
python scripts/sim/digicam_psf.py \
huggingface_repo=bezzam/DigiCam-CelebA-26K \
sim.waveprop=False \
sim.deadspace=True \
digicam.gamma=2.2 \
digicam.ap_center="[58,76]" \
digicam.ap_shape="[19,25]" \
digicam.rotate=0 \
digicam.horizontal_shift=-60 \
digicam.vertical_shift=-80
For a… See the full description on the dataset page: https://huggingface.co/datasets/bezzam/DigiCam-CelebA-26K.celeba-hqceleba
CelebA dataset
A copy of celeba dataset.
https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html
How to use
Download data
huggingface-cli download --local-dir /path/to/datasets/celeba --repo-type dataset Yuehao/celeba
unzip /path/to/datasets/celeba/img_align_celeba.zip -d /path/to/datasets/celeba
Load data via torchvision.datasets.CelebA
torchvision.datasets.CelebA(root='/path/to/datasets')
celeba-hq-256CelebA_bbox_and_facepointsCelebFaces 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.celebaPorting of the famous celeba dataset to 🤗 Datasets.
Dataset Component Descriptions
Attributes (attr)
Description: The attributes feature consists of binary labels that represent the presence or absence of 40 different facial attributes. Each attribute is encoded as either 0 (absence) or 1 (presence). These attributes cover a wide range of facial characteristics and styles, such as "Smiling", "Wearing Hat", "Eyeglasses", etc.
Data Type: Sequence
Length: 40
Dtype: int8… See the full description on the dataset page: https://huggingface.co/datasets/eurecom-ds/celeba.celebA-HQ
Citation
@article{DBLP:journals/corr/abs-1710-10196,
author = {Tero Karras and
Timo Aila and
Samuli Laine and
Jaakko Lehtinen},
title = {Progressive Growing of GANs for Improved Quality, Stability, and Variation},
journal = {CoRR},
volume = {abs/1710.10196},
year = {2017},
url = {http://arxiv.org/abs/1710.10196},
eprinttype = {arXiv},
eprint = {1710.10196},
timestamp = {Mon, 13 Aug 2018… See the full description on the dataset page: https://huggingface.co/datasets/Chris1/celebA-HQ.celeba-captioned
CelebA Captioned
A captioned version of the CelebA
face dataset. Each image is paired with one or more natural-language captions generated by ChatGPT-5.5,
making it suitable for image-to-text (captioning) and text-to-image generation tasks.
Dataset Structure
Data Fields
Field
Type
Description
image
Image ({bytes, path} struct)
The face image. Renders in the dataset viewer.
celeb_id
int64
Identity label for the celebrity.
captions… See the full description on the dataset page: https://huggingface.co/datasets/lazarohurtado/celeba-captioned.deepfake-celeba
MetaFLOS Deepfake Dataset (CelebA Face Deepfake Generated Images)
Flux2-Klein generated deepfake images from CelebA face descriptions, for deepfake detection / comparison research.
Content
19,867 generated images (full CelebA validation split)
Resolution: 256×256
Each corresponds to a CelebA real face (see real_orig field in manifest)
Generation style: snapshot/crop candid-photo look (not studio portrait) — off-center framing, subject possibly touching or cut by… See the full description on the dataset page: https://huggingface.co/datasets/tjw/deepfake-celeba.celeb-a-hqCelebAMask-HQCelebA-faces-cropped-128
Dataset Card for "CelebA-faces-cropped-128"
Just a 128px version of the CelebA-faces dataset, which I've cropped to the face regions using dlib. Processing notebook: https://colab.research.google.com/drive/1-P5mKb5VEQrzCmpx5QWomlq0-WNXaSxn?usp=sharing
More Information needed
celebAceleba-faces-captioned
Dataset Card for "celeba-faces-captioned"
More Information needed
vqgan16k_reconstruction_celebAcelebA_LLaVAceleba-spoof-for-face-antispoofing-testcelebahq-captionceleba-spoof-dataset
Biometric Attack Dataset
The similar dataset that includes all ethnicities - Anti Spoofing Real Dataset
We suggest you the dataset similar to CelebA Dataset but with photos of real people, additionally the dataset for face anti spoofing and face recognition includes not only images, but videos of the individuals!
The videos were gathered by capturing faces of genuine individuals presenting spoofs, using facial presentations. Our dataset proposes a novel approach that… See the full description on the dataset page: https://huggingface.co/datasets/UniqueData/celeba-spoof-dataset.CelebA-attrsceleba-hq