datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
facesyntheticsspigacaptioned
Dataset Card for "face_synthetics_spiga_captioned"
This is a copy of the Microsoft FaceSynthetics dataset with SPIGA-calculated landmark annotations, and additional BLIP-generated captions.
For a copy of the original FaceSynthetics dataset with no extra annotations, please refer to pcuenq/face_synthetics.
Here is the code for parsing the dataset and generating the BLIP captions:
from transformers import pipeline
dataset_name = "pcuenq/face_synthetics_spiga"
faces =… See the full description on the dataset page: https://huggingface.co/datasets/multimodalart/facesyntheticsspigacaptioned.CelebA-faces-with-attributesCelebA-facesWider_FaceSegLiteHolistic-Processing-Illusion-Faces
HoloFaceIllusion-Bench-EEG
A large-scale benchmark of holistic-face illusion stimuli for testing
human-vs-DNN alignment on configural face processing and for paired
EEG-decoder evaluation. Built entirely with classical CV
(dlib landmarks + MediaPipe Face Mesh + InsightFace gender/age +
OpenCV Poisson cloning + Reinhard LAB colour transfer) — no neural
networks or generative AI are used to create any pixel.
Three paradigms are included:
Paradigm
Cases
Conditions per case… See the full description on the dataset page: https://huggingface.co/datasets/Enhui-1/Holistic-Processing-Illusion-Faces.generated-passport-faces-aditya-second-halfflickr_faces_res512_50k
Dataset Card for flicker-faces
This is a FiftyOne dataset with 52001 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/flickr_faces_res512_50k")
# Launch the App
session = fo.launch_app(dataset)… See the full description on the dataset page: https://huggingface.co/datasets/harpreetsahota/flickr_faces_res512_50k.face_synthetics_spiga
Dataset Card for "face_synthetics_spiga"
This is a copy of Microsoft FaceSynthetics dataset with SPIGA landmark annotations. For a copy of the original FaceSynthetics dataset with no extra annotations, please refer to pcuenq/face_synthetics.
Please, refer to the original license, which we replicate in this repo. The SPIGA annotations were created by Hugging Face Inc. and are distributed under the MIT license.
This dataset was prepared using the code below. It iterates through the… See the full description on the dataset page: https://huggingface.co/datasets/pcuenq/face_synthetics_spiga.anime-faces
Dataset Card for anime-faces
Dataset Summary
This is a dataset consisting of 21551 anime faces scraped from www.getchu.com, which are then cropped using the anime face detection algorithm in https://github.com/nagadomi/lbpcascade_animeface. All images are resized to 64 * 64 for the sake of convenience. Please also cite the two sources when using this dataset.
Some outliers are still present in the dataset:
Bad cropping results
Some non-human faces.
Feel free to contribute… See the full description on the dataset page: https://huggingface.co/datasets/huggan/anime-faces.filtered-faces
DMD-Swap SD3.5 Filtered Dataset, Chunked Export
This artifact is composed of independently resumable archive chunks.
Extract all archives/**/*.tar.zst files into the repository root.
The latent dataset loader supports the extracted metadata/chunks/* layout.
anime-faces-256Speaking_Faces
Data were collected from 142 subjects, yielding over 13,000 instances of synchronized data (3.8 TB).
Link to the published paper: https://www.mdpi.com/1424-8220/21/10/3465/htm
Data Acquisition
FLIR T540 thermal camera (464×348 pixels, 24◦ FOV) and a Logitech C920 Pro HD web-camera (768×512 pixels, 78◦ FOV) with a built-in dual stereo microphone were used for data collection purpose. Each subject participated in two trials where each trial consisted of two sessions. In the first session… See the full description on the dataset page: https://huggingface.co/datasets/issai/Speaking_Faces.140k-Real-and-Fake-Faces
140k Real and Fake Faces Dataset
📝 Dataset Description
Dataset Summary
The 140k Real and Fake Faces Dataset is a balanced face image collection designed to benchmark models for detecting StyleGAN-generated faces. The dataset contains 70,000 real human face photographs sourced from the Flickr-Faces-HQ (FFHQ) dataset compiled by NVIDIA, and 70,000 fake faces sampled from the 1 Million Fake Faces dataset generated by StyleGAN and originally provided by Bojan.… See the full description on the dataset page: https://huggingface.co/datasets/TheKernel01/140k-Real-and-Fake-Faces.face-segmentation-image-dataset
Image Dataset of Face Segmentation for recognition tasks
Dataset comprises 87,800+ images annotated with 100+ landmarks, providing a comprehensive foundation for research in face recognition, segmentation tasks, and object recognition. It is designed to support the development of learning models, recognition algorithms, and segmentation techniques.
By utilizing this dataset, researchers and developers can advance their understanding and capabilities in facial recognition, face… See the full description on the dataset page: https://huggingface.co/datasets/UniDataPro/face-segmentation-image-dataset.ramanv-image-real-faces-diversityanime-faces
Dataset Card for anime-faces
Dataset Summary
This is a dataset consisting of 21551 anime faces scraped from www.getchu.com, which are then cropped using the anime face detection algorithm in https://github.com/nagadomi/lbpcascade_animeface. All images are resized to 64 * 64 for the sake of convenience. Please also cite the two sources when using this dataset.
Some outliers are still present in the dataset:
Bad cropping results
Some non-human faces.
Feel free to… See the full description on the dataset page: https://huggingface.co/datasets/Thunderbeast/anime-faces.facesyntheticsspigacaptioned
Dataset Card for "face_synthetics_spiga_captioned"
This is a copy of the Microsoft FaceSynthetics dataset with SPIGA-calculated landmark annotations, and additional BLIP-generated captions.
For a copy of the original FaceSynthetics dataset with no extra annotations, please refer to pcuenq/face_synthetics.
Here is the code for parsing the dataset and generating the BLIP captions:
from transformers import pipeline
dataset_name = "pcuenq/face_synthetics_spiga"
faces =… See the full description on the dataset page: https://huggingface.co/datasets/YuhoLiang/facesyntheticsspigacaptioned.passport-faces
Passport Faces
Face crop images organized into men/ and women/ folders, with a _metadata.csv in each.
CelebA-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
imdb_wiki_facesimdb_faces_age_gender_name_256SD_v2_facesfaceswapimg_dataface_segmentationAn example of a dataset that we've collected for a photo edit App.
The dataset includes 20 selfies of people (man and women)
in segmentation masks and their visualisations.3D-FaceShellceleba-faces-captioned
Dataset Card for "celeba-faces-captioned"
More Information needed
facesyntheticsspigacaptioned_1
Dataset Card for "facesyntheticsspigacaptioned_1"
More Information needed
anime-faces-256px-v2
Danbooru Anime Faces-v2 (256x256)
Version 2 of the aipracticecafe/anime-faces-256px, now with 87,505 high-quality anime face crops (256x256) sourced from Danbooru. The images are focused on specific popular characters and high-quality artists, processed using YOLO-based face detection and automated tagging.
The dataset is organized into four folders (0, 1, 2, 3) representing aesthetic quality rankings, where 0 is the lowest quality and 3 is the highest.
From Version 1 the tag… See the full description on the dataset page: https://huggingface.co/datasets/puruchinera/anime-faces-256px-v2.dalle2-facesutk_faces
UTK Faces
Original paper: Age Progression/Regression by Conditional Adversarial Autoencoder
Homepage: https://susanqq.github.io/UTKFace/
Bibtex:
@inproceedings{zhifei2017cvpr,
title={Age Progression/Regression by Conditional Adversarial Autoencoder},
author={Zhang, Zhifei, Song, Yang, and Qi, Hairong},
booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2017},
organization={IEEE}
}