bitmind/celeb-a-hq dataset hosted on Hugging Face and contributed by the HF Datasets community

High-quality version of the CELEBA dataset, consisting of 30000 images in 1024 x 1024 resolution.

Note: CelebAHQ dataset may contain potential bias. The fairness indicators example goes into detail about several considerations to keep in mind while using the CelebAHQ dataset.

WARNING: This dataset currently requires you to prepare images on your own.

To use this dataset:

import tensorflow_datasets as tfds

ds = tfds.load('celeb_a_hq', split='train')
for ex in ds.take(4):
 print(ex)

See the guide for more informations on tensorflow_datasets.

https://storage.googleapis.com/tfds-data/visualization/fig/celeb_a_hq-1024-2.0.0.png" alt="Visualization" width="500px">

EiMon724/bitmind-celeb-a-hq dataset hosted on Hugging Face and contributed by the HF Datasets community

The CelebA-HQ-256 dataset is a high-resolution dataset of celebrity faces.

Large-scale datasets have played indispensable roles in the recent success of face generation/editing and significantly facilitated the advances of emerging research fields. However, the academic community still lacks a video dataset with diverse facial attribute annotations, which is crucial for the research on face-related videos. In this work, we propose a large-scale, high-quality, and diverse video dataset with rich facial attribute annotations, named the High-Quality Celebrity Video Dataset (CelebV-HQ). CelebV-HQ contains 35,666 video clips with the resolution of 512x512 at least, involving 15,653 identities. All clips are labeled manually with 83 facial attributes, covering appearance, action, and emotion. We conduct a comprehensive analysis in terms of age, ethnicity, brightness stability, motion smoothness, head pose diversity, and data quality to demonstrate the diversity and temporal coherence of CelebV-HQ. Besides, its versatility and potential are validated on two represen