The dataset viewer is not available for this dataset.
Error code: ConfigNamesError
Exception: RuntimeError
Message: Dataset scripts are no longer supported, but found celeb-a.py
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 66, in compute_config_names_response
config_names = get_dataset_config_names(
^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 161, in get_dataset_config_names
dataset_module = dataset_module_factory(
^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 1207, in dataset_module_factory
raise e1 from None
File "/usr/local/lib/python3.12/site-packages/datasets/load.py", line 1167, in dataset_module_factory
raise RuntimeError(f"Dataset scripts are no longer supported, but found {filename}")
RuntimeError: Dataset scripts are no longer supported, but found celeb-a.pyNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Dataset Card for CelebFaces Attributes (CelebA)
Dataset Details
Dataset Description
The CelebFaces Attributes Dataset (CelebA) consists of 202,599 facial images of 10,177 individuals, annotated with 40 binary attributes per image (e.g., smiling, eyeglasses, male/female).
In our repository, we use only the images and attributes, making the dataset suitable for multi-label classification.
Dataset Sources
- Homepage: https://mmlab.ie.cuhk.edu.hk/projects/CelebA.html
- Paper: Liu, Z., Luo, P., Wang, X., & Tang, X. (2015). Deep learning face attributes in the wild. In Proceedings of the IEEE international conference on computer vision (pp. 3730-3738).
Dataset Structure
Total images: 202,599
Attributes: 40 binary labels per image
Splits:
Train: 162,770 images
Validation: 19,867 images
Test: 19,962 images
Image specs: JPEG format, RGB images
Example Usage
Below is a quick example of how to load this dataset via the Hugging Face Datasets library.
from datasets import load_dataset
# Load the dataset
dataset = load_dataset("randall-lab/celeb-a", split="train", trust_remote_code=True)
# dataset = load_dataset("randall-lab/celeb-a", split="test", trust_remote_code=True)
# dataset = load_dataset("randall-lab/celeb-a", split="validation", trust_remote_code=True)
# Access a sample from the dataset
example = dataset[0]
image = example["image"]
attributes = example["attributes"]
image.show() # Display the image
print(f"Attributes: {attributes}")
Citation
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} }
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