README.md

---
# For reference on dataset card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/datasets-cards
{}
---

# Dataset Card for CelebFaces Attributes (CelebA)

<!-- Provide a quick summary of the dataset. -->

## Dataset Details

### Dataset Description

<!-- Provide a longer summary of what this dataset is. -->
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

<!-- Provide the basic links for the dataset. -->

- **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

<!-- This section provides a description of the dataset fields, and additional information about the dataset structure such as criteria used to create the splits, relationships between data points, etc. -->

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

<!-- If there is a paper or blog post introducing the dataset, the APA and Bibtex information for that should go in this section. -->

**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} 
}