mrJordi0/galaxy-zoo-dataset

Galaxy Morphological Classification (Derived from Galaxy Zoo)

Dataset Summary

This dataset is designed for Galaxy Morphological Classification tasks, specifically tailored for training deep learning models such as Efficient Vision Transformers (ViT).

The dataset contains galaxy images and their corresponding morphological labels (8 classes). The underlying images and ground-truth classifications are derived from the Galaxy Zoo DECaLS project (Walmsley et al., 2022). This repository formats the original scientific data into a clean structure ready for Machine Learning pipelines.

• Original Data Source: Galaxy Zoo Data Releases
• Scientific Reference Paper: Galaxy Zoo DECaLS: Detailed Visual Morphology for 314,000 Galaxies from a Hybrid Human-Machine Classifier

[Image of Galaxy Morphology Classification Schema]

Dataset Structure

The dataset consists of galaxy images (cutouts) and their corresponding integer class labels.

Data Fields

• image: A PIL.Image.Image object containing the galaxy cutout.
• label: An int64 integer representing the morphological class.

Label Mapping (Class ID to Morphology)

The dataset uses the following integer mapping for the 8 morphological classes:

ID	Class Name	Description
0	Round Elliptical	Smooth galaxy, completely round shape.
1	In-between Elliptical	Smooth galaxy, slightly elongated.
2	Cigar-shaped Elliptical	Smooth galaxy, very elongated/cigar-like.
3	Edge-on Spiral	Spiral galaxy viewed from the side (disk visible).
4	Barred Spiral	Spiral galaxy with a central bar structure.
5	Unbarred Spiral	Spiral galaxy without a central bar.
6	Irregular	Galaxy with no defined shape or disturbed structure.
7	Merger	Two or more galaxies interacting/merging.

Usage

Loading the Dataset

You can load this dataset directly using the Hugging Face datasets library. The data is already split into train, validation, and test sets.

from datasets import load_dataset

# Load the dataset from the Hugging Face Hub
dataset = load_dataset("mrJordi0/galaxy-zoo-dataset")

# Access the splits
train_data = dataset['train']
test_data = dataset['test']

# Example: Display the first image and its label
example = train_data[0]
image = example['image']
label = example['label']

# Mapping dictionary for reference
id2label = {
    0: "Round Elliptical",
    1: "In-between Elliptical",
    2: "Cigar-shaped Elliptical",
    3: "Edge-on Spiral",
    4: "Barred Spiral",
    5: "Unbarred Spiral",
    6: "Irregular",
    7: "Merger"
}

print(f"Label ID: {label} | Morphology: {id2label[label]}")
image.show()


## Citation & Credits

This dataset is derived from the **Galaxy Zoo** project. If you use this dataset, please cite the original Galaxy Zoo publications:

### Primary Citation (Galaxy Zoo DECaLS)
```bibtex
@article{Walmsley_2022,
    author = {Walmsley, Mike and Lintott, Chris and Géron, Tobias and Kruk, Sandor and Krawczyk, Coleman and Willett, Kyle W. and Bamford, Steven and Dickinson, Hugh and Fortson, Lucy and Gal, Yarin and Keel, William and Masters, Karen L. and Mehta, Vihang and Simmons, Brooke D. and Smethurst, Rebecca and Baeten, Elisabeth M L and Macmillan, Christine},
    title = {Galaxy Zoo DECaLS: Detailed visual morphology measurements from volunteers and deep learning for 314,000 galaxies},
    journal = {Monthly Notices of the Royal Astronomical Society},
    volume = {509},
    number = {3},
    pages = {3966-3988},
    year = {2022},
    doi = {10.1093/mnras/stab2093},
    url = {https://doi.org/10.1093/mnras/stab2093}
}

Original Galaxy Zoo Data Release

• Data Source: Galaxy Zoo Data Releases
• Paper: Galaxy Zoo DECaLS on arXiv
• License: Creative Commons Attribution 4.0 International (CC BY 4.0)

Acknowledgments

This dataset is a processed version created for machine learning applications. All credit for the original classifications and data collection goes to the Galaxy Zoo team and the hundreds of thousands of volunteers who participated in the project.

For questions about the original Galaxy Zoo data, contact the Galaxy Zoo team via their website.