---
language:
- en
license: cc-by-4.0
task_categories:
- image-classification
tags:
- astronomy
- astrophysics
- galaxies
- galaxy-zoo
- decals
- vision-transformer
pretty_name: Galaxy Zoo DECaLS (8-Class Classification)
size_categories:
- 100K<n<1M
source_datasets:
- extended|galaxy_zoo
dataset_info:
  features:
  - name: image
    dtype: image
  - name: label
    dtype: int64
  splits:
  - name: train
    num_bytes: 1358384582
    num_examples: 99808
  - name: validation
    num_bytes: 339371652
    num_examples: 24952
  - name: test
    num_bytes: 425327496
    num_examples: 31191
  download_size: 2116174766
  dataset_size: 2123083730
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
---

# 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). The 8-class morphological taxonomy and classification approach used in this dataset are based on **Lin et al. (2021)**, which first applied Vision Transformers to galaxy morphology classification using this same data. This repository formats the original scientific data into a clean structure ready for Machine Learning pipelines.

- **Original Data Source:** [Galaxy Zoo Data Releases](https://data.galaxyzoo.org/)
- **Data Paper:** Walmsley et al. (2022) — [Galaxy Zoo DECaLS: Detailed Visual Morphology for 314,000 Galaxies](https://arxiv.org/abs/2102.08414)
- **Classification Approach Paper:** Lin et al. (2021) — [Galaxy Morphological Classification with Efficient Vision Transformer](https://arxiv.org/abs/2110.01024)

## 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)

| 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. |

**Visual Examples of the Classes:**

![Sample Galaxies](sample_galaxy.jpg)

## Usage

```python
from datasets import load_dataset

dataset = load_dataset("mrJordi0/galaxy-zoo-dataset")

train_data = dataset['train']
test_data = dataset['test']

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"
}

example = train_data[0]
print(f"Label ID: {example['label']} | Morphology: {id2label[example['label']]}")
example['image'].show()
```

## Citation

If you use this dataset, please cite both papers:

```bibtex
@misc{Lin2021,
    author = {Lin, Joshua Yao-Yu and Liao, Song-Mao and Huang, Hung-Jin and Kuo, Wei-Ting and Ou, Olivia Hsuan-Min},
    title = {Galaxy Morphological Classification with Efficient Vision Transformer},
    year = {2021},
    eprint = {2110.01024},
    archivePrefix = {arXiv},
    primaryClass = {astro-ph.GA},
    url = {https://arxiv.org/abs/2110.01024}
}

@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}
}
```

## 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.

- **Data Source**: [Galaxy Zoo Data Releases](https://data.galaxyzoo.org/)
- **License**: Creative Commons Attribution 4.0 International (CC BY 4.0)