README.md

---
license: cc-by-sa-4.0
size_categories:
- 1M<n<10M
dataset_info:
- config_name: default
  features:
  - name: image
    dtype: image
  - name: image_crop
    dtype: image
  - name: dr8_id
    dtype: string
  - name: galaxy_size
    dtype: int64
  splits:
  - name: test
    num_bytes: 12057249781.25
    num_examples: 86471
  - name: validation
    num_bytes: 12065699086.25
    num_examples: 86499
  - name: train
    num_bytes: 1181934533243.5
    num_examples: 8474566
  download_size: 1206114375284
  dataset_size: 1206057482111
- config_name: v1.0
  features:
  - name: image
    dtype: image
  - name: dr8_id
    dtype: string
  splits:
  - name: train
    num_bytes: 959387460144.3469
    num_examples: 8474566
  - name: test
    num_bytes: 9785671701.822557
    num_examples: 86471
  - name: validation
    num_bytes: 9798204502.80013
    num_examples: 86499
  download_size: 982501453040
  dataset_size: 978971336348.9696
configs:
- config_name: default
  data_files:
  - split: test
    path: with_crops/test-*
  - split: validation
    path: with_crops/validation-*
  - split: train
    path: with_crops/train-*
- config_name: v1.0
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
  - split: validation
    path: data/validation-*
tags:
- astronomy
- huggingscience
- science
---
# Galaxies for training astroPT

Here we have ~8.5 million galaxy cutouts from the [DESI legacy survey DR8](https://www.legacysurvey.org/dr8/description/).
The cut outs are 512x512 pixel jpg images centred on the galaxy source.

I've split away 1% of the images into a test set, and 1% into a validation set.
The remaining 98% of the images comprise the training set.

## Metadata (galaxy properties)

There is also accompanying metadata!
To combine the metadata with the galaxy images you can do (for example):
```python
from datasets import load_dataset, concatenate_datasets

# Load the `galaxies' dataset with metadata
galaxies = load_dataset("Smith42/galaxies", streaming=True)
metadata = load_dataset("Smith42/galaxies_metadata", streaming=True).remove_columns("dr8_id")
combined = concatenate_datasets([galaxies['train'], metadata['train']], axis=1)
```
Or you can use v2.0 of this dataset to get the metadata direct with no faffing 🤯:
```python
galaxies = load_dataset("Smith42/galaxies", revision="v2.0", streaming=True)
```
The metadata is also available in parquet format in the root dir of this repo. 
You can link the metadata with the galaxies via their dr8_id.

## Embeddings from the pre-trained family of AstroPTv2 models

Same story with pre-generated embeddings from [AstroPTv2](https://huggingface.co/smith42/astropt_v2.0).

You can combine the embeddings with galaxy imagery and metadata by doing:
```python
galaxies = load_dataset("Smith42/galaxies", revision="v2.0", streaming=True)
embs = load_dataset("Smith42/galaxies_embeddings", streaming=True)
combined = concatenate_datasets([galaxies['train'], embs['train']], axis=1)
```

## Useful links

Paper here: [https://arxiv.org/abs/2405.14930](https://arxiv.org/abs/2405.14930)

Models here: [https://huggingface.co/Smith42/astroPT](https://huggingface.co/Smith42/astroPT)

And here: [https://huggingface.co/Smith42/astroPT_v2.0](https://huggingface.co/Smith42/astroPT_v2.0)

Code here: [https://github.com/smith42/astroPT](https://github.com/smith42/astroPT)

Upstream catalogue is [on Zenodo](https://zenodo.org/records/8360385) and paper describing the catalogue is available as [Walmsley+2023](https://doi.org/10.1093/mnras/stad2919).

If you find this dataset useful please consider citing the sources below 🚀🚀:

```
@article{ref_dey2019,
	author = {Dey, A. and Schlegel, D. J. and Lang, D. and Blum, R. and Burleigh, K. and Fan, X. and Findlay, J. R. and Finkbeiner, D. and Herrera, D. and Juneau, S. and others},
	title = {{Overview of the DESI Legacy Imaging Surveys}},
	journal = {Astronomical Journal},
	volume = {157},
	number = {5},
	pages = {168},
	year = {2019},
	issn = {1538-3881},
	publisher = {The American Astronomical Society},
	doi = {10.3847/1538-3881/ab089d}
}
```

```
@article{ref_walmsley2023,
	author = {Walmsley, M. and G{\ifmmode\acute{e}\else\'{e}\fi}ron, T. and Kruk, S. and Scaife, A. M. M. and Lintott, C. and Masters, K. L. and Dawson, J. M. and Dickinson, H. and Fortson, L. and Garland, I. L. and others},
	title = {{Galaxy Zoo DESI: Detailed morphology measurements for 8.7M galaxies in the DESI Legacy Imaging Surveys}},
	journal = {Monthly Notices of the Royal Astronomical Society},
	volume = {526},
	number = {3},
	pages = {4768--4786},
	year = {2023},
	issn = {0035-8711},
	publisher = {Oxford Academic},
	doi = {10.1093/mnras/stad2919}
}
```

```
@article{ref_smith2024,
	author = {Smith, M. J. and Roberts, R. J. and Angeloudi, E. and Huertas-Company, M.},
	title = {{AstroPT: Scaling Large Observation Models for Astronomy}},
	journal = {ArXiv e-prints},
	year = {2024},
	eprint = {2405.14930},
	doi = {10.48550/arXiv.2405.14930}
}
```