astronolan/galaxy10-aion

Galaxy10 AION-1 Benchmark

This dataset provides the exact train/test split used to produce the Galaxy Morphology Classification results in the AION-1 paper (Table 2, Section 7.2.2) and used in the AION-Search paper.

Task

Classify galaxy images into 10 morphology classes from Galaxy Zoo DECaLS:

Label	Class Name
0	Disturbed Galaxies
1	Merging Galaxies
2	Round Smooth Galaxies
3	In-between Round Smooth Galaxies
4	Cigar Shaped Smooth Galaxies
5	Barred Spiral Galaxies
6	Unbarred Tight Spiral Galaxies
7	Unbarred Loose Spiral Galaxies
8	Edge-on Galaxies without Bulge
9	Edge-on Galaxies with Bulge

Splits

Split	Count	Description
train	7,120	90% class-stratified split
test	796	10% held-out evaluation set

These are the exact indices used in the AION-1 paper.

Columns

Column	Type	Description
image_rgb	image	256x256x3 uint8 RGB from Galaxy10_DECals.h5 (PNG)
ra	float64	Right ascension (degrees)
dec	float64	Declination (degrees)
Galaxy10_DECals_index	int64	Row index into Galaxy10_DECals.h5
label	int64	Morphology class (0-9)
label_name	string	Human-readable class name
image_bands	binary	96x96 4-band (g,r,i,z) float32 cutout from Legacy Survey DR10

Reconstructing image_bands

The image_bands column stores float32 flux values as a nested list (4 bands x 96 x 96 pixels). To reconstruct:

import numpy as np
cutout = np.array(row["image_bands"], dtype=np.float32)  # (4, 96, 96)
# cutout[0] = g-band, cutout[1] = r-band, cutout[2] = i-band, cutout[3] = z-band

Using with AION-1

To tokenize with the AION codec and compute embeddings:

import torch
from aion.codecs import CodecManager
from aion.modalities import LegacySurveyImage
from aion.model import AION

codec_manager = CodecManager(device="cuda")
model = AION.from_pretrained("polymathic-ai/aion-base").to("cuda").eval()

cutout = np.array(row["image_bands"], dtype=np.float32)  # (4, 96, 96)
image_flux = torch.tensor(cutout).unsqueeze(0).to("cuda")
image = LegacySurveyImage(flux=image_flux, bands=["DES-G", "DES-R", "DES-I", "DES-Z"])
tokens = codec_manager.encode(image)
embeddings = model.encode({"tok_image": tokens["tok_image"]}, num_encoder_tokens=600)
mean_embedding = embeddings.mean(dim=1)  # (1, 768)

AION-1 Paper Results (Table 2)

Model	Accuracy (%)
AION-B	84.0
AION-L	87.2
AION-XL	86.5
Oquab et al. (2023)	71.4
EfficientNet	80.0
Walmsley et al. (2022)	89.6
AION-Search	X

Data Sources

• Galaxy10_DECals.h5: Galaxy Zoo 10 catalog (Leung and Bovy, 2018; Walmsley et al., 2022)
• 4-band cutouts: Downloaded from ALASKY via CDS HiPS2FITS at 0.262 arcsec/pixel

The underlying image data is from the DESI Legacy Imaging Surveys and Galaxy Zoo, subject to their respective data-use policies.