metadata
license: apache-2.0
language:
- en
tags:
- galaxy
- SimCLR
- self-supervised
- embeddings
- generative-evaluation
datasets:
- galaxy-zoo
library_name: PyTorch
pipeline_tag: feature-extraction
🪐 GalaxyEmb: Galaxy Embedding Model using SimCLR (GZ SDSS)
GalaxyEmb is a self-supervised learning model trained on the GalaxyZoo SDSS dataset using the SimCLR framework. It maps galaxy images into a compact and meaningful latent space for use in similarity detection, retrieval, and evaluation of generative models.
🔍 Model Details
- Architecture: SimCLR
- Dataset: Galaxy Zoo - SDSS
- Input resolution: 424x424
- Output: N-dimensional normalized embedding vector
- Framework: PyTorch
💡 Intended Uses
This model is designed for:
- Similarity detection between galaxy morphologies
- Image retrieval based on morphological similarity
- Evaluation of conditional generative galaxy models, based on:
- Consistency (alignment with input condition)
- Diversity (intra-class variability)
- Realism (visual and statistical plausibility)
- The three metrics was proposed by Astolfi et al. (2024)
💻 How to Use
You can load the model by download checkpoint_0050.pth.tar and extract embeddings in get_galaxy_emb.ipynb using your own galaxy images.
You should modify the path as your downloaded one and your galaxy image folder:
checkpoint_path = "/checkpoint_0050.pth.tar"
# Your image folder path
image_folder = "../images"
# Your output folder path
output_folder = "../images_features"
📄 Citation
This embedding tool is release with our work below, used for calculating evaluation metrics for generated images. If you use or reproduce based on that, please cite our work.
@misc{ma2025aidreamunseengalaxies,
title={Can AI Dream of Unseen Galaxies? Conditional Diffusion Model for Galaxy Morphology Augmentation},
author={Chenrui Ma and Zechang Sun and Tao Jing and Zheng Cai and Yuan-Sen Ting and Song Huang and Mingyu Li},
year={2025},
eprint={2506.16233},
archivePrefix={arXiv},
primaryClass={astro-ph.GA},
url={https://arxiv.org/abs/2506.16233},
}