---
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](https://github.com/google-research/simclr)
- **Dataset**: [Galaxy Zoo - SDSS](https://zoo4.galaxyzoo.org)
- **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)](https://arxiv.org/abs/2406.10429)

## 💻 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:

```python
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}, 
}
```