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--- |
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license: apache-2.0 |
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language: |
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- en |
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tags: |
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- galaxy |
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- SimCLR |
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- self-supervised |
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- embeddings |
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- generative-evaluation |
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datasets: |
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- galaxy-zoo |
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library_name: PyTorch |
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pipeline_tag: feature-extraction |
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--- |
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# 🪐 GalaxyEmb: Galaxy Embedding Model using SimCLR (GZ SDSS) |
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**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**. |
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## 🔍 Model Details |
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- **Architecture**: [SimCLR](https://github.com/google-research/simclr) |
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- **Dataset**: [Galaxy Zoo - SDSS](https://zoo4.galaxyzoo.org) |
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- **Input resolution**: 424x424 |
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- **Output**: N-dimensional normalized embedding vector |
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- **Framework**: PyTorch |
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## 💡 Intended Uses |
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This model is designed for: |
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- **Similarity detection** between galaxy morphologies |
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- **Image retrieval** based on morphological similarity |
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- **Evaluation of conditional generative galaxy models**, based on: |
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- **Consistency** (alignment with input condition) |
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- **Diversity** (intra-class variability) |
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- **Realism** (visual and statistical plausibility) |
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- The three metrics was proposed by [Astolfi et al. (2024)](https://arxiv.org/abs/2406.10429) |
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## 💻 How to Use |
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You can load the model by download `checkpoint_0050.pth.tar` and extract embeddings in `get_galaxy_emb.ipynb` using your own galaxy images. |
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You should modify the path as your downloaded one and your galaxy image folder: |
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```python |
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checkpoint_path = "/checkpoint_0050.pth.tar" |
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# Your image folder path |
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image_folder = "../images" |
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# Your output folder path |
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output_folder = "../images_features" |
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``` |
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## 📄 Citation |
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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. |
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``` |
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@misc{ma2025aidreamunseengalaxies, |
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title={Can AI Dream of Unseen Galaxies? Conditional Diffusion Model for Galaxy Morphology Augmentation}, |
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author={Chenrui Ma and Zechang Sun and Tao Jing and Zheng Cai and Yuan-Sen Ting and Song Huang and Mingyu Li}, |
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year={2025}, |
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eprint={2506.16233}, |
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archivePrefix={arXiv}, |
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primaryClass={astro-ph.GA}, |
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url={https://arxiv.org/abs/2506.16233}, |
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} |
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``` |
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