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license: apache-2.0 |
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language: |
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- en |
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base_model: |
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- nasa-ibm-ai4science/Surya-1.0 |
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--- |
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# 🌌 Surya – Active Region Segmentation |
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## 📖 Model Overview |
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This repository hosts **fine-tuned weights of Surya** – a heliophysics foundation model – for the task of **solar Active Region (AR) segmentation**. |
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Solar Active Regions are magnetically complex structures associated with **flares** and **coronal mass ejections (CMEs)**. Within ARs, the **Polarity Inversion Line (PIL)** serves as a critical precursor of eruptions. Accurate segmentation of ARs containing PILs is essential for **space weather forecasting** and understanding solar magnetic complexity. |
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## 📊 Results |
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We benchmarked Surya against a standard UNet baseline on the ARPIL dataset. |
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| Model | Params | IoU | Dice Coeff | |
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|--------|--------|-------|------------| |
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| UNet | 9.2 M | 0.688 | 0.801 | |
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| **Surya (LoRA)** | **4.1 M** | **0.768** | **0.853** | |
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Surya achieves **higher segmentation quality with fewer parameters**, highlighting the benefits of foundation model pretraining and parameter-efficient adaptation. |
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## 🖼 Example |
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<p align="center"> |
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<img src="ar_seg.png" width="100%"> |
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</p> |
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- **Top Row**: Input SDO/HMI data (Date: 2014-02-01, Time: 08:12) |
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- **Middle Row**: Surya segmentation output |
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- **Bottom Row**: Ground Truth |
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## ⚡ Usage |
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Follow the instructions at [Surya/downstream_examples/ar_segmentation](https://github.com/NASA-IMPACT/Surya/tree/main/downstream_examples/ar_segmentation) |
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## 🤝 Acknowledgements |
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- **NASA IMPACT** and **IBM** for developing the Surya foundation model |
