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tags: |
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- pytorch_model_hub_mixin |
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- model_hub_mixin |
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![Messis](./assets/messis.jpeg) |
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`Messis`is a crop classification model for Switzerland, trained on the ZueriCrop 2.0 dataset. It is fine-tuned from the Prithvi geospatial foundation model, optimized for high-resolution Sentinel-2 imagery specific to Swiss agricultural landscapes. Messis leverages a hierarchical label structure and pretrained weights. |
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### Key Features |
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1. **Adapted for High-Resolution Crop Classification:** Messis is fine-tuned from the Prithvi geospatial foundation model, originally trained on U.S. data, and optimized for high-resolution Sentinel-2 imagery specific to Swiss agricultural landscapes. |
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2. **Leveraged Hierarchical Label Structure:** Utilizes a remote-sensing-focused hierarchical label structure, enabling more accurate classification across multiple levels of crop granularity. |
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3. **Pretrained Weight Utilization:** Demonstrated significant performance improvement by leveraging Prithvi's pretrained weights, achieving a doubled F1 score compared to training from scratch. |
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4. **Dataset:** Trained on the ZueriCrop 2.0 dataset, which features higher image dimension (224x224 pixels) compared to the original ZueriCrop dataset. |
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### Usage |
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Experience the Messis model firsthand by trying it out in our interactive [Hugging Face Spaces Demo](https://huggingface.co/spaces/crop-classification/messis-demo). This demo allows you to test the model's capabilities directly on your own data or sample images. |
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For comprehensive details on how Messis was developed, including full access to the DVC pipeline producing the dataset, model code, preprocessing steps, and training scripts, visit our [GitHub Repository](https://github.com/Satellite-Based-Crop-Classification/messis). Here, you’ll find everything you need to understand, reproduce, or further fine-tune the model. |
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