Update README.md

README.md

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 - Remote sensing
 - ZueriCrop 2.0 dataset
 - Hierarchical classification
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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.
+`Messis` is an advanced crop classification model for the agricultural landscapes of Switzerland. It is built upon the geospatial foundation model [Prithvi](https://huggingface.co/ibm-nasa-geospatial/Prithvi-100M), which was originally pre-trained on U.S. satellite data. Messis has been trained using our ZueriCrop 2.0 dataset, a specially curated collection of Sentinel-2 imagery that covers agricultural regions in Switzerland. 
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+The Messis model leverages a three-tier hierarchical label structure, optimized for remote sensing tasks, to enhance its classification accuracy across different crop types. By adapting Prithvi to the specific challenges of Swiss agriculture—such as smaller field sizes and higher image resolutions by the Sentinel-2 satellites—Messis demonstrates the versatility of pretrained geospatial models in handling new downstream tasks.
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+Additionally, Messis reduces the need for extensive labeled data by effectively utilizing Prithvi's pretrained weights. In evaluations, Messis achieved a notable F1 score of 34.8% across 48 crop classes, doubling the performance when compared to the same model initialized with random weights. 
 
 <img src="./assets/messis.jpeg" alt="Messis" width="600">
 
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 ### Usage
 
-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.
+Experience the Messis model firsthand by trying it out in our interactive [Huggingface Spaces Demo](https://huggingface.co/spaces/crop-classification/messis-demo).
 
-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.
+For comprehensive details on how Messis was developed, including full access to the DVC pipeline producing the ZueriCrop 2.0 dataset, model code, preprocessing steps, and training scripts, visit our [GitHub Repository](https://github.com/Satellite-Based-Crop-Classification/messis). There, you’ll find everything you need to understand, reproduce, or further fine-tune the model.