Messis 1.0 Release

README.md

@@ -4,6 +4,18 @@ tags:
 - model_hub_mixin
 ---
 
-This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
-- Library: [More Information Needed]
-- Docs: [More Information Needed]
+![Messis](./assets/messis.jpeg)
+
+`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.
+
+### Key Features
+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.
+2. **Leveraged Hierarchical Label Structure:** Utilizes a remote-sensing-focused hierarchical label structure, enabling more accurate classification across multiple levels of crop granularity.
+3. **Pretrained Weight Utilization:** Demonstrated significant performance improvement by leveraging Prithvi's pretrained weights, achieving a doubled F1 score compared to training from scratch.
+4. **Dataset:** Trained on the ZueriCrop 2.0 dataset, which features higher image dimension (224x224 pixels) compared to the original ZueriCrop dataset.
+
+### 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.
+
+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.

config.json

@@ -1,7 +1,7 @@
 {
  "hparams": {
  "accumulate_grad_batches": 2,
- "backbone_weights_path": "./prithvi/models/Prithvi_100M.pt",
+ "backbone_weights_path": "huggingface",
  "bands": [
  0,
  1,