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README.md

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-license: apache-2.0
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+---
+license: mit
+---
+
+# ResNetWildFireModel for Wildfire Classification
+
+## Model Details
+
+- **Model Architecture:** ResNet-18 (Modified)  
+- **Framework:** PyTorch  
+- **Input Shape:** 3-channel RGB images  
+- **Number of Parameters:** ~11.7M (Based on ResNet-18)  
+- **Output:** Single regression value (wildfire severity)
+
+## Model Description
+
+This model is a **fine-tuned ResNet-18** for wildfire severity regression. The pretrained **ResNet-18** backbone is used with its feature extractor **frozen**, while only the **final fully connected layer** is trained. The last fully connected layer has been replaced with a **single output neuron** for regression tasks, predicting the severity of the wildfire.
+
+## Training Details
+
+- **Optimizer:** Adam  
+- **Batch Size:** 32  
+- **Loss Function:** Mean Squared Error (MSE)  
+- **Number of Epochs:** 10  
+- **Dataset:** [Wildfire Severity Dataset](https://www.kaggle.com/datasets/brsdincer/wildfire-detection-image-data)  
+
+### Losses Per Epoch
+
+| Epoch | Training Loss | Validation Loss |
+|-------|---------------|-----------------|
+| 1     | 0.2182        | 0.0593          |
+| 2     | 0.0483        | 0.0508          |
+| 3     | 0.0347        | 0.0482          |
+| 4     | 0.0275        | 0.0461          |
+| 5     | 0.0253        | 0.0474          |
+| 6     | 0.0187        | 0.0457          |
+| 7     | 0.0131        | 0.0456          |
+| 8     | 0.0111        | 0.0451          |
+| 9     | 0.0096        | 0.0463          |
+| 10    | 0.0079        | 0.0474          |
+
+## License
+
+This model is released under the **MIT License**.
+
+---