Update README.md

README.md

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 - **Framework:** PyTorch  
 - **Input Shape:** 3-channel RGB images  
 - **Number of Parameters:** ~11.7M (Based on ResNet-18)  
-- **Output:** Single regression value (wildfire severity)
+- **Output:** Binary classification (wildfire presence)
 
 ## 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.
+This model is a **fine-tuned ResNet-18** for wildfire classification. 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 binary classification, predicting the presence of wildfire.
 
 ## Training Details
 
 - **Optimizer:** Adam  
 - **Batch Size:** 32  
-- **Loss Function:** Mean Squared Error (MSE)  
+- **Loss Function:** Binary Cross-Entropy (BCE)  
 - **Number of Epochs:** 10  
-- **Dataset:** [Wildfire Severity Dataset](https://www.kaggle.com/datasets/brsdincer/wildfire-detection-image-data)  
+- **Dataset:** [Wildfire Detection Image Data](https://www.kaggle.com/datasets/brsdincer/wildfire-detection-image-data)  
 
 ### Losses Per Epoch
 
@@ -44,3 +44,4 @@ This model is a **fine-tuned ResNet-18** for wildfire severity regression. The p
 This model is released under the **MIT License**.
 
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