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license: mit
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# 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:** Binary classification (wildfire presence)
## Model Description
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:** Binary Cross-Entropy (BCE)
- **Number of Epochs:** 10
- **Dataset:** [Wildfire Detection Image Data](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**.
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