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
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.