EfficientNetWildFireModel for Wildfire Classification
Model Details
- Model Architecture: EfficientNet-B0 (Modified)
- Framework: PyTorch
- Input Shape: 3-channel RGB images
- Number of Parameters: ~5.3M (Based on EfficientNet-B0)
- Output: Binary classification (wildfire presence)
Model Description
This model is a fine-tuned EfficientNet-B0 for wildfire classification. The pretrained EfficientNet-B0 backbone is used with its feature extractor frozen, while only the final classification 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 | 0.1859 | 0.0699 |
| 2 | 0.0553 | 0.0580 |
| 3 | 0.0263 | 0.0576 |
| 4 | 0.0146 | 0.0553 |
| 5 | 0.0105 | 0.0555 |
| 6 | 0.0080 | 0.0554 |
| 7 | 0.0062 | 0.0554 |
| 8 | 0.0052 | 0.0559 |
| 9 | 0.0043 | 0.0550 |
| 10 | 0.0037 | 0.0563 |
License
This model is released under the MIT License.
