VGGWildFireModel for Wildfire Classification
Model Details
- Model Architecture: VGG-16 (Modified)
- Framework: PyTorch
- Input Shape: 3-channel RGB images
- Number of Parameters: ~ (Based on VGG-16)
- Output: Binary classification (wildfire presence)
Model Description
This model is a fine-tuned VGG-16 for wildfire classification. The pretrained VGG-16 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.
Training Details
- Optimizer: Adam
- Batch Size: 32
- Loss Function: Binary Cross-Entropy
- Number of Epochs: 10
- Dataset: Wildfire Detection Image Data
Losses Per Epoch
| Epoch | Training Loss | Validation Loss |
|---|
| 1 | 0.2571 | 0.3858 |
| 2 | 0.0846 | 0.1935 |
| 3 | 0.0165 | 0.1573 |
| 4 | 0.0013 | 0.1204 |
| 5 | 0.0001 | 0.1243 |
| 6 | 0.0000 | 0.1247 |
| 7 | 0.0000 | 0.1244 |
| 8 | 0.0000 | 0.1242 |
| 9 | 0.0000 | 0.1240 |
| 10 | 0.0000 | 0.1236 |
License
This model is released under the MIT License.
