AlexNetWildFire for Wildfire Classification
Model Details
- Model Architecture: AlexNet (Modified)
- Framework: PyTorch
- Input Shape: 3-channel RGB images
- Number of Parameters: ~ (Based on AlexNet)
- Output: Binary classification (wildfire presence)
Model Description
This model is a fine-tuned AlexNet for wildfire classification. The pretrained AlexNet 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 | 0.2161 | 0.2249 |
| 2 | 0.1158 | 0.3618 |
| 3 | 0.1004 | 0.3120 |
| 4 | 0.0213 | 0.3541 |
| 5 | 0.0299 | 0.2749 |
| 6 | 0.0326 | 0.2352 |
| 7 | 0.0032 | 0.2222 |
| 8 | 0.0012 | 0.3360 |
| 9 | 0.0001 | 0.3072 |
| 10 | 0.0000 | 0.2819 |
License
This model is released under the MIT License.
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