LeNet for Wildfire Classification
Model Details
- Model Architecture: LeNet (Modified)
- Framework: PyTorch
- Input Shape: 3-channel RGB images
- Number of Parameters: ~ (Calculated based on input size)
- Output: Binary classification (wildfire presence)
Model Description
This model is a modified version of the classic LeNet architecture, adapted for wildfire classification. It consists of two convolutional layers followed by three fully connected layers. The model was trained using ReLU activations, max pooling, and a final linear layer for binary classification.
Training Details
- Optimizer: Adam
- Loss Function: Binary Cross-Entropy
- Batch Size: 32
- Number of Epochs: 10
- Dataset: Wildfire Detection Image Data
Losses Per Epoch
| Epoch | Training Loss | Validation Loss |
|---|---|---|
| 1 | 0.8609 | 0.3632 |
| 2 | 0.3368 | 0.3023 |
| 3 | 0.2723 | 0.2852 |
| 4 | 0.1966 | 0.1914 |
| 5 | 0.2889 | 0.2610 |
| 6 | 0.1914 | 0.2747 |
| 7 | 0.2148 | 0.2520 |
| 8 | 0.1643 | 0.1751 |
| 9 | 0.1938 | 0.1929 |
| 10 | 0.1130 | 0.2095 |
License
This model is released under the MIT License.