sebastiansarasti/VGGWildFireClassifier

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.

---