sebastiansarasti/ResNetWildFireClassifier

ResNetWildFireModel for Wildfire Classification

Model Details

• Model Architecture: ResNet-18 (Modified)
• Framework: PyTorch
• Input Shape: 3-channel RGB images
• Number of Parameters: ~11.7M (Based on ResNet-18)
• Output: Binary classification (wildfire presence)

Model Description

This model is a fine-tuned ResNet-18 for wildfire classification. The pretrained ResNet-18 backbone is used with its feature extractor frozen, while only the final fully connected 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	0.2182	0.0593
2	0.0483	0.0508
3	0.0347	0.0482
4	0.0275	0.0461
5	0.0253	0.0474
6	0.0187	0.0457
7	0.0131	0.0456
8	0.0111	0.0451
9	0.0096	0.0463
10	0.0079	0.0474

License

This model is released under the MIT License.

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