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README.md
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license: mit
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license: mit
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language:
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- en
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---
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# LeNet for Wildfire Classification
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## Model Details
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- **Model Architecture:** LeNet (Modified)
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- **Framework:** PyTorch
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- **Input Shape:** 3-channel RGB images
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- **Number of Parameters:** ~ (Calculated based on input size)
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- **Output:** Binary classification (wildfire presence)
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## Model Description
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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.
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## Training Details
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- **Optimizer:** (Not specified, assumed Adam or SGD)
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- **Loss Function:** Binary Cross-Entropy (assumed)
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- **Batch Size:** (Not specified)
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- **Number of Epochs:** 10
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- **Dataset:** [Wildfire Detection Image Data](https://www.kaggle.com/datasets/brsdincer/wildfire-detection-image-data)
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### Losses Per Epoch
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| Epoch | Training Loss | Validation Loss |
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|-------|--------------|----------------|
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| 1 | 0.8609 | 0.3632 |
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| 2 | 0.3368 | 0.3023 |
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| 3 | 0.2723 | 0.2852 |
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| 4 | 0.1966 | 0.1914 |
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| 5 | 0.2889 | 0.2610 |
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| 6 | 0.1914 | 0.2747 |
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| 7 | 0.2148 | 0.2520 |
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| 8 | 0.1643 | 0.1751 |
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| 9 | 0.1938 | 0.1929 |
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| 10 | 0.1130 | 0.2095 |
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## License
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This model is released under the **MIT License**.
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