🔥 Forest Fire Detection Model

This model detects forest fires in images using a deep learning CNN trained on the Wildfire Dataset.

Model Details

Architecture: Sequential CNN with Conv2D, MaxPooling2D, Dense, Dropout layers.
Input Size: 150x150 RGB images
Output: Binary classification (fire or nofire)
Framework: TensorFlow / Keras

Training Data

Dataset: The Wildfire Dataset
Classes: fire, nofire
Preprocessing: Images resized to 150x150, normalized to [0, 1]

Training Script

The model was trained using the following script (see attached notebook for full details):

model = Sequential([
    Input(shape=(150, 150, 3)),
    Conv2D(32, (3,3), activation='relu'),
    MaxPooling2D(pool_size=(2,2)),
    Conv2D(64, (3, 3), activation='relu'),
    MaxPooling2D(pool_size=(2, 2)),
    Conv2D(128, (3, 3), activation='relu'),
    MaxPooling2D(pool_size=(2, 2)),
    Flatten(),
    Dense(512, activation='relu'),
    Dropout(0.5),
    Dense(1, activation='sigmoid')
])
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
model.fit(...)

Intended Use

Use Case: Automated detection of forest fires in aerial or ground images.
Limitations: Not suitable for video, may not generalize to all forest types or lighting conditions.

How to Use

import requests

API_URL = "https://api-inference.huggingface.co/models/YOUR_USERNAME/YOUR_MODEL_NAME"
headers = {"Authorization": "Bearer YOUR_HF_API_TOKEN"}

with open("your_image.jpg", "rb") as f:
    data = f.read()
response = requests.post(API_URL, headers=headers, files={"file": data})
print(response.json())

Evaluation

Test Accuracy: 70%
Metrics: Not suitable for video, may not generalize to all forest types or lighting conditions.

Citation

If you use this model, please cite the dataset and this repository.

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