| | ---
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| | language: en
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| | license: mit
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| | library_name: tensorflow
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| | tags:
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| | - computer-vision
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| | - drowsiness-detection
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| | - driver-safety
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| | - cnn
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| | - tensorflow
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| | datasets:
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| | - custom
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| | metrics:
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| | - accuracy
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| | - binary-crossentropy
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| | pipeline_tag: image-classification
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| | ---
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| |
|
| | # Driver Drowsiness Detection Model
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| |
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| | This model is designed to detect driver drowsiness from facial images using a CNN architecture.
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| |
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| | ## Model Details
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| | - Architecture: CNN
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| | - Input: Facial images (64x64x3)
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| | - Output: Binary classification (drowsy/not drowsy)
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| |
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| | ## Usage
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| | ```python
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| | import tensorflow as tf
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| | import cv2
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| | import numpy as np
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| |
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| | # Load model
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| | model = tf.keras.models.load_model('drowsiness_model.h5')
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| |
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| | # Preprocess image
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| | img = cv2.imread('face.jpg')
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| | img = cv2.resize(img, (64, 64))
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| | img = img / 255.0
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| | img = np.expand_dims(img, axis=0)
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| |
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| | # Make prediction
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| | prediction = model.predict(img)
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| | is_drowsy = prediction[0][0] > 0.5
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| | ```
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| |
|
| | ## Training Details
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| | - Dataset: Custom driver drowsiness dataset
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| | - Training method: Binary cross-entropy loss with Adam optimizer
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| | - Validation split: 20%
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| | - Early stopping with patience=3
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| |
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| | ## Model Architecture
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| | - Input Layer: 64x64x3 images
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| | - Convolutional Layers:
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| | - Conv2D(32, 3x3) + BatchNorm + ReLU
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| | - MaxPooling2D(2x2)
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| | - Conv2D(64, 3x3) + BatchNorm + ReLU
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| | - MaxPooling2D(2x2)
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| | - Conv2D(128, 3x3) + BatchNorm + ReLU
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| | - MaxPooling2D(2x2)
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| | - Dense Layers:
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| | - Dense(128) + BatchNorm + ReLU
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| | - Dropout(0.5)
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| | - Dense(1) + Sigmoid
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| |
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| | ## Performance
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| | - Binary classification for drowsiness detection
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| | - Optimized for real-time inference
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| | - Suitable for embedded systems and edge devices
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| |
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| | ## License
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| | This model is released under the MIT License.
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| |
|
