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--- |
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language: en |
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license: mit |
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library_name: transformers |
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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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model_name: drowsiness_model.h5 |
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datasets: |
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- ckcl/drowsiness_dataset |
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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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widget: |
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- text: Example input |
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pipeline_tag: image-classification |
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base_model: |
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- google/mobilenet_v2_1.0_224 |
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--- |
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# Driver Drowsiness Detection Model |
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This model is designed to detect driver drowsiness from facial images using a CNN architecture. |
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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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## 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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# Load model |
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model = tf.keras.models.load_model('drowsiness_model.h5') |
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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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# 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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## 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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## 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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## License |
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This model is released under the MIT License. |
