Update README.md

README.md

@@ -1,74 +1,76 @@
----
-language: en
-license: mit
-library_name: tensorflow
-tags:
-- computer-vision
-- drowsiness-detection
-- driver-safety
-- cnn
-- tensorflow
-datasets:
-- custom
-metrics:
-- accuracy
-- binary-crossentropy
-pipeline_tag: image-classification
----
-
-# Driver Drowsiness Detection Model
-
-This model is designed to detect driver drowsiness from facial images using a CNN architecture.
-
-## Model Details
-- Architecture: CNN
-- Input: Facial images (64x64x3)
-- Output: Binary classification (drowsy/not drowsy)
-
-## Usage
-```python
-import tensorflow as tf
-import cv2
-import numpy as np
-
-# Load model
-model = tf.keras.models.load_model('drowsiness_model.h5')
-
-# Preprocess image
-img = cv2.imread('face.jpg')
-img = cv2.resize(img, (64, 64))
-img = img / 255.0
-img = np.expand_dims(img, axis=0)
-
-# Make prediction
-prediction = model.predict(img)
-is_drowsy = prediction[0][0] > 0.5
-```
-
-## Training Details
-- Dataset: Custom driver drowsiness dataset
-- Training method: Binary cross-entropy loss with Adam optimizer
-- Validation split: 20%
-- Early stopping with patience=3
-
-## Model Architecture
-- Input Layer: 64x64x3 images
-- Convolutional Layers:
-  - Conv2D(32, 3x3) + BatchNorm + ReLU
-  - MaxPooling2D(2x2)
-  - Conv2D(64, 3x3) + BatchNorm + ReLU
-  - MaxPooling2D(2x2)
-  - Conv2D(128, 3x3) + BatchNorm + ReLU
-  - MaxPooling2D(2x2)
-- Dense Layers:
-  - Dense(128) + BatchNorm + ReLU
-  - Dropout(0.5)
-  - Dense(1) + Sigmoid
-
-## Performance
-- Binary classification for drowsiness detection
-- Optimized for real-time inference
-- Suitable for embedded systems and edge devices
-
-## License
-This model is released under the MIT License.
+---
+language: en
+license: mit
+library_name: tensorflow
+tags:
+  - computer-vision
+  - drowsiness-detection
+  - driver-safety
+  - cnn
+  - tensorflow
+model_name: driver-drowsiness-detector
+datasets:
+  - ckcl/drowsiness_dataset
+  - custom
+metrics:
+  - accuracy
+  - binary-crossentropy
+pipeline_tag: image-classification
+---
+
+# Driver Drowsiness Detection Model
+
+This model is designed to detect driver drowsiness from facial images using a CNN architecture.
+
+## Model Details
+- Architecture: CNN
+- Input: Facial images (64x64x3)
+- Output: Binary classification (drowsy/not drowsy)
+
+## Usage
+```python
+import tensorflow as tf
+import cv2
+import numpy as np
+
+# Load model
+model = tf.keras.models.load_model('drowsiness_model.h5')
+
+# Preprocess image
+img = cv2.imread('face.jpg')
+img = cv2.resize(img, (64, 64))
+img = img / 255.0
+img = np.expand_dims(img, axis=0)
+
+# Make prediction
+prediction = model.predict(img)
+is_drowsy = prediction[0][0] > 0.5
+```
+
+## Training Details
+- Dataset: Custom driver drowsiness dataset
+- Training method: Binary cross-entropy loss with Adam optimizer
+- Validation split: 20%
+- Early stopping with patience=3
+
+## Model Architecture
+- Input Layer: 64x64x3 images
+- Convolutional Layers:
+  - Conv2D(32, 3x3) + BatchNorm + ReLU
+  - MaxPooling2D(2x2)
+  - Conv2D(64, 3x3) + BatchNorm + ReLU
+  - MaxPooling2D(2x2)
+  - Conv2D(128, 3x3) + BatchNorm + ReLU
+  - MaxPooling2D(2x2)
+- Dense Layers:
+  - Dense(128) + BatchNorm + ReLU
+  - Dropout(0.5)
+  - Dense(1) + Sigmoid
+
+## Performance
+- Binary classification for drowsiness detection
+- Optimized for real-time inference
+- Suitable for embedded systems and edge devices
+
+## License
+This model is released under the MIT License.