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README.md

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+---
+language: en
+tags:
+- computer-vision
+- image-classification
+- mobile-phone-detection
+- tensorflow
+- mobilenetv2
+datasets:
+- mobilephoneusagedatasetiitr
+widget:
+- src: https://example.com/sample1.jpg
+  candidate_labels: using_phone,no_phone
+- src: https://example.com/sample2.jpg
+  candidate_labels: using_phone,no_phone
+---
+
+# Mobile Phone Usage Detection Model
+
+## Model Description
+
+This is a deep learning model trained to detect mobile phone usage in images. The model uses Transfer Learning with MobileNetV2 as the base architecture and custom classification layers.
+
+- **Model type:** Image Classification
+- **Task:** Binary Classification (Using Phone vs No Phone)
+- **Architecture:** MobileNetV2 with custom classifier
+- **Input shape:** (224, 224)
+- **Output:** Binary probability
+
+## Intended Uses & Limitations
+
+### Intended Use Cases
+- Surveillance systems
+- Driver safety monitoring
+- Classroom monitoring
+- Workplace productivity analysis
+
+### Limitations
+- Performance may vary with different lighting conditions
+- May have reduced accuracy with low-resolution images
+- Trained on specific dataset - may need fine-tuning for other domains
+
+## Training Data
+
+The model was trained on the [Mobile Phone Usage Dataset from IITR](https://www.kaggle.com/datasets/lakshyataragi/mobilephoneusagedatasetiitr)
+
+- **Training samples:** 711
+- **Validation samples:** 177
+- **Classes:** ['negative', 'positive']
+
+## Training Procedure
+
+### Preprocessing
+- Image resizing to (224, 224)
+- Normalization (pixel values scaled to [0,1])
+- Data augmentation (rotation, shifting, flipping, zooming)
+
+### Training Hyperparameters
+- **Batch Size:** 32
+- **Learning Rate:** 0.001 (initial), 0.0001 (fine-tuning)
+- **Optimizer:** Adam
+- **Loss Function:** Binary Crossentropy
+- **Epochs:** 50 (initial) + 20 (fine-tuning)
+
+## Evaluation Results
+
+| Metric | Value |
+|--------|-------|
+| Accuracy | 0.8418 |
+| Precision | 0.8509 |
+| Recall | 0.8981 |
+| Loss | 0.3919 |
+
+## How to Use
+
+```python
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+
+# Load model
+model = tf.keras.models.load_model('fine_tuned_phone_detection_model.h5')
+
+def predict_phone_usage(image_path):
+    # Preprocess image
+    img = Image.open(image_path).convert("RGB")
+    img = img.resize((224, 224))
+    img_array = np.array(img) / 255.0
+    img_array = np.expand_dims(img_array, axis=0)
+    
+    # Predict
+    prediction = model.predict(img_array)[0][0]
+    class_names = ['no_phone', 'using_phone']
+    result = class_names[1] if prediction > 0.5 else class_names[0]
+    confidence = prediction if prediction > 0.5 else 1 - prediction
+    
+    return result, confidence
+```
+
+## Model Architecture
+
+The model uses MobileNetV2 as base architecture with custom classification layers:
+
+- Base Model: MobileNetV2 (pre-trained on ImageNet)
+- Global Average Pooling
+- Dropout (0.3)
+- Dense Layer (128 units, ReLU activation)
+- Batch Normalization
+- Dropout (0.5)
+- Output Layer (1 unit, Sigmoid activation)
+
+## License
+
+MIT License