🚗 D3-CNN-2: Driver Distraction Detector (Simplified & Improved)

D3-CNN-2 is a simplified version of the original D3-CNN — a Convolutional Neural Network (CNN) built entirely from scratch using NumPy to detect driver distractions from images and videos. Despite its simpler architecture, D3-CNN-2 achieves significantly higher test accuracy (86%) with just 11 epochs, compared to 78% after 40 epochs in the original.

This model is CPU-trained and features a Streamlit frontend for testing with image or video input.

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📸 Sample Input vs Prediction

Input Image	Prediction
	Texting

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🧠 Model Architecture (D3-CNN-2)

The architecture is even simpler than the original D3-CNN, yet more effective:

self.layers = [
    Conv2D(filters=32, kernel_size=3, in_channels=3, padding=1),
    ReLU(),
    MaxPool2D(),
    Flatten(),
    Dense(input_size=16*16*32, output_size=128),
    ReLU(),
    Dense(input_size=128, output_size=NUM_CLASSES),
    Softmax()
]

📊 Architecture Diagram

Note: Please include the architecture diagram as architecture.png in the project root directory.

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📂 Dataset

• A mix of publicly available data and self-collected images.
• Download the dataset from this Google Drive link: 📎 Download Dataset

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✅ Model Performance

• Trained on CPU from scratch
• Epochs: 11
• Test Accuracy: ~86%
• Lightweight and fast-converging

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🚀 Setup & Installation

1. Clone the repository

git clone https://github.com/AS-0167/D3-CNN.git
cd D3-CNN

2. Install dependencies

pip install -r requirements.txt

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🧪 Running the Streamlit App

You can upload an image or a video and get distraction predictions.

streamlit run app.py

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🏋️‍♂️ Training the Model (Optional)

To retrain the model from scratch using the dataset:

1. Ensure dependencies are installed:

   pip install -r requirements.txt

2. Run the training notebook:

   jupyter notebook train-detect-distraction.ipynb

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🎓 About the Project

This project was created as a hands-on academic experiment to:

• Learn how CNNs work internally
• Understand forward and backward propagation using NumPy
• Handle dataset collection and preprocessing
• Build a mini deep learning pipeline without relying on big frameworks

No libraries like TensorFlow or PyTorch were used.

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👨‍💻 Author

[AS-0167] 🧑‍🎓 Student, Enthusiastic Learner of Machine Learning & Computer Vision