😷 Face Mask Detection using CNN (TensorFlow & Keras)

A deep learning project that detects whether a person is wearing a face mask or not using a Convolutional Neural Network (CNN).
Built with TensorFlow/Keras, this project demonstrates end-to-end image classification — from data preprocessing to model training and prediction.

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🧠 Project Overview

During the COVID-19 pandemic, face mask detection became an essential computer vision application.
This project aims to automatically classify facial images into two categories:

• 😷 With Mask
• 😶 Without Mask

Using Convolutional Neural Networks (CNNs), the model learns to identify subtle differences in facial features between masked and unmasked faces.
The trained model can later be integrated into real-time systems such as webcam apps or surveillance tools. you can find it in my kaggle notebook her: https://www.kaggle.com/code/aekmeddad/face-mask-dataset/notebook

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📂 Dataset

The dataset used in this project is the Face Mask Dataset (available on Kaggle or other public sources).

Structure:

data/ │ ├── with_mask/ │ ├── with_mask_1.jpg │ ├── with_mask_2.jpg │ └── ... │ └── without_mask/ ├── without_mask_1.jpg ├── without_mask_2.jpg └── ...

• With Mask: 3725 images
• Without Mask: 3828 images
• Total: 7553 images

All images were resized, normalized, and split into training and validation sets during preprocessing.

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⚙️ Installation & Setup

1️⃣ Clone the repository

git clone https://github.com/meddadaek/face-mask-detection.git
cd face-mask-detection

2️⃣ Install dependencies
pip install -r requirements.txt
🧑‍💻 Author

Doro Kanji
💼 AI & Deep Learning Enthusiast
🐙 GitHub: meddadaek

⭐ Contribute

Contributions are welcome!
If you’d like to improve this project:

Fork the repository

Create a new branch (feature/new-feature)

Commit your changes

Push the branch and open a Pull Request

🏁 License

This project is open source under the MIT License.
Feel free to use, modify, and share it — but please give credit where due. 💙



⚡ "Deep Learning is not about coding — it’s about teaching machines how to see and understand."

##SOME OF THE OUTPUT

---<img width="403" height="598" alt="Screenshot 2025-11-03 165503" src="https://github.com/user-attachments/assets/dc64dac0-ed50-48fc-8ef5-eebf7bae577b" />
<img width="597" height="581" alt="Screenshot 2025-11-03 165557" src="https://github.com/user-attachments/assets/f0343a23-3709-44e9-87e3-cd9bb8c2ee27" />