Face Mask Detection

Introduction

This project aims to create a face mask detection system using Convolutional Neural Networks (CNNs). It uses the TensorFlow and Keras libraries to build and train the model, and the OpenCV library to process video input for real-time detection. .

Technologies Used

• Python: Programming language used for the project.
• TensorFlow: Open-source machine learning framework used to build and train the model.
• Keras: High-level neural networks API, running on top of TensorFlow.
• OpenCV: Open-source computer vision library used for image and video processing.
• NumPy: Library for numerical operations in Python.
• Matplotlib: Library for creating static, animated, and interactive visualizations in Python.
• Google Colab: Cloud service used for training and testing the model.

Features

• Data Augmentation: Utilizes ImageDataGenerator for real-time data augmentation.
• Model Architecture: Consists of convolutional layers, max pooling layers, dropout layers, and dense layers.
• Training and Validation: Trains the model with training data and validates it using validation data.
• Real-Time Detection: Uses OpenCV to process video input and detect the presence of a face mask.

How to Run

1. Clone the Repository

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

Contact

• LinkedIn: Aaditya Raj Pandey
• GitHub: aaditya620321