COVID-19 Facemask Detector

Overview

The COVID-19 Facemask Detector is an application developed using Python and the Keras API, employing deep learning techniques to determine whether a person in an image is wearing a facemask. The primary goal is to contribute to public health initiatives by providing a reliable tool for facemask detection, essential in the ongoing efforts to combat the spread of COVID-19.

Features

• Convolutional Neural Network (CNN):

  • Utilizes a ConvNet architecture for image classification, leveraging convolution layers to efficiently extract and learn patterns from the dataset.

• Optimization Techniques:

  • Implements various optimization techniques, including regularization, different optimizers, and hyper-parameter tuning, to enhance the model's accuracy and prevent overfitting.

• Image Augmentation:

  • Applies image augmentation to diversify the dataset, ensuring the model's robustness and ability to generalize effectively to real-world scenarios.

• Dataset Compilation:

  • Gathers a comprehensive dataset from Kaggle and supplements it with self-captured images from real-world surroundings, enhancing the model's adaptability.

Getting Started

Prerequisites

• Python 3.x
• Keras
• OpenCV
• Matplotlib
• NumPy
• scikit-learn

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/COVID-19-Facemask-Detector.git
   cd COVID-19-Facemask-Detector

2. Install dependencies:

   pip install -r requirements.txt

Usage

To test out the model onto the real data, simply run the code in the following sections of Jupyter Notebook:

• Importing the required libraries
• Building our Neural Network Model
• Loading the weights of the model
• Testing the model onto the real data

Once the script is running, the facemask status will be detected in real-time. 

The application will keep running until the Esc key is pressed.