EyeMasker: An Efficient Mask Detection System

Introduction

EyeMasker is a machine learning project developed to detect whether individuals in images are wearing face masks. It uses Python, OpenCV, and popular machine learning frameworks to build and train a Convolutional Neural Network (CNN) model. This project is structured to run efficiently in Google Colab, leveraging cloud-based resources for easy access and scalability.

Key Features

• Flexible Data Loading: Easily import and preprocess image datasets from Google Drive.
• Comprehensive Data Labeling: Categorize images into distinct "with mask" and "without mask" classes.
• Robust Model Training: Build and train a deep learning CNN model specifically for mask detection.
• Performance Evaluation: Evaluate the model's performance with high precision using test data.
• Real-Time Inference: Make fast and accurate predictions on new, unseen images.

Requirements

• Python 3.x
• Google Colab (for execution)
• Google Drive (for storing and loading datasets)
• Essential Libraries: OpenCV, TensorFlow/Keras, NumPy, os

Setup Instructions

1. Clone this repository or download the project files to your system:

   git clone https://github.com/RudranshVyas-3107/EyeMasker.git

2. Open mask_detection.ipynb in Google Colab.
3. Install the required Python libraries using the following commands:

   !pip install opencv-python
   !pip install tensorflow

Usage Guide

1. Mount Google Drive: Use Google Colab to mount your Google Drive, making it easy to access your dataset.
2. Data Preparation: Load and label your dataset, ensuring images are correctly categorized into "with mask" and "without mask" groups.
3. Train the Model: Use the provided CNN architecture to train your model on the labeled dataset.
4. Evaluate Model: Assess the model's performance using validation and test datasets.
5. Run Inference: Use the trained model to predict mask usage on new, unseen images.

Dataset Structure

Ensure that your dataset in Google Drive is organized as follows:

• with_mask: Contains images of individuals wearing face masks.
• without_mask: Contains images of individuals without face masks.

Future Improvements

• Model Optimization: Explore different CNN architectures to enhance model accuracy.
• Real-Time Detection: Implement the system in real-time video streams using OpenCV.
• Deployment: Deploy the model on a cloud platform to allow for scalable, real-time mask detection services.

Contributions

Contributions to improve EyeMasker are welcome! Feel free to fork this repository, make changes, and submit a pull request with your improvements or suggestions.

Acknowledgements

This project leverages open-source libraries like OpenCV and TensorFlow. Special thanks to the creators of the datasets that made the training and evaluation of this model possible.