This project employs a Deep Neural Network, specifically a Convolutional Neural Network (CNN), to differentiate between images of people wearing masks, without masks, and those with incorrectly placed masks. Both manually built and pretrained networks will be utilized to perform this classification task.

• The Coronavirus pandemic has brought about significant changes in global lifestyles.
• Wearing masks has become crucial for individual safety.
• Detecting individuals without masks poses a challenge due to the large populations.
• This face mask detection project serves as a digitalized scanning tool applicable in various settings such as schools, hospitals, banks, airports, etc.
  • The project utilizes image processing and deep learning techniques to detect people's faces and segregate them into three classes: people with masks, people without masks, and those with partially worn masks.
  • The tool enables remote monitoring of face mask status, allowing efficient supervision and instructions from a remote location.

Upon completing this project, you will achieve the following learning objectives:

1. Load Image Dataset:

   • Gain the ability to load an image dataset using ImageDataGenerator from the specified path directory.

2. Perform Data Augmentation:

   • Learn to perform data augmentation on the fly to create batches of the dataset, enhancing model generalization.

3. Build Convolutional Neural Networks:

   • Develop skills to build Convolutional Neural Networks tailored for classification problems.

4. Visualize & Interpret CNN Layers:

   • Visualize and interpret what CNN layers learn, gaining insights into feature extraction.

5. Use Transfer Learning:

   • Apply transfer learning techniques using pre-trained models for effective solutions to classification problems.

• Face_Mask_Detection_using_CNN.ipynb: Jupyter notebook containing the project implementation and experimentation.

1. Clone the repository:

git clone https://github.com/Praveen76/Face-Mask-Detection-Using-CNN.git
cd Face-Mask-Detection-Using-CNN

2. Open and explore the Jupyter notebook Face_Mask_Detection_using_CNN.ipynb to understand the project implementation.

3. Execute the code cells within the notebook to experiment with the face mask detection using CNN.

4. Visualize the results and interpretations.

Feel free to contribute, report issues, or suggest improvements!

If you have a Data Science mini-project that you'd like to share, please follow the guidelines in CONTRIBUTING.md.

Please adhere to our Code of Conduct in all your interactions with the project.

This project is licensed under the MIT License.

For questions or inquiries, feel free to contact me on Linkedin.

I’m a seasoned Data Scientist and founder of TowardsMachineLearning.Org. I've worked on various Machine Learning, NLP, and cutting-edge deep learning frameworks to solve numerous business problems.