Aim of the Project

To build a Web App using Deep Convolutional Neural Networks to predict whether a person is Wearing a Mask or Not

Life Cycle of the Project

1. Collected the dataset from Kaggle which contained 12,000 images in total for both the classes
2. Used Data Augmentation to generate extra data from the existing dataset
3. Applied Transfer Learning using Pre-trained VGG16 model and built a High Performance Custom CNN Model providing an Accuracy of 99.60 % on the Testing Dataset
4. Saved the DL model with an h5 extension
5. Built a Web App using Python at the Backend with Flask API and HTML, CSS & JS serving at the Frontend
6. The Web App allows the User to upload an image, makes predictions on this image using the saved DL model and sends a Prediction text indicating whether the person in the image is Wearing a Mask or Not

Results from the Project

• If a person is Wearing a Mask

• If a person is Not wearing a Mask

Technologies Used

| Python | Tensorflow | Keras | CNN | VGG16 |

| Flask | Matplotlib | Numpy |

Performance of the Model

• Training Accuracy = 95.82 %

• Validation Accuracy = 97.71 %

• Testing Accuracy = 99.60 %

• Training Loss = 10.53 %

• Validation Loss = 6.8 %

• Testing Loss = 1.2 %

Check out the Project Demo

GitHub Repository