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The objective is to create a Real-Time Face Mask Detector which can solve monitoring issues in crowded areas such as Airports, Metros, etc. using CNN and OpenCV.
The goal is to create a Deep Learning model to detect in real-time whether a person is wearing a face mask or not
This project includes:
Preparing a detailed python notebook using CNN for detecting Face Masks in Real-time.
Designing a Convolutional Neural Network (CNN) Model using AveragePooling2D, Flatten, Dense, and Dropout layers
Using MobileNetv2 as the base model
Compiling the Model using Adam optimizer, Binary Crossentropy loss, and accuracy metric functions
Using EarlyStopping Callback to terminate the training if there is no improvement in the monitor performance measure of your choice for certain epochs in a row
Making a plot for the loss function to visualize the change in the loss at every epoch
Making a plot for the accuracy metric to visualize the accuracy at every epoch
Saving the entire model that includes the model’s architecture, weights, and training configuration,
Testing the model using a webcam using OpenCV, and detect the Face Masks in real-time.
This project can be used as final year project, capstone project, personal portfolio project, resume, proof of concept.
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