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Data Science:Deep Learning-CNN & OpenCV -Face Mask Detection

Name: Data Science:Deep Learning-CNN & OpenCV -Face Mask Detection
Rating: 4.2 (3 reviews)

A practical hands on Deep Learning Project, using CNN and OpenCV to detect face masks in an image or live video streams

Created byAutomationGig .

Last updated 8/2025

English

What you'll learn

Data Analysis and Understanding
Data Augumentation
Data Generators
CNN and OpenCV
Pretrained Models like MobileNetV2
Compiling and Fitting a customized pretrained model
Model Evaluation
Model Serialization
Classification Metrics
Model Evaluation
Using trained model to detect face mask on images
Using trained model to detect face mask on video streams

Course content

11 sections • 41 lectures • 1h 43m total length

Project Overview1:22
Build a face mask detection model using deep learning, CNN, TensorFlow, Keras, and OpenCV in Python on Google Colab, then analyze data and evaluate on images and live video.
Introduction to Google Colab2:35
Understanding the project folder structure2:29
Navigate the project folder structure for dataset, pre-trained models, and output plots. Understand the dataset of masked and unmasked images and the training notebooks for CNN detection.

Understanding the dataset and the folder structure3:00
Understand the dataset structure with training, validation, and evolution folders, each containing masks and no masks subfolders. The data splits yield 6,042 training, 1,057 validation, and 454 testing images.
Loading the data from Google Drive3:21
Importing the Libraries2:24
Import tensorflow and keras libraries for the face mask detection project, including mobilenet, image preprocessing, and layers like average pooling, dropout, dense, with the Adam optimizer and a classification report.
About Config and Resize File6:19
Configure training in config file with dataset paths and mask vs no-mask classes, and use the resize utility to prepare frames for training and validation, and plot loss and accuracy.
Some common Methods and Utilities5:44

About Data Augmentation2:48
Data augmentation expands training data and improves deep learning models for face mask detection by generating varied, synthetic images through techniques like padding, rotation, flipping, and color changes.
Implementing Data Augmentation techniques4:11
Apply data augmentation to the training data for face mask detection with CNNs, scale pixel values to -1 to 1, and apply rotation, zoom, shift, shear, and flips for MobileNet.

About Convolutional Neural Network (CNN)1:52
Understand convolutional neural networks (CNN) and their convolutional layers with filters or kernels, pooling and fully connected layers, using softmax for image classification and segmentation, and automatic feature extraction.
About OpenCV2:19
Understanding pre-trained models1:30
About MobileNetV2 model1:24
Loading the MobileNetV2 classifier1:30
Load the MobileNetV2 classifier with ImageNet weights, exclude the top fully connected layers, and use the network's expected input size; in the next session we'll attach a custom head.
Building a new fully-connected (FC) head3:03
Construct a new fully-connected head atop a prepared base model, using average pooling and flattening, with ReLU activation, dropout, and a two-class softmax dense output for face mask detection.
Building the final model1:45

Predicting on the test data0:57
Predict on the test data using the trained CNN and OpenCV face mask detection model, pass required parameters, and generate predictions in the expected format for the genetic classification report.
About Classification Report1:16
Explore the classification report as a performance metric showing precision, recall, f1 score, and support, with precision and recall defined and the f1 score maximized when they match.
Classification Report in action1:44
Plot training and validation accuracy and loss2:31
Serialize/Writing the model to disk1:10
Save the face mask model to disk in H5 format using the output folder from the config file, then load it to predict on static images and light video streams.

About Pretrained Caffe models for Face Detection2:18
Learn to use pretrained caffe models for face detection in OpenCV to locate a face in an image, crop the first detected region, and run your mask-detection model on it.
Loading the face detection model from drive3:33
Loading the mask detection model from drive0:33
Load the face mask detection model from drive using the load model method and the saved model path, then use it to predict masks on faces.
Extracting the Face Detections5:06
Identify faces in images by loading a gap model, converting images to blobs, running the network, and filtering detections by confidence to draw bounding boxes for face mask detection.
Using the trained mask detection model to predict face mask on images4:13
Apply a trained face mask detection model to images by extracting faces, preprocessing (color conversion and 224x224 resizing), and predicting mask or no mask with green or red boxes.

Importing Libraries2:29
Import libraries and configure a live video stream in Google Colab, using video stream utilities and open source code to prepare a mask-detection model with a confidence threshold.
Function to detect and predict whether mask is present on a person's face in a v5:37
Learn to build a frame-by-frame function that detects faces in video frames, preprocesses each face, and predicts mask presence using batch inference for real-time performance.
Loading our serialized face detector model from disk0:55
Loading the face mask detector model from disk0:43
Predicting face masks while looping over the video streams5:51

Requirements

Basics knowledge of Python
Basic knowledge on Neural Networks and OpenCV is recommended

Description

If you want to learn the process to detect whether a person is wearing a face mask using AI and Machine Learning algorithms then this course is for you.

In this course I will cover, how to build a Face Mask Detection model to detect and predict whether a person is wearing a face mask or not in both static images and live video streams with very high accuracy using Deep Learning Models. This is a hands on project where I will teach you the step by step process in creating and evaluating a machine learning model using CNN and OpenCV.

This course will walk you through the initial data exploration and understanding, Data Augumentation, Data Generators, customizing pretrained Models like MobileNetV2, model building and evaluation. Then using the trained model to detect the presence of face mask in images and video streams.

I have splitted and segregated the entire course in Tasks below, for ease of understanding of what will be covered.

Task 1 : Project Overview.

Task 2 : Introduction to Google Colab.

Task 3 : Understanding the project folder structure.

Task 4 : Understanding the dataset and the folder structure.

Task 5 : Loading the data from Google Drive.

Task 6 : Importing the Libraries.

Task 7 : About Config and Resize File.

Task 8 : Some common Methods and Utilities

Task 9 : About Data Augmentation.

Task 10 : Implementing Data Augmentation techniques.

Task 11 : About Data Generators.

Task 12 : Implementing Data Generators.

Task 13 : About Convolutional Neural Network (CNN).

Task 14 : About OpenCV.

Task 15 : Understanding pre-trained models.

Task 16 : About MobileNetV2 model.

Task 17 : Loading the MobileNetV2 classifier.

Task 18 : Building a new fully-connected (FC) head.

Task 19 : Building the final model.

Task 20 : Role of Optimizer in Deep Learning.

Task 21 : About Adam Optimizer.

Task 22 : About binary cross entropy loss function.

Task 23 : Putting all together.

Task 24 : About Epoch and Batch Size

Task 25 : Model Fitting.

Task 26 : Predicting on the test data.

Task 27 : About Classification Report.

Task 28 : Classification Report in action.

Task 29 : Plot training and validation accuracy and loss.

Task 30 : Serialize/Writing the mode to disk.

Task 31 : About Pretrained Caffe models for Face Detection.

Task 32 : Loading the face detection model from drive.

Task 33 : Loading the mask detection model from drive.

Task 34 : Extracting the Face Detections.

Task 35 : Using the trained mask detection model to predict face mask on images.

Task 36 : Importing Libraries.

Task 37 : Function to detect and predict whether mask is present on a person's face in a video.

Task 38 : Loading our serialized face detector model from disk.

Task 39 : Loading the face mask detector model from disk.

Task 40 : Predicting face masks while looping over the video streams.

We all know the impact that COVID19 has made in our daily life and how face masks are becoming a new normal in our day to day life. Face masks have become one of the most important tool to stop or reduce the spread of the virus. In this course we will see how we can build a model to classify whether a person is wearing a face mask or not and the same can be used in crowded areas like malls, bus stand, etc.

Take the course now, and have a much stronger grasp of Deep learning in just a few hours!

You will receive :

1. Certificate of completion from AutomationGig.

2. All the datasets used in the course are in the resources section.

3. The Jupyter notebook and other project files are provided at the end of the course in the resource section.

So what are you waiting for?

Grab a cup of coffee, click on the ENROLL NOW Button and start learning the most demanded skill of the 21st century. We'll see you inside the course!

Happy Learning !!

[Please note that this course and its related contents are for educational purpose only]

[Music : bensound]

Who this course is for:

Anyone who is interested in Deep Learning.
Someone who want to learn Deep Learning, CNN, OpenCV, and also using and customizing pretrained models for image classification.
Someone who wants to use AI to detect the presence of face masks on images and video streams.

Data Science:Deep Learning-CNN & OpenCV -Face Mask Detection

What you'll learn

Explore related topics

Course content

Introduction and Getting Started3 lectures • 6min

Data Understanding & Importing Libraries5 lectures • 21min

Data Augmentation2 lectures • 7min

Data Generators2 lectures • 6min

Model Building7 lectures • 13min

Compiling the Model4 lectures • 7min

Fitting the Model2 lectures • 3min

Model Evaluation5 lectures • 8min

Using trained model to predict face mask on images5 lectures • 16min

Using trained model to predict face mask on live video streaming5 lectures • 16min

Requirements

Description

Who this course is for: