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!
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.




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