
Develop a cnn-based mask detection system using OpenCV to classify images and video streams as mask or no mask, with data augmentation, training and validation evaluation, and real-time predictions.
Set up Google Colab with a gpu runtime, import libraries for a CNN project, and explore sequential models to build face mask detection using convolution, pooling, dropout, and dense layers.
Explore the dataset structure for mask detection, with training, validation, and test splits organized into mask and no-mask subdirectories to train a CNN classifier and assess generalization and overfitting.
Learn to download the covid face mask dataset from Kaggle, unzip it, and organize train, test, and validation sets into mask and non-mask folders for CNN and OpenCV workflows.
Visualize a 4x4 grid of 16 images from the Covid-19 face mask dataset using matplotlib, showing eight masked and eight no-mask images, with titles derived from filenames.
Apply image augmentation for covid19 face mask detection, using flips, zoom, rotation, and normalization to expand the training set and reduce overfitting.
Build a convolutional neural network (cnn) from scratch using a sequential model, with convolutional layers, filters, relu activation, max pooling, dropout, flattening, and dense layers to classify images.
Compile and train a cnn model for covid19 face mask detection, monitor loss and accuracy to spot overfitting, and evaluate on test data.
Apply a trained convolutional neural network (cnn) model to predict mask presence on uploaded static images, returning whether each image shows a mask or no mask.
Explore face mask detection in video streams using a CNN model and OpenCV, including face detection with a cascade classifier and real-time mask predictions.
Would you like to learn how to detect if someone is wearing a Face Mask or not using Artificial Intelligence that can be deployed in bus stations, airports, or other public places?
Would you like to build a Convolutional Neural Network model using Deep learning to detect Covid-19 Face Mask?
If the answer to any of the above questions is "YES", then this course is for you.
Enroll Now in this course and learn how to detect Face Mask on the static images as well as in the video streams using Tensorflow and OpenCV.
As we know, COVID-19 has affected the whole world very badly. It has a huge impact on our everyday life, and this crisis is increasing day by day. In the near future, it seems difficult to eradicate this virus completely.
To counter this virus, Face Masks have become an integral part of our lives. These Masks are capable of stopping the spread of this deadly virus, which will help to control the spread. As we have started moving forward in this ‘new normal’ world, the necessity of the face mask has increased. So here, we are going to build a model that will be able to classify whether the person is wearing a mask or not. This model can be used in crowded areas like Malls, Bus stands, and other public places.
This is a hands-on Data Science guided project on Covid-19 Face Mask Detection using Deep Learning and Computer Vision concepts. We will build a Convolutional Neural Network classifier to classify people based on whether they are wearing masks or not and we will make use of OpenCV to detect human faces on the video streams. No unnecessary lectures. As our students like to say :
"Short, sweet, to the point course"
The same techniques can be used in :
Skin cancer detection
Normal pneumonia detection
Brain defect analysis
Retinal Image Analysis
Enroll now and You will receive a CERTIFICATE OF COMPLETION and we encourage you to add this project to your resume. At a time when the entire world is troubled by Coronavirus, this project can catapult your career to another level.
So bring your laptop and start building, training and testing the Data Science Covid 19 Convolutional Neural Network model right now.
You will learn:
How to detect Face masks on the static images as well as in the video streams.
Classify people who are wearing masks or not using deep learning
Learn to Build and train a Convolutional neural network
Make a prediction on new data using the trained CNN Model
We will be completing the following tasks:
Task 1: Getting Introduced to Google Colab Environment & importing necessary libraries
Task 2: Downloading the dataset directly from the Kagge to the Colab environment.
Task :3 Data visualization (Image Visualization)
Task 4: Data augmentation & Normalization
Task 5: Building Convolutional neural network model
Task 6: Compiling & Training CNN Model
Task 7: Performance evaluation & Testing the model & saving the model for future use
Task 8: Make use of the trained model to detect face masks on the static image uploaded from the local system
Task 9: Make use of the trained model to detect face masks on the video streams
So, grab a coffee, turn on your laptop, click on the ENROLL NOW button, and start learning right now.