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

Data Science:Deep Learning-CNN & OpenCV -Face Mask Detection, available at $19.99, has an average rating of 3, with 41 lectures, based on 2 reviews, and has 7 subscribers.

You will learn about 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 This course is ideal for individuals who are Anyone who is interested in Deep Learning. or Someone who want to learn Deep Learning, CNN, OpenCV, and also using and customizing pretrained models for image classification. or Someone who wants to use AI to detect the presence of face masks on images and video streams. It is particularly useful for Anyone who is interested in Deep Learning. or Someone who want to learn Deep Learning, CNN, OpenCV, and also using and customizing pretrained models for image classification. or Someone who wants to use AI to detect the presence of face masks on images and video streams.

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

Summary

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

Price: $19.99

Average Rating: 3

Number of Lectures: 41

Number of Published Lectures: 41

Number of Curriculum Items: 41

Number of Published Curriculum Objects: 41

Original Price: ₹999

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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.

Target Audiences

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.

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 buildingand 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]

Course Curriculum

Chapter 1: Introduction and Getting Started

Lecture 1: Project Overview

Lecture 2: Introduction to Google Colab

Lecture 3: Understanding the project folder structure

Chapter 2: Data Understanding & Importing Libraries

Lecture 1: Understanding the dataset and the folder structure

Lecture 2: Loading the data from Google Drive

Lecture 3: Importing the Libraries

Lecture 4: About Config and Resize File

Lecture 5: Some common Methods and Utilities

Chapter 3: Data Augmentation

Lecture 1: About Data Augmentation

Lecture 2: Implementing Data Augmentation techniques

Chapter 4: Data Generators

Lecture 1: About Data Generators

Lecture 2: Implementing Data Generators

Chapter 5: Model Building

Lecture 1: About Convolutional Neural Network (CNN)

Lecture 2: About OpenCV

Lecture 3: Understanding pre-trained models

Lecture 4: About MobileNetV2 model

Lecture 5: Loading the MobileNetV2 classifier

Lecture 6: Building a new fully-connected (FC) head

Lecture 7: Building the final model

Chapter 6: Compiling the Model

Lecture 1: Role of Optimizer in Deep Learning

Lecture 2: About Adam Optimizer

Lecture 3: About binary cross entropy loss function.

Lecture 4: Putting all together

Chapter 7: Fitting the Model

Lecture 1: About Epoch and Batch Size

Lecture 2: Model Fitting

Chapter 8: Model Evaluation

Lecture 1: Predicting on the test data

Lecture 2: About Classification Report

Lecture 3: Classification Report in action

Lecture 4: Plot training and validation accuracy and loss

Lecture 5: Serialize/Writing the model to disk

Chapter 9: Using trained model to predict face mask on images

Lecture 1: About Pretrained Caffe models for Face Detection

Lecture 2: Loading the face detection model from drive

Lecture 3: Loading the mask detection model from drive

Lecture 4: Extracting the Face Detections

Lecture 5: Using the trained mask detection model to predict face mask on images

Chapter 10: Using trained model to predict face mask on live video streaming

Lecture 1: Importing Libraries

Lecture 2: Function to detect and predict whether mask is present on a person's face in a v

Lecture 3: Loading our serialized face detector model from disk

Lecture 4: Loading the face mask detector model from disk

Lecture 5: Predicting face masks while looping over the video streams

Chapter 11: Project Files and Code

Lecture 1: Full Project Code

Instructors

AutomationGig .
ELEARNING HUB

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5 stars: 1 votes

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