Udemy

Face Mask Recognition: Deep Learning based Desktop App

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  • 3,483 Students
  • Updated 6/2022
  • Certificate Available
4.6
(85 Ratings)
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Course Information

Registration period
Year-round Recruitment
Course Level
Study Mode
Duration
4 Hour(s) 36 Minute(s)
Language
English
Taught by
G Sudheer, datascience Anywhere, Brightshine Learn
Certificate
  • Available
  • *The delivery and distribution of the certificate are subject to the policies and arrangements of the course provider.
Rating
4.6
(85 Ratings)

Course Overview

Face Mask Recognition: Deep Learning based Desktop App

Learn and Build Face Recognition for Face Mask Detection Desktop App using Python, TensorFlow 2, OpenCV, PyQT, Qt

Project that you will be Developing:

Prerequisite of Project: OpenCV

  1. Image Processing with OpenCV

Section -0 : Setting Up Project

  1. Install Python

  2. Install Dependencies

Section -1 : Data Preprocessing

  1. Gather Images

  2. Extract Faces only from Images

  3. Labeling (Target output) Images

  4. Data Preprocessing

    1. RGB mean subtraction image

Section - 2: Develop Deep Learning Model

  1. Training Face Recognition with OWN Deep Learning Model.

    1. Convolutional Neural Network

  2. Model Evaluation

Section - 3: Prediction with CNN Model

1. Putting All together


Section - 4: PyQT Basics

Section -5: PyQt based Desktop Application


Overview:

I will start the course by installing Python and installing the necessary libraries in Python for developing the end-to-end project. Then I will teach you one of the prerequisites of the course that is image processing techniques in OpenCV and the mathematical concepts behind the images. We will also do the necessary image analysis and required preprocessing steps for the images. Then we will do a mini project on Face Detection using OpenCV and Deep Neural Networks.

With the concepts of image basics, we will then start our project phase-1, face identity recognition. I will start this phase with preprocessing images, we will extract features from the images using deep neural networks. Then with the features of faces, we will train the different Deep learning models like Convolutional Neural Network.  I will teach you the model selection and hyperparameter tuning for face recognition models

Once our Deep learning model is ready, will we move to Section-3, and write the code for preforming predictions with CNN model.

Finally, we will develop the desktop application and make prediction to live video streaming.

What are you waiting for? Start the course develop your own Computer Vision Flask Desktop Application Project using Machine Learning, Python and Deploy it in Cloud with your own hands.

Course Content

  • 8 section(s)
  • 68 lecture(s)
  • Section 1 Introduction
  • Section 2 Setting Up Project
  • Section 3 Data Preparation & Preprocessing
  • Section 4 Face Recognition Model for Mask Identification with Deep Learning
  • Section 5 Predictions with Face Recognition model for Face Mask
  • Section 6 PyQt Basics
  • Section 7 Desktop App with PyQt
  • Section 8 BONUS

What You’ll Learn

  • Face Recognition for Mask detection with Deep Learning
  • Develop Convolutional Network Network for Face Mask from Scratch using TensorFlow
  • Preprocess the big data of image
  • OpenCV for Face Detection
  • Computer Vision Desktop Application with PyQt
  • PyQt Essential Concepts


Reviews

  • E
    Evgeny Gushchin
    4.5

    The course is overall very good especially for beginners. I am not giving this course full five stars but only 4.5 because some aspects were not well explained such as CNN training and how to read CNN's outputs.

  • V
    Vinat Goyal
    3.0

    It would be more efficient to use dictionary instead of a function to determine the color for each class.

  • M
    Muhammad Rizki Budiman
    5.0

    for the learning content so far is excellent, but the reviewer takes too much time (1 week) to respond our question and only ask for screenshot then disappear again, i meant even this course its alreeady 2 years, the reviewer should respond at least 1 day.

  • H
    Hatice Kubra Guc
    5.0

    its very good to learn basics and examples of python tensorflow

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