Udemy - Face Mask Recognition Desktop App with Deep Learning & PyQT

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Udemy - Face Mask Recognition Desktop App with Deep Learning & PyQT (Size: 1.2 GB)
  1. Bonus Lecture.html 1 KB
  1. Download Resources.html 307.2 B
  1. Install Python.mp4 16.9 MB
  1. Install Python.srt 2.8 KB
  1. Introduction.mp4 23.5 MB
  1. Introduction.srt 3.4 KB
  1. Load Numpy Zip Data into Notebook.mp4 14 MB
  1. Load Numpy Zip Data into Notebook.srt 5 KB
  1. Load TensorFlow based CNN Model in a Notebook.mp4 19.6 MB
  1. Load TensorFlow based CNN Model in a Notebook.srt 6.8 KB
  1. What you will Develop.mp4 13.6 MB
  1. What you will Develop.srt 1.5 KB
  1. What you will develop.mp4 10.8 MB
  1. What you will develop.srt 2.2 KB
  10. Face Detection Load Model.mp4 6.8 MB
  10. Face Detection Load Model.srt 1.9 KB
  10. QLabel.mp4 30.7 MB
  10. QLabel.srt 7.5 KB
  11. Face Detection Blob from Image.mp4 16.4 MB
  11. Face Detection Blob from Image.srt 4 KB
  11. QLineEdit.mp4 12 MB
  11. QLineEdit.srt 3 KB
  12. Draw Bounding Box for Detected Face.mp4 41.6 MB
  12. Draw Bounding Box for Detected Face.srt 9 KB
  12. QPushButton.mp4 9 MB
  12. QPushButton.srt 2.5 KB
  13. QComboBox.mp4 9.8 MB
  13. QComboBox.srt 2.2 KB
  13. Step - 4, Crop the Detected Face.mp4 30.4 MB
  13. Step - 4, Crop the Detected Face.srt 5 KB
  14. Placing & Arranging Widgets.mp4 5 MB
  14. Placing & Arranging Widgets.srt 2.2 KB
  14. Step - 5, Image Processing - Blob from Image (RGB mean subtraction image).mp4 37.1 MB
  14. Step - 5, Image Processing - Blob from Image (RGB mean subtraction image).srt 8.5 KB
  15. Placing Widgets using QHBoxLayout and QVBoxLayout.mp4 41.1 MB
  15. Placing Widgets using QHBoxLayout and QVBoxLayout.srt 9.3 KB
  15. Step - 5, Image Processing - Rotate & Flip Image.mp4 19.4 MB
  15. Step - 5, Image Processing - Rotate & Flip Image.srt 3.9 KB
  16. Signals and Slots.mp4 24.3 MB
  16. Signals and Slots.srt 4.5 KB
  16. Step -5, Remove Negative values and Normalize.mp4 19.6 MB
  16. Step -5, Remove Negative values and Normalize.srt 4.5 KB
  17. Apply Data Preparation process to All images.mp4 35.6 MB
  17. Apply Data Preparation process to All images.srt 9.2 KB
  17. Backend Operations in PyQt.mp4 48.9 MB
  17. Backend Operations in PyQt.srt 8.7 KB
  18. Step - 6, Save Preprocessed Data in Numpy zip.mp4 12.9 MB
  18. Step - 6, Save Preprocessed Data in Numpy zip.srt 3.6 KB
  2. Create Virtual Environment in Python.mp4 4.7 MB
  2. Create Virtual Environment in Python.srt 2.7 KB
  2. Data.mp4 36.2 MB
  2. Data.srt 7.3 KB
  2. Defining Labels and Setting Colors.mp4 15.4 MB
  2. Defining Labels and Setting Colors.srt 4.9 KB
  2. Install Visual Studio Code.mp4 28.5 MB
  2. Install Visual Studio Code.srt 8.4 KB
  2. One Hot Encoding to target or output variable (y).mp4 21.1 MB
  2. One Hot Encoding to target or output variable (y).srt 5.8 KB
  2. Setting up Visual studio code.mp4 7.7 MB
  2. Setting up Visual studio code.srt 2.5 KB
  2.1 1_Download_the_data.pdf 571.7 KB
  3. Create Main Window.mp4 7.7 MB
  3. Create Main Window.srt 3 KB
  3. Data Preparation Process.mp4 24.1 MB
  3. Data Preparation Process.srt 5.3 KB
  3. Install Libraries like TensorFlow 2, OpenCV etc..mp4 31 MB
  3. Install Libraries like TensorFlow 2, OpenCV etc..srt 6 KB
  3. Setting Up Project.mp4 28.5 MB
  3. Setting Up Project.srt 8.4 KB
  3. Split the Data into Train and Test sets.mp4 10.1 MB
  3. Split the Data into Train and Test sets.srt 3.2 KB
  3. Step - 1, Face Detection.mp4 49.4 MB
  3. Step - 1, Face Detection.srt 13.3 KB
  4. Convolutional Neural Network Architecture.mp4 13.3 MB
  4. Convolutional Neural Network Architecture.srt 9.1 KB
  4. Data Preparation Import Required Python Libraries.mp4 11.3 MB
  4. Data Preparation Import Required Python Libraries.srt 4 KB
  4. Install PyQt and Connect VS code to Virtual Environment.mp4 5.4 MB
  4. Install PyQt and Connect VS code to Virtual Environment.srt 1.7 KB
  4. PyQT Front End Design of Desktop App.mp4 30.6 MB
  4. PyQT Front End Design of Desktop App.srt 8.4 KB
  4. Step -2, Data Preprocess.mp4 31.2 MB
  4. Step -2, Data Preprocess.srt 6.9 KB
  5. Data Preparation Get all Images Path in Folder.mp4 24.6 MB
  5. Data Preparation Get all Images Path in Folder.srt 6.7 KB
  5. Develop CNN model in TensorFlow 2.mp4 35.2 MB
  5. Develop CNN model in TensorFlow 2.srt 9 KB
  5. Face Mask Desktop App using PyQt.html 102.4 B
  5. PyQt Background.mp4 7.7 MB
  5. PyQt Background.srt 3.1 KB
  5. Step - 3, Get Predictions from CNN Model for Face Mask.mp4 33.7 MB
  5. Step - 3, Get Predictions from CNN Model for Face Mask.srt 6.2 KB
  6. Compile CNN model, Setting Adam Optimizer & Loss Function.mp4 20.8 MB
  6. Compile CNN model, Setting Adam Optimizer & Loss Function.srt 4.2 KB
  6. Data Preparation Labeling.mp4 8.8 MB
  6. Data Preparation Labeling.srt 2 KB
  6. Generate text for Prediction info.mp4 28.6 MB
  6. Generate text for Prediction info.srt 5 KB
  6. Your First PyQt App with QtWidgets.mp4 17.4 MB
  6. Your First PyQt App with QtWidgets.srt 6.1 KB
  7. Data Preparation Get Images Path and Labelling Images in multiple Folders.mp4 22.1 MB
  7. Data Preparation Get Images Path and Labelling Images in multiple Folders.srt 2.6 KB
  7. Get Face Mask Prediction to an Image.mp4 33.5 MB
  7. Get Face Mask Prediction to an Image.srt 5.7 KB
  7. Qt Template.mp4 16.7 MB
  7. Qt Template.srt 4.7 KB
  7. Train CNN model.mp4 11.4 MB
  7. Train CNN model.srt 2.9 KB
  8. QtWidgets.mp4 4.1 MB
  8. QtWidgets.srt 1.8 KB
  8. Real Time Face Mask Prediction.mp4 28.5 MB
  8. Real Time Face Mask Prediction.srt 5.6 KB
  8. Save Deep Learning Model in TensorFlow.mp4 24.8 MB
  8. Save Deep Learning Model in TensorFlow.srt 5.3 KB
  8. Step - 3, Face Detection.mp4 4.9 MB
  8. Step - 3, Face Detection.srt 1.2 KB
  9. Face Detection Read Image.mp4 10.4 MB
  9. Face Detection Read Image.srt 2.5 KB
  9. QWidget.mp4 28.1 MB
  9. QWidget.srt 8.6 KB
  Bonus Resources.txt 307.2 B
  Get Bonus Downloads Here.url 204.8 B
  hello_world.py 102.4 B
  lemon.jpg?042148 35.8 KB
  peach.jpg?042148 34.8 KB
  qt_icon.png 5 KB
  qt_template.py 2.3 KB
  raddish.jpeg 56.2 KB
  sample.jpg?042148 748.5 KB
  strawberry.jpg?042148 41.3 KB
  ▲ 130 total files

Description


Face Mask Recognition Desktop App with Deep Learning & PyQT
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 61 lectures (3h 59m) | Size: 958.6 MB
Learn Face Recognition for Face Mask Detection using Python, TensorFlow 2, OpenCV, PyQT, Qt
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

Requirements
Basic Python Knowledge
Familiar with Tensor Flow and Deep Learning
Familiar with Numpy and Pandas

Description
Project that you will be Developing:

Prerequisite of Project: OpenCV

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