[EXE] Mask Detector in Live Cameras

[mlunghi]

Learning Goals

[Learning goals, bulleted/numbered list is preferred] [e.g. learn the concept and the use of train/validation/test dataset using scikit-learn ] Learn to preprocess images, use a new mobile neural network architecture, learn tensorflow.

Exercise Statement

[Explain and describe what the exercise is] [e.g. apply simple random-forest model to classify titanic survivability from titanic data ] Apply Mobile Net V2 model to detect whether someone is wearing a mask from live video.

Prerequisites

[Prerequisites, in terms of concepts or other exercises in this repo] [e.g. random-forest model, stochastic gradient descent, exercise #32] Python

Data source/summary:

[Provide a succinct summary of what the data is and where it is from] [e.g. This involves covid19 fatality dataset from John Hopkin's website (links..) ] Mask images form Github.

(Optional) Suggest/Propose Solutions

[e.g. I have the solution using PyTorch, will be happy to create pull request to include the exercise statement/solution] [e.g. I think chapter 3 of A. Geron's textbook works out the solution for this exercise] [e.g. fast.ai's chapter 5 has the perfect solution for this] I have the solution in scikit learn, will be happy to share

(Optional) Further Links/Credits to Relevant Resources:

[e.g. This exercise and solution's proposal came from a lab session from DL2020]

[gimseng]

@mlunghi This is a good idea ! Feel free to create the project. The latest available folder is 008, so feel free to create a pull request when you are ready.

[MrPotato-00]

Hello, I want to work on this issue. Can you assign this to me?