/Face_Mask_Detector_WebAPP-By_Streamlit_Heroku-

This Project has been implemented by using OpenCV to detect faces in the input images and a pre-trained Keras CNN model (MobileNetV2) as mask/no-mask binary classifier applied to the faces Images. The Deep Learning model currently used has been trained using images data set from Kaggle. The trained model has been shared in this repo.

Primary LanguageJupyter Notebook

Deployment_PROJECT (Face Mask Detector) Using Streamlit and Heroku by Applying Pre- Trained CNN Model (MobileNetV2)

by Mohamed Sebaie Sebaie

This is a simple Streamlit frontend for face mask detection in images using a pre-trained Keras CNN model MobileNetV2 and OpenCV then deploy on heroku.

The Web Application I Created, is in This Link.

The Data used for training can be found through This Link on Kaggle Website.

All work here is done on CoLab

General Info

  • This Project has been implemented by using OpenCV to detect faces in the input images and a a pre-trained Keras CNN model (MobileNetV2) as mask/no-mask binary classifier applied to the faces Images. The Deep Learning model currently used has been trained using this image data set from kaggle here . The trained model has been shared in this repo. The face detector algorithm comes from here: the Caffee model files are in CAFFEE folder directory.

Web APP Explanation

Once an image has been uploaded, the classification happens automatically.

About The Data:

The dataset used for Training consists of one zip file Face Mask Dataset that is download in Colab and unzipped then Create a pre-trained Keras CNN model (MobileNetV2) and Training then evaluate, save and test the model. The NoteBooks are in face_mask_detector_notebooks.

Finally, After creating the Model and save as h5 file, Deploy the model with Streamlit frontend and upload it toHeroku Platform..

The Web Application I Created, is in This Link.

Good Reference for Deployment a Streamlit Frontend to Heroku here.