A deep learning CNN model to identify and classify and check if a person is wearing a mask or not.

Last update: Mar 01, 2022

Related tags

Deep Learning FaceMask-detection

Overview

Face Mask Detection

The Model is designed to check if any human is wearing a mask or not.

Dataset Description

The Dataset contains a total of 11,792 images divided in three folders

Test (992 images),
Train (10,000 images),
Valid (800 images),

Each of these folders has sub-folders :

Test
- WithMask
- WithoutMask
Train
- WithMask
- WithoutMask
Valid
- WithMask
- WithoutMask

you can download the dataset here

Convolution Base (VGG19)

to import the model, run

  from tensorflow.keras.applications import VGG19

Model Parameters

The total number of parameters of the model are 22,122,049

Result

The training stablized due to early stopping after achieving the following parametrs

Accuracy	in percentage
Training Accuracy	99.62
Validation Accuracy	99.75
Testing Accuracy	1.0

Loss	in decimal
Training loss	0.0213
Validation loss	0.0760
Testing loss	5.9828e-04

The model trained for 16 epochs before early stopping conditions were met.

Owner

GitHub Repository

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A deep learning CNN model to identify and classify and check if a person is wearing a mask or not.

Related tags

Overview

Face Mask Detection

Dataset Description

Convolution Base (VGG19)

Model Parameters

Result

Owner

Serving PyTorch 1.0 Models as a Web Server in C++

RuleBERT: Teaching Soft Rules to Pre-Trained Language Models

Accelerated deep learning R&D

SCALoss: Side and Corner Aligned Loss for Bounding Box Regression (AAAI2022).

Hashformers is a framework for hashtag segmentation with transformers.

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Companion repo of the UCC 2021 paper "Predictive Auto-scaling with OpenStack Monasca"

Code for the prototype tool in our paper "CoProtector: Protect Open-Source Code against Unauthorized Training Usage with Data Poisoning".

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