Face Mask Detection is a project to determine whether someone is wearing mask or not, using deep neural network.

Last update: Jan 18, 2022

Overview

face-mask-detection

Face Mask Detection is a project to determine whether someone is wearing mask or not, using deep neural network. It contains 3 scripts that provides training new model, testing model with specific images and live face mask detection.
The gif below shows how live face mask detection works.

This model has trained with 10k face images (5k without mask and 5k with mask - dataset sources are mentioned in dataset section) using 3 layers of 2d CNN as main process. (the complete summary of model layers is given below)

Model: "sequential"                                               
_________________________________________________________________ 
Layer (type)                 Output Shape              Param #    
================================================================= 
rescaling (Rescaling)        (None, 180, 180, 3)       0          
_________________________________________________________________ 
conv2d (Conv2D)              (None, 180, 180, 16)      448        
_________________________________________________________________ 
max_pooling2d (MaxPooling2D) (None, 90, 90, 16)        0          
_________________________________________________________________ 
conv2d_1 (Conv2D)            (None, 90, 90, 32)        4640       
_________________________________________________________________ 
max_pooling2d_1 (MaxPooling2 (None, 45, 45, 32)        0          
_________________________________________________________________ 
conv2d_2 (Conv2D)            (None, 45, 45, 64)        18496      
_________________________________________________________________ 
max_pooling2d_2 (MaxPooling2 (None, 22, 22, 64)        0          
_________________________________________________________________ 
flatten (Flatten)            (None, 30976)             0          
_________________________________________________________________ 
dense (Dense)                (None, 128)               3965056    
_________________________________________________________________ 
dense_1 (Dense)              (None, 2)                 258        
================================================================= 
Total params: 3,988,898                                           
Trainable params: 3,988,898                                       
Non-trainable params: 0                                           
_________________________________________________________________

This model has reached fantastic result of 0.9990 accuracy with test dataset( 20% ). moreover, it only takes 10 epochs to reach this result. As the graph below illustrates, from the very first epoch, accuracy is more than 0.98 and by 10th epoch, loss roughly touchs 10e-7 that is a great result.

usage

Currently, there is not any argument parser available, so if you want change any default variable, you can change variables in configurations section at the begining of each script. schema is given below.

DATASET_PATH = "./dataset/" # to provide dataset, create a directory containing all classes as a directory
# and put images to relevant class. ex:
# dataset --> no_mask --> img1, img2, ...
#             correct_mask --> img1, img2, ...
OUTPUT_MODEL_PATH = 'saved_model/my_model' # output file to save model
OUTPUT_WEIGHTS_PATH = 'saved_weight/my_checkpoint2' # output file to save model's weights
EPOCHS = 10 # number of epochs
BATCH_SIZE = 32 # bach size
IMG_HEIGHT = 180 # image height
IMG_WIDTH = 180 # image width

To train a new model, after changing the configuration above, just run:

python train_model.py

To test the trained model with some specific images, run:

python test_model.py img1.jpg img2.jpg ....

At last, to run live detection mode, run:

python live_detection.py

dataset

The dataset we have used, is the combination of Flickr-Faces-HQ (FFHQ) dataset and MaskedFace-Net dataset. 5k images from each dataset. (summation of both datasets is more than 180k images)

Releases(v0.1)

v0.1(Sep 8, 2021)

Face-Mask-Detection 0.1 Release

The first official version.
Source code(tar.gz)
Source code(zip)

Face Library is an open source package for accurate and real-time face detection and recognition

Face Library Face Library is an open source package for accurate and real-time face detection and recognition. The package is built over OpenCV and us

52 Nov 9, 2022

AI Face Mesh: This is a simple face mesh detection program based on Artificial intelligence.

AI Face Mesh: This is a simple face mesh detection program based on Artificial Intelligence which made with Python. It's able to detect 468 different

1 Jan 13, 2022

Cancer-and-Tumor-Detection-Using-Inception-model - In this repo i am gonna show you how i did cancer/tumor detection in lungs using deep neural networks, specifically here the Inception model by google.

Cancer-and-Tumor-Detection-Using-Inception-model In this repo i am gonna show you how i did cancer/tumor detection in lungs using deep neural networks

1 Jan 1, 2022

Jetson Nano-based smart camera system that measures crowd face mask usage in real-time.

MaskCam MaskCam is a prototype reference design for a Jetson Nano-based smart camera system that measures crowd face mask usage in real-time, with all

212 Dec 29, 2022

A short code in python, Enchpyter, is able to encrypt and decrypt words as you determine, of course

Enchpyter Enchpyter is a program do encrypt and decrypt any word you want (just letters). You enter how many letters jumps and write the word, so, the

2 Oct 10, 2022

An e-commerce company wants to segment its customers and determine marketing strategies according to these segments.

customer_segmentation_with_rfm Business Problem : An e-commerce company wants to

3 Jan 6, 2022

Program your own vulkan.gpuinfo.org query in Python. Used to determine baseline hardware for WebGPU.

query-gpuinfo-data License This software is not presently released under a license. The data in data/ is obtained under CC BY 4.0 as specified there.

5 Jul 18, 2022

This project contains an implemented version of Face Detection using OpenCV and Mediapipe. This is a code snippet and can be used in projects.

Live-Face-Detection Project Description: In this project, we will be using the live video feed from the camera to detect Faces. It will also detect so

3 Oct 2, 2021

PocketNet: Extreme Lightweight Face Recognition Network using Neural Architecture Search and Multi-Step Knowledge Distillation

PocketNet This is the official repository of the paper: PocketNet: Extreme Lightweight Face Recognition Network using Neural Architecture Search and M

40 Dec 22, 2022

Face Mask Detection is a project to determine whether someone is wearing mask or not, using deep neural network.

Related tags

Overview

face-mask-detection

usage

dataset

You might also like...

Face Library is an open source package for accurate and real-time face detection and recognition

AI Face Mesh: This is a simple face mesh detection program based on Artificial intelligence.

Cancer-and-Tumor-Detection-Using-Inception-model - In this repo i am gonna show you how i did cancer/tumor detection in lungs using deep neural networks, specifically here the Inception model by google.

Jetson Nano-based smart camera system that measures crowd face mask usage in real-time.

A short code in python, Enchpyter, is able to encrypt and decrypt words as you determine, of course

An e-commerce company wants to segment its customers and determine marketing strategies according to these segments.

Program your own vulkan.gpuinfo.org query in Python. Used to determine baseline hardware for WebGPU.

This project contains an implemented version of Face Detection using OpenCV and Mediapipe. This is a code snippet and can be used in projects.

PocketNet: Extreme Lightweight Face Recognition Network using Neural Architecture Search and Multi-Step Knowledge Distillation

Releases(v0.1)

v0.1(Sep 8, 2021)

Face-Mask-Detection 0.1 Release

Owner

amirsalar

Rank 3 : Source code for OPPO 6G Data Generation Challenge

PyTorch implementation of the R2Plus1D convolution based ResNet architecture described in the paper "A Closer Look at Spatiotemporal Convolutions for Action Recognition"

load .txt to train YOLOX, same as Yolo others

Code for the SIGGRAPH 2021 paper "Consistent Depth of Moving Objects in Video".

The (Official) PyTorch Implementation of the paper "Deep Extraction of Manga Structural Lines"

Source Code for DialogBERT: Discourse-Aware Response Generation via Learning to Recover and Rank Utterances (https://arxiv.org/pdf/2012.01775.pdf)

Paper: De-rendering Stylized Texts

This is the official Pytorch implementation of "Lung Segmentation from Chest X-rays using Variational Data Imputation", Raghavendra Selvan et al. 2020

Code for the KDD 2021 paper 'Filtration Curves for Graph Representation'

A Dying Light 2 (DL2) PAKFile Utility for Modders and Mod Makers.

Voxel-based Network for Shape Completion by Leveraging Edge Generation (ICCV 2021, oral)

Galileo library for large scale graph training by JD

Bravia core script for python

Paper Code：A Self-adaptive Weighted Differential Evolution Approach for Large-scale Feature Selection

Official pytorch implementation of "DSPoint: Dual-scale Point Cloud Recognition with High-frequency Fusion"

Unofficial keras(tensorflow) implementation of MAE model from Masked Autoencoders Are Scalable Vision Learners

Deep Learning & 3D Convolutional Neural Networks for Speaker Verification

[NeurIPS2021] Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks

TCNN Temporal convolutional neural network for real-time speech enhancement in the time domain

The Submission for SIMMC 2.0 Challenge 2021