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

🐸STT integration examples

Contrastive learning of Class-agnostic Activation Map for Weakly Supervised Object Localization and Semantic Segmentation (CVPR 2022)

Train a deep learning net with OpenStreetMap features and satellite imagery.

Implementation of FSGNN

CTRL-C: Camera calibration TRansformer with Line-Classification

Data reduction pipeline for KOALA on the AAT.

The implementation of 'Image synthesis via semantic composition'.

Pynomial - a lightweight python library for implementing the many confidence intervals for the risk parameter of a binomial model

Spectralformer: Rethinking hyperspectral image classification with transformers

Unofficial implementation of the paper: PonderNet: Learning to Ponder in TensorFlow

FrankMocap: A Strong and Easy-to-use Single View 3D Hand+Body Pose Estimator

An implementation of the "Attention is all you need" paper without extra bells and whistles, or difficult syntax

Deep Inside Convolutional Networks - This is a caffe implementation to visualize the learnt model

A toolkit for controlling Euro Truck Simulator 2 with python to develop self-driving algorithms.

Transformer Tracking (CVPR2021)

Using Random Effects to Account for High-Cardinality Categorical Features and Repeated Measures in Deep Neural Networks

Implementation of popular bandit algorithms in batch environments.

Steer OpenAI's Jukebox with Music Taggers

MARS: Learning Modality-Agnostic Representation for Scalable Cross-media Retrieva

Housing Price Prediction