Face-Mask-Detection-CNN

KO | EN

gitlite — search

#python #typescript #go #android #machine-learning #docker #nodejs #macos #awesome #cli #awesome-list #vue #security #ios

Face-Mask-Detection-CNN

(★ 18)

This Deep Learning model is trained in such a way that detects people wearing mask or not, even in heavy crowded places

Face-Mask-Detection-CNN 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# "Face Mask Detection with CNN (Convolutional Neural Networks)"
<p align="left"> <img src="https://www.logmask.com/images/mask-detection-sample.jpg" height="350px" /> </p>

### Problem Statement and Business Case:-
As Predicted by scientists, many new deadly diseases will be born in the next 10 years. Just like Coronavirus disease (COVID-19), the best thing to do with these diseases is to take precaution, use sanitizer and use face masks.Wearing a face mask will help prevent the spread of infection and prevent the individual from contracting any airborne infectious germs. When someone coughs, talks, sneezes they could release germs into the air that may infect others.

But the main problem is that people dont take seriously, that how important the mask is. So, to deal with such a problem, A Machine Learning Model has been made which detects people wearing mask or not within seconds, even in the highly crowded public places. Nowadays, wearing a mask is a must, so in every organization, As camera is placed in every organization,  so the model will flag people who didnt wear mask and will give red signal for culprits. and allow people who wears mask.

### Pre-Requisites / Technologies Used:-
- Python Programming Language (Intermediate), Deep Learning basic knowledge of CNN and Machine Learning
- Libraries: NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn, OpenCV and tensorflow

### Step_1: Data Pre-Processing of model :-
- Creating a dataset of 500 images of each with and without mask.
- Data Cleaning and transformation of data to similar size of 100x100 px.
- Converting BGR [Blue Green Red] to Grayscale method [Black & White]
- Visualizing data using graphs like histogram, count-plot, etc.

### Step_2: Training the Model:-
- We train the Model using CNN Convolutional Neural Network to build the model
- Used pooling layer, conv2d and other sequential layers to train the model with 64 neurons.
- Then performing hyper-parameter tuning to the model.

### Step_3: Testing and Measuring the performance of the model:-
- Splitting the dataset into three parts: Training (80% data) and testing (20% data)
- Now, train the model using CNN Deep Learning Model
- Achived Accuracy of 0.94 (94%) on testing dataset

#### **Show 💗 by ⭐ My Repository**

Face-Mask-Detection-CNN

Face-Mask-Detection-CNN 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Face-Mask-Detection-CNN

Face-Mask-Detection-CNN 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README