https://github.com/aome510/mask-classifier
Detecting/classifying masked faces in real-time
https://github.com/aome510/mask-classifier
deep-learning keras machine-learing python
Last synced: about 1 year ago
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Detecting/classifying masked faces in real-time
- Host: GitHub
- URL: https://github.com/aome510/mask-classifier
- Owner: aome510
- Created: 2019-08-02T05:24:04.000Z (almost 7 years ago)
- Default Branch: master
- Last Pushed: 2019-08-24T14:54:16.000Z (almost 7 years ago)
- Last Synced: 2025-04-14T14:21:50.785Z (about 1 year ago)
- Topics: deep-learning, keras, machine-learing, python
- Language: Python
- Homepage:
- Size: 82.4 MB
- Stars: 36
- Watchers: 5
- Forks: 14
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
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README
# Mask Classifier
## Introduction
This repository contains codes for training and evaluating the Mask Classifier model, which is mostly implemented in ***Python 3*** and ***Keras*** framework.
When training and testing the model, we mostly use [SSH](https://github.com/mahyarnajibi/SSH) to detect and crop face images.
Please read our paper at [here](./paper/paper.pdf) for further details on preparing the datasets as well as training and testing the model.
This project was supported by [VinAI research](https://www.vinai.io).
## People
Our team consists of four members including me (***Thang Pham***), ***Bao Tran***, ***Duy Pham***, and ***Long Nguyen***.
This project was performed while the authors interned at [VinAI research](https://www.vinai.io/).
## Model Performance
Our team has tried different network architectures and trained on different frameworks such as ***PyTorch***, ***Keras***, and ***Caffe***.
We observed that deploying ***ResNet50*** pretrained network brought us high accuracy model as well as fast training time.
During the experiment, the Mask Classifer model can achieve about ***96.5%*** accuracy and a performance of ***10-15 FPS*** on a Nvidia's GeForce GTX Titan X machine.
### Some of the results
## Contents
1. [Installation](#install)
2. [Running the demo](#demo)
3. [Training a model](#train)
## Installation
1. Clone the repository:
```
git clone https://github.com/aome510/Mask-Classifier.git
```
2. Setup SSH:
* [Install and setup SSH requirements](./lib/SSH/README.md#installation)
* [Install SSH caffemodel](./lib/SSH/README.md#demo)
3. Install python requirements:
```
pip install -r requirements.txt
```
## Running the demo
To run the demo, first you need to download the pre-trained mask classifier model from [here](https://drive.google.com/file/d/1Ogew-mbiMqznzO3ok_T-IiqQGpq4OHw5) and then save it into a folder named ```model/```.
After downloading the model, you can run demo on webcam:
```
python demo.py
```
If you want to demo on videos, you can download our demo videos from [here](https://drive.google.com/drive/folders/1PSw1eDErcjLaOkIUxYXbLEacBNdaMjHQ) and then save it into a folder named ```data/videos/```. After that, you can modify ```demo.py``` to run demo on videos.
### Preparing the datasets
1. ***CelebA*** dataset:
You can download ***CelebA*** dataset from [here](https://www.kaggle.com/jessicali9530/celeba-dataset) and then save it into a folder named ```data/celebA/```
2. ***WiderFace*** dataset:
You can download ***WiderFace*** dataset from [here](https://drive.google.com/open?id=16a0TSvPI_3up7tbblxTHxXj5hZ9N7RyH) and then save it into a folder named ```data/WiderFace/```
3. ***MAFA*** dataset:
You can download ***MAFA*** dataset from [here](https://drive.google.com/drive/folders/1nbtM1n0--iZ3VVbNGhocxbnBGhMau_OG) and then save it into a folder named ```data/MAFA/```
4. ***Mask Classifier*** dataset (our dataset):
You can download ***Mask Classifier*** dataset from [here](https://drive.google.com/open?id=1WLKCLvR_nXSsOUywqKixoetz03WoJ8cL) and then save it into a folder name ```data/mask_classifier/```
### Combining the datasets
After downloading all the datasets listed above you can run:
```
python gen_data.py
```
to combine the datasets and split them for training and cross-validating the model.
### Training the model
After preparing the combined dataset, you can start training the model:
```
python train.py
```
By default, we use ***resnet50*** network to train the model. You can change to ***reception_resnet_v2*** network by modifying ```train.py```.
Please read [this](https://keras.io/applications/) if you want to train with diffrent network architectures.