Data Usability

Predictly helps in providing easy-to-use data usage. It also supports providing datasets including data augmentation techniques, multiple export formats, consumption through REST API etc.

Precise Prediction

A quality-labelled dataset helps in creating a path where you can build a model which provides better output results and get rid of Gargabe In Garbage Out cases. Accurate data labelling ensures better quality assurance within machine learning algorithms, allowing the model to train and yield the expected output.

Save Time

You don’t have to spend days to collect the right type of data and then spend months just label them. Predictly helps in collecting the right type of data for your project and provide you with the annotated data within a much lesser time period.

According to the World Health Organization( as of July 12, 2020 report), the current explosion of COVID-19 has affected over 13,039,853 people and more than 571,659 deaths in more than 200 countries worldwide. It has increased quickly globally, bringing massive health, economic, environmental, and social challenges to the entire human population. COVID-19 is a disease that reaches from human to human, which can be controlled by ensuring a facial mask’s proper use. For public protection and health, WHO suggested people should wear face masks to avoid the risk of virus transmission, and a social distance of at least 2m be kept between individuals to prevent the person-to-person spread of disease.

Several precautionary actions have been taken to overcome this disease’s spread was wearing a mask is one of them. Amongst them, washing hands, managing a safe distance, wearing a mask, avoiding touching eyes, nose, and mouth are the main; here, wearing a mask is the simplest one. But checking whether everyone wears a mask or not and if a person properly wears a mask is not that easy. Therefore, many industrialized countries need a system that should automate the warning process of a person when anyone violates the rules like not wearing a mask, not properly wearing a mask, etc. That automated system is a face mask detection system.

Face mask detection dataset is created by collecting several images of people who are not wearing masks, wearing masks of different masks by extracting them from different sources or machines (cameras) of different resolutions. After that, our annotation team annotated those images with two different labels, such as mask, no mask. The main goal of this dataset is to create an AI-based system that will alert a person if a person is not wearing the mask.


Usage Methods

  • E-mail
  • Cloud Storage
  • Cloud Bucket

Dataset Export Formats

  • Pascal VOC format
  • YOLO Format
  • Tensorflow Records Format
  • COCO Format

Use Cases



  • Before Covid, hospitals or healthcare industries used face masks most of the time when they are going to do an operation in the operating room. But during and after the pandemic situation, wearing a protective face mask is a must. It should not only be for hospital staff and every person who is going to check and admit to the hospital. So at this time, hospital staff are integrating a face mask detection system to reduce the extra burden on workers and provide a more secure place for everyone.

  • This face mask detection system will monitor every hospital staff to find out whether the staff is wearing a mask during their respective shifts or not. If any medical staff who are not wearing masks are located, they will get a notification with a reminder note to wear a mask. Similarly, if any person except the medical staff, the system can pay concentration and detect the presence of the mask and automatically send a warning notification or report to the authorities.



  • With the discharge of the COVID-19 pandemic, there has been an increase in panic among people about the safety of their loved ones. As a result, homeowners Associations (HoA) and other residential apartment communities have been put under a spotlight to take necessary actions to secure their premises to reduce the spread of this virus. Maintaining social distancing, not crowding, and wearing masks inside residential complexes are the most prominent guidelines. So to automate the COVID compliance monitoring process, Homeowners Associations (HoA) and other residential apartment communities start integrating face mask detection systems into their surveillance cameras. This system will send warning/alert messages to respective people or supervisors if a person is found without wearing a face mask.


Shopping Malls and retail shops

  • For shopping malls and retail shops authorities, it is difficult to verify that every staff and shopper wears a mask or not in their entire shopping time. Many malls place security at the main entrance to check the people, but that is now not enough to reduce Covid-19 virus transmission. Retail and shopping mall managers need to monitor their premises to control the current occupancy and wearing of masks. So respective authorities adopt face mask detection systems to Maintain a delightful customer experience and promote proper behaviour among their staff and personalized alerts issued to employees who are not wearing their masks.


Airports and railway stations

  • Airports and railway stations authorities are integrating face mask detection systems into their existing CCTV cameras to detect whether travellers wear masks or not. The system will capture and store the passengers’ facial data at the entrance of airports or railway stations. If any passenger doesn’t wear a mask, the system will send passengers photos to the respective authorities. In addition, if a person’s face is recognized as a staff of the airport or railway station, then an alert message will be sent to the respective staff’s phone and authorities.



  • Offices can utilize The Face Mask Detection System at office premises to detect if employees keep safety standards at work. It observes employees without masks and sends them a warning to wear a mask. The reports can be downloaded or mailed at the end of the day to take people who are not complying with the commands or the requirements.

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