The dataset contains real images of people with face mask and without face mask. Each class contains 150 images.

Cite us:

Ferdib-Al-Islam, Suprio Sarkar, Nusrat Jahan and Farjana Yeasmin Rupa, “Face Mask Detection Dataset”. Zenodo, Jul. 15, 2021. doi: 10.5281/zenodo.5305989

Here are a few use cases for this project:

1. Workplace Safety: This model could be used to ensure safety regulations in hazardous workplaces (like construction sites or manufacturing plants), by constantly monitoring whether employees are wearing their essential protective gear like helmets and face masks.

2. Public Health Monitoring: The model could be deployed to surveil public spaces or track adherence to mask regulations during public health crises such as the COVID-19 pandemic.

3. Sports Analytics: The model could be used in analyses of sports matches since some sports require specific protective gear. It can identify whether players are following the rules and wearing the appropriate gear when on the field.

4. Traffic Monitoring: The model could be utilized by traffic enforcement agencies to identify whether riders/motorbike users are wearing helmets for ensuring road safety rules are adhered.

5. Entertainment Industry: Movie and entertainment companies can use this model during the production process to ensure that stuntmen or actors are following safety measures and wearing the necessary protective equipment while shooting potentially dangerous scenes.

From the site: Masks play a crucial role in protecting the health of individuals against respiratory diseases, as is one of the few precautions available for COVID-19 in the absence of immunization. With this dataset, it is possible to create a model to detect people wearing masks, not wearing them, or wearing masks improperly. This dataset contains 853 images belonging to the 3 classes, as well as their bounding boxes in the PASCAL VOC format. The classes are:

With mask; Without mask; Mask worn incorrectly.

Merged Face Mask Detection

## Overview

Merged Face Mask Detection is a dataset for object detection tasks - it contains Masks Face Masks2 annotations for 1,041 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Original datasets https://www.kaggle.com/datasets/ashishjangra27/face-mask-12k-images-dataset

In this project, trained COVID-19 face mask detector, we’ll proceed to detect COVID-19 face masks in images, we have a total of 1376 images with 690 images in the with mask and 686 images in the ‘without mask. In order to train a custom face mask detector, we need to break our project into two distinct phases: Training: Here we’ll focus on loading our face mask detection dataset from disk, training a model (using Keras/TensorFlow) on this dataset, and then serializing the face mask detector to disk Deployment: Once the face mask detector is trained, we can then move on to loading the mask detector, performing face detection, and then classifying each face as with_mask or without_mask.

Real-time face mask detecting system is required which will alert the people and helps in preventing the pandemic. Deep learning is subbranch of machine learning that works with algorithms that are inspired by the human brain. Deep learning offer image detection, image classification and Convolutional Neural Networks (CNN). CNN are mainly used in computer vision detection and classification tasks. In this project Deep learning techniques are used to differentiate faces wearing a mask and not wearing a mask. CNN are used to include the efficient number of Convolutional Neural Layers for accurate detection. OpenCV is a library of programming functions which mainly aimed at real time computer vision, Machine Learning and Image processing and used for detection of faces, objects, and handwritings. It plays a key role in detection of face with and without mask. The computer vision mainly aims at manipulating and retrieve data from a real time source. By summarizing we firstly create a CNN for detection of facial images and then employee Deep learning algorithms for detection of faces with and without masks using Tensor flow and Open CV libraries.

BAFMD contains images posted on Twitter during the pandemic from around the world with more images from underrepresented race and age groups to mitigate the problem for the face mask detection task.

HuangYiYang/Face-Mask-Detection-Dataset dataset hosted on Hugging Face and contributed by the HF Datasets community

Facemask_detections

## Overview

Facemask_detections is a dataset for object detection tasks - it contains Mask Detection annotations for 2,180 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Dataset

This dataset was created by Supun Kuruppu

Contents

Face Mask Detection System Market Outlook

The global face mask detection system market size is projected to grow from USD 1.2 billion in 2023 to USD 4.5 billion by 2032, at a compound annual growth rate (CAGR) of 15.6%. The significant growth in the market is driven by the increasing necessity for advanced safety measures in public and private spaces to manage and mitigate the spread of infectious diseases. This surge in demand is fueled by the continuous evolution of technology, which has made face mask detection systems more accurate, efficient, and accessible.

One of the primary growth factors for the face mask detection system market is the ongoing global health crises such as the COVID-19 pandemic, which have underscored the importance of preventative health measures in public and private spaces. Governments and organizations worldwide have been quick to adopt technologies that enforce health guidelines, making face mask detection systems an essential part of this strategy. The enforcement of mask mandates in public spaces, including transportation hubs, retail stores, and healthcare facilities, has significantly boosted the demand for these systems.

Technological advancements in artificial intelligence (AI) and machine learning (ML) are another major growth driver. AI-based face mask detection systems offer high accuracy and real-time monitoring capabilities, making them highly effective in identifying whether individuals are complying with mask-wearing protocols. These systems can be integrated with existing surveillance and access control systems, providing a seamless solution that enhances overall security and safety. The integration of AI has also enabled the development of smart cameras and IoT devices that can efficiently manage large crowds and high-traffic areas.

The increased focus on workplace safety and compliance is further propelling market growth. Corporate offices, manufacturing plants, and other work environments are increasingly investing in face mask detection systems to ensure the health and safety of their employees. These systems help in monitoring compliance with health guidelines, thereby reducing the risk of outbreaks within the workplace. Additionally, the implementation of such systems can also mitigate legal risks for businesses by demonstrating their commitment to employee safety.

From a regional perspective, North America is expected to dominate the face mask detection system market due to early adoption of advanced technologies and strong regulatory frameworks supporting public health initiatives. The Asia Pacific region is also anticipated to witness significant growth, driven by high population density, increasing awareness about health and safety, and rising technological adoption. Europe, Latin America, and the Middle East & Africa will also contribute to market expansion but at a comparatively moderate pace.

Component Analysis

The face mask detection system market comprises three primary components: hardware, software, and services. Hardware components include cameras, sensors, and other physical devices required for the detection systems. These are essential for capturing real-time data and are often integrated with existing surveillance infrastructure. High demand for advanced and durable hardware solutions is driving continuous innovation in this segment. Companies are focusing on developing smart cameras with higher resolution and better night vision capabilities to ensure accurate detection under various conditions.

Software forms the core of face mask detection systems, enabling the analysis and interpretation of data captured by hardware components. This segment includes AI and ML algorithms that recognize and verify mask compliance. The software segment is expected to grow at a significant pace, driven by the need for accurate and efficient solutions. Continuous advancements in AI and machine learning technologies are enabling the development of more sophisticated software solutions that offer high accuracy, faster processing times, and real-time monitoring capabilities. The ability to integrate these software solutions with existing security and surveillance systems is also a key growth driver.

The services segment includes installation, maintenance, and support services required for the deployment and operation of face mask detection systems. This segment is crucial for ensuring the seamless functioning and longevity of the systems. As the adoption of face mask detection systems increases, so does the demand for reliable and efficient services. Companies are focusing o

Proper Improper Face Mask Detection

## Overview

Proper Improper Face Mask Detection is a dataset for object detection tasks - it contains Face Mask annotations for 3,614 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Dataset Summary

A dataset from kaggle. origin: https://dphi.tech/challenges/data-sprint-76-human-activity-recognition/233/data

  Introduction

• PROBLEM STATEMENT
  

• About Files
  

Train - contains all the images that are to be used for training your model. In this folder you will find 15 folders namely - 'calling', ’clapping’, ’cycling’, ’dancing’, ‘drinking’, ‘eating’, ‘fighting’, ‘hugging’, ‘laughing’, ‘listeningtomusic’, ‘running’, ‘sitting’… See the full description on the dataset page: https://huggingface.co/datasets/Kai1014/facemask-kaggle.

The global face mask detection system market was valued at 1620 million in 2025, and is projected to reach 2680.2 million by 2033, growing at a CAGR of 4.9% from 2025 to 2033. The market growth is attributed to the increasing demand for facial recognition systems, the rising adoption of AI and machine learning technologies, and the growing concerns about public health and safety. The key market drivers include the mandatory use of face masks in public places to prevent the spread of infectious diseases, the increasing adoption of biometrics for security and surveillance purposes, and the growing awareness of the benefits of face mask detection systems. The market is segmented by application, type, and region. By application, the market is segmented into airports, hospitals, corporate offices, industries, sports venues, public transport, retail, hospitality, and others. By type, the market is segmented into technology, equipment, and other. By region, the market is segmented into North America, South America, Europe, Middle East & Africa, and Asia Pacific. North America is the largest market for face mask detection systems, followed by Europe and Asia Pacific. The growing demand for facial recognition systems in the transportation sector, the increasing adoption of AI and machine learning technologies, and the rising concerns about public health and safety are the key factors driving the market growth in these regions.

The global face mask detection system market size was valued at USD 1928.6 million in 2023 and is expected to expand at a compound annual growth rate (CAGR) of 15.7% from 2023 to 2033. The market value is expected to reach USD 7,147.9 million by 2033. Increasing concerns regarding the transmission of infectious diseases, such as COVID-19, have propelled the adoption of face mask detection systems. These systems play a crucial role in ensuring compliance with mask-wearing mandates and mitigating the spread of viruses. Key drivers of the market include the rising adoption of artificial intelligence (AI) and computer vision technologies for facial recognition and mask detection. Additionally, government regulations and mandates requiring the use of face masks in public spaces have contributed to the market growth. The increasing demand for these systems in various sectors, including healthcare, transportation, retail, and hospitality, is expected to drive the market further. Technological advancements and the integration of thermal imaging and temperature screening capabilities are also fueling market expansion.

Face Mask Detection 3

## Overview

Face Mask Detection 3 is a dataset for object detection tasks - it contains Face Mask ESVl 5OyM annotations for 4,326 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The global Face Mask Detection System market is forecasted to grow at a noteworthy CAGR of 15.73% between 2025 and 2033. By 2033, market size is expected to surge to USD 7.00 Billion, a substantial rise from the USD 1.88 Billion recorded in 2024.

The Global Face Mask Detection System market size to cross USD 7 Billion by 2033. [https://edison.valuemarketresearch.com//uploads/report_images/VMR1121

Dataset

This dataset was created by Kotagiri Krishna

Contents

Dataset

This dataset was created by sravan004

Contents

It contains the following files:

During the present time, COVID-19 situation is the topmost priority in our life. We are introducing a new dataset named Covid Face-Mask Monitoring Dataset which is based on Bangladesh perspective. We have a main concern to detect people who are using masks or not in the street. Furthermore, few people are not wearing masks properly which is harmful for other people and we have the intention to detect them also. Our proposed dataset contains 6,550 images and those images collected from the walking street, bus stop, street tea stall, foot-over bridge and so on. Among the full dataset, we selected 5,750 images for training purposes and 800 images for validation purposes. Our selected dimension is 1080 × 720 pixels for entire dataset. The percentage of validation data from the full dataset is almost 12.20%. We used a personal cell phone camera, DSLR for collecting frames and adding them into our final dataset. We have also planned to collect images from the mentioned place using an action camera or CCTV surveillance camera. But, from Bangladesh perspective it is not easy to collect clear and relevant data for research. To extend, CCTV surveillance cameras are mostly used in the university, shopping complex, hospital, school, college where using a mask is mandatory. But our goal of research is different. In addition, we want to mention that in our proposed dataset there are three classes which are 1. Mask, 2. No_mask, 3. Mask_not_in_position.