---
title: "Medical Masks Dataset"
description: "The dataset includes annotated images of individuals wearing face masks in various positions, designed for face mask detection, object detection, and face recognition tasks, providing…"
url: "https://unidata.pro/datasets/medical-masks-image-dataset/"
date_modified: "2026-02-03T13:05:56+03:00"
language: "en-US"
---
The dataset includes annotated images of individuals wearing face masks in various positions, designed for face mask detection, object detection, and face recognition tasks, providing diverse training data to develop deep learning detection models for identifying mask usage and supporting detection systems

## Dataset Structure

### The Numbers Section

**Numbered list:**

- **Number:** 167, 880 — **Text:** Images
- **Number:** 41, 970 — **Text:** People

### Tooltips Section

**Tooltip items:**

- **Name:** Medicine
- **Name:** Computer Vision
- **Name:** Machine Learning
- **Name:** Facial Recognition

### Dataset Information

**Table with data:**

| Characteristic | Data |
| --- | --- |
| Description | Images of human faces wearing medical masks |
| Data types | Image |
| Tasks | Face Mask Detection, Computer Vision |
| Number of people | 41, 970 |
| Number of files in a set | 4 images per person |
| Total number of images | 167, 880 |
| Types of mask states | No mask, mask on chin, mask covering mouth, mask fully on |
| Labeling | Metadata (ID, gender, age, country) |
| Gender | Male, Female |
| Age | Min = 18, max = 72 |

**Media Slider:**

- **Image in the slider:** ![](https://unidata.pro/wp-content/uploads/2025/05/primer-foto1-scaled.webp)
- **Image in the slider:** ![](https://unidata.pro/wp-content/uploads/2025/05/primer-foto3-scaled.webp)
- **Image in the slider:** ![](https://unidata.pro/wp-content/uploads/2025/05/primer-foto4-scaled.webp)
- **Image in the slider:** ![](https://unidata.pro/wp-content/uploads/2025/05/primer-foto2-scaled.webp)

### LLM Languages

**Section Title:** Statistics

**List of Statistics:**

- **Filter by:** Age Distribution — **GIF image:** Age Distribution — **Table with data:**

| Age | Count |
| --- | --- |
| Under 18 | 729 |
| 19-25 | 18431 |
| 26-32 | 11900 |
| 33-39 | 6737 |
| 40-46 | 2713 |
| 47-53 | 966 |
| 54-59 | 296 |
| 60-66 | 133 |
| 67+ | 60 |
- **Filter by:** Top 20 Countries — **GIF image:** Top 20 Countries — **Table with data:**

| Country | COUNTA of country |
| --- | --- |
| RU | 15466 |
| PK | 4897 |
| IN | 2337 |
| PH | 1726 |
| KE | 1638 |
| NG | 1344 |
| BR | 1327 |
| TR | 1251 |
| BD | 1194 |
| KZ | 1018 |
| UA | 819 |
| US | 762 |
| ZA | 612 |
| BY | 610 |
| NP | 593 |
| GH | 445 |
| LK | 413 |
| VE | 392 |
| ID | 388 |
| ZM | 311 |

### Statistics - Charts

**Charts with Titles:**

- **Shortcode:** [ays_chart id='53'] — **caption above the graph:** Gender distribution
- **Shortcode:** [ays_chart id='54'] — **caption above the graph:** Continent distribution

### Technical Specifications

**Table with data:**

| Characteristic | Data |
| --- | --- |
| Image extension | jpg |

**Source and data collection methodology:** Source and collection methodology: Data was collected via crowdsourcing platforms.

### Dataset Use Cases - Slider

**Industry Cards:**

- **Industry:** Healthcare & Public Safety — **Title:** Medical Face Mask Detection in Hospitals — **Text:** Medical Masks Dataset provides face mask images of individuals wearing and not wearing protective masks. Hospitals and clinics use this dataset to train detection systems that monitor mask usage and ensure compliance with safety standards. Its detailed annotations help build reliable mask detectors for real-time applications.
- **Industry:** Surveillance & Security — **Title:** Automated Mask Usage Monitoring — **Text:** Security systems rely on the medical face mask detection dataset for developing models that identify people wearing or missing surgical masks in public spaces. Since the dataset consists of manually annotated data, it supports object detection and segmentation tasks, helping authorities deploy detection models with the highest accuracy in crowded environments.
- **Industry:** Computer Vision Research — **Title:** Improving Detection Models with Annotated Data — **Text:** Researchers use the medical masks recognition dataset to experiment with deep learning and learning methods for face recognition and mask detection tasks. The training set includes segmentation masks and instance segmentation data, making it a valuable benchmark dataset for evaluating proposed models in computer vision and medical imaging studies.
- **Industry:** AI & Technology Development — **Title:** Training Machine Learning Systems for Real-World Use — **Text:** The mask dataset supports machine learning teams working on detection models for smart cameras, mobile apps, and monitoring tools. This dataset comprising face mask images offers manually segmented annotations that speed up the training process. By using this data collection, developers build scalable detection tasks that achieve highest accuracy in deployment.

### Fact

**FAQs Heading:** FAQs

**List of Questions:**

- **Question:** What mask types are covered in Medical Masks Dataset? — **Answer:** It includes multiple mask states: no mask, mask on chin, mask covering only the mouth, and mask fully worn. These variations allow detection algorithms to distinguish between correct and incorrect mask usage.
- **Question:** How diverse is the dataset? — **Answer:** The dataset contains images of 41, 970 individuals aged 18 to 72, with both male and female participants across different countries. This diversity improves detection accuracy and ensures that models generalize across varied demographics.
- **Question:** Is the dataset appropriate for training real-time face mask detection models? — **Answer:** Yes. The dataset is suitable for building real-time face mask detection systems used in surveillance, workplace monitoring, transportation hubs, healthcare facilities, and other environments where rapid detection is required.
- **Question:** What types of annotations are provided? — **Answer:** This dataset includes metadata with ID, gender, age, and country for each image. These annotations support segmentation tasks, object detection, and deep learning approaches.
- **Question:** What should I consider before buying this dataset? — **Answer:** Before making a purchase, make sure the mask states, image resolutions, and metadata annotations align with your project’s needs. Consider whether the dataset’s demographic diversity and size are sufficient for your deep learning or computer vision tasks.
- **Question:** Do Unidata datasets follow GDPR or other data privacy regulations? — **Answer:** Yes. Each dataset respects GDPR rules and applicable data protection laws. All information is collected ethically and legally.
- **Question:** How are Unidata datasets stored? — **Answer:** At Unidata, datasets are housed in AWS’s secure and scalable cloud infrastructure. Our processes comply with ISO 27001 and ISO 27701, recognized worldwide for information security and privacy standards. This guarantees a protected, reliable, and standards-driven environment for managing sensitive data.
- **Question:** How are Unidata datasets licensed? — **Answer:** Unidata datasets follow the dual licensing model: sample data is free, while the full dataset is available after purchase.
- **Question:** How can this dataset reduce false positives in mask detection? — **Answer:** Including correctly worn masks as well as incorrectly positioned masks allows models to learn subtle visual differences between mask states. This helps improve the accuracy of computer vision models and reduces incorrect detections in real-world environments.

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