Face Mask Detection - Dataset Ninja

Introduction #

The Face Mask Detection dataset offers the potential to develop a model for identifying individuals who are correctly wearing masks, those without masks, and those wearing masks improperly. Face masks play a vital role in safeguarding individuals’ health, especially against respiratory diseases. In the context of COVID-19 and in the absence of widespread immunization, wearing masks is one of the primary preventive measures.

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Summary #

Face Mask Detection is a dataset for an object detection task. Possible applications of the dataset could be in the safety and surveillance industries.

The dataset consists of 853 images with 4072 labeled objects belonging to 3 different classes including with_mask, without_mask, and mask_weared_incorrect.

Images in the Face Mask Detection dataset have bounding box annotations. All images are labeled (i.e. with annotations). There are no pre-defined train/val/test splits in the dataset. The dataset was released in 2020.

Explore #

Face Mask Detection dataset has 853 images. Click on one of the examples below or open "Explore" tool anytime you need to view dataset images with annotations. This tool has extended visualization capabilities like zoom, translation, objects table, custom filters and more. Hover the mouse over the images to hide or show annotations.

Sample annotation mask from Face Mask Detection

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Have a look at 853 images

View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 3 annotation classes in the dataset. Find the general statistics and balances for every class in the table below. Click any row to preview images that have labels of the selected class. Sort by column to find the most rare or prevalent classes.

Rows 1-3 of 3

Class ㅤ	Images ㅤ	Objects ㅤ	Count on image average	Area on image average
with_mask➔ rectangle	768	3232	4.21	7.68%
without_mask➔ rectangle	286	717	2.51	3.45%
mask_weared_incorrect➔ rectangle	97	123	1.27	3.09%

Co-occurrence matrix #

Co-occurrence matrix is an extremely valuable tool that shows you the images for every pair of classes: how many images have objects of both classes at the same time. If you click any cell, you will see those images. We added the tooltip with an explanation for every cell for your convenience, just hover the mouse over a cell to preview the description.

Images #

Explore every single image in the dataset with respect to the number of annotations of each class it has. Click a row to preview selected image. Sort by any column to find anomalies and edge cases. Use horizontal scroll if the table has many columns for a large number of classes in the dataset.

Object distribution #

Interactive heatmap chart for every class with object distribution shows how many images are in the dataset with a certain number of objects of a specific class. Users can click cell and see the list of all corresponding images.

Class sizes #

The table below gives various size properties of objects for every class. Click a row to see the image with annotations of the selected class. Sort columns to find classes with the smallest or largest objects or understand the size differences between classes.

Rows 1-3 of 3

Class	Object count	Avg area	Max area	Min area	Min height	Min height	Max height	Max height	Avg height	Avg height	Min width	Min width	Max width	Max width
with_mask rectangle	3232	1.83%	42.99%	0.01%	3px	1.33%	341px	85.46%	37px	13.11%	2px	0.5%	318px	63.6%
without_mask rectangle	717	1.38%	20.17%	0.01%	4px	1.78%	163px	44%	31px	10.84%	3px	0.75%	149px	49.5%
mask_weared_incorrect rectangle	123	2.44%	30.78%	0.04%	6px	2.67%	198px	54.67%	44px	14.95%	6px	1.5%	171px	62.18%

Spatial Heatmap #

The heatmaps below give the spatial distributions of all objects for every class. These visualizations provide insights into the most probable and rare object locations on the image. It helps analyze objects' placements in a dataset.

Objects #

Table contains all 4072 objects. Click a row to preview an image with annotations, and use search or pagination to navigate. Sort columns to find outliers in the dataset.

Rows 1-10 of 4072

Object ID ㅤ	Class ㅤ	Image name click row to open	Image size height x width	Height ㅤ	Height ㅤ	Width ㅤ	Width ㅤ	Area ㅤ
1➔	with_mask rectangle	maksssksksss700.png	400 x 225	37px	9.25%	34px	15.11%	1.4%
2➔	with_mask rectangle	maksssksksss700.png	400 x 225	36px	9%	30px	13.33%	1.2%
3➔	without_mask rectangle	maksssksksss455.png	400 x 301	152px	38%	111px	36.88%	14.01%
4➔	without_mask rectangle	maksssksksss473.png	203 x 400	45px	22.17%	39px	9.75%	2.16%
5➔	with_mask rectangle	maksssksksss473.png	203 x 400	35px	17.24%	35px	8.75%	1.51%
6➔	with_mask rectangle	maksssksksss473.png	203 x 400	40px	19.7%	33px	8.25%	1.63%
7➔	with_mask rectangle	maksssksksss473.png	203 x 400	38px	18.72%	33px	8.25%	1.54%
8➔	with_mask rectangle	maksssksksss774.png	226 x 400	41px	18.14%	39px	9.75%	1.77%
9➔	with_mask rectangle	maksssksksss774.png	226 x 400	59px	26.11%	52px	13%	3.39%
10➔	with_mask rectangle	maksssksksss774.png	226 x 400	57px	25.22%	45px	11.25%	2.84%

License #

Face Mask Detection is under CC0 1.0 license.

Source

Citation #

If you make use of the Face Mask Detection data, please cite the following reference:

@dataset{Face Mask Detection,
	author={Andrew Maranhão},
	title={Face Mask Detection},
	year={2020},
	url={https://www.kaggle.com/datasets/andrewmvd/face-mask-detection}
}

Source

If you are happy with Dataset Ninja and use provided visualizations and tools in your work, please cite us:

@misc{ visualization-tools-for-face-mask-detection-dataset,
  title = { Visualization Tools for Face Mask Detection Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/face-mask-detection } },
  url = { https://datasetninja.com/face-mask-detection },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { nov },
  note = { visited on 2024-11-21 },
}

Download #

Dataset Face Mask Detection can be downloaded in Supervisely format:

As an alternative, it can be downloaded with dataset-tools package:

pip install --upgrade dataset-tools

… using following python code:

import dataset_tools as dtools

dtools.download(dataset='Face Mask Detection', dst_dir='~/dataset-ninja/')

Make sure not to overlook the python code example available on the Supervisely Developer Portal. It will give you a clear idea of how to effortlessly work with the downloaded dataset.

The data in original format can be downloaded here.

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Disclaimer #

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