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Face Mask Detection Dataset

85332257
Tagsafety, surveillance
Taskobject detection
Release YearMade in 2020
LicenseCC0 1.0
Download403 MB

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.

Dataset LinkHomepage

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.

Dataset Poster

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.

OpenSample annotation mask from Face Mask DetectionSample image from Face Mask Detection
OpenSample annotation mask from Face Mask DetectionSample image from Face Mask Detection
OpenSample annotation mask from Face Mask DetectionSample image from Face Mask Detection
OpenSample annotation mask from Face Mask DetectionSample image from Face Mask Detection
OpenSample annotation mask from Face Mask DetectionSample image from Face Mask Detection
OpenSample annotation mask from Face Mask DetectionSample image from Face Mask Detection
OpenSample annotation mask from Face Mask DetectionSample image from Face Mask Detection
OpenSample annotation mask from Face Mask DetectionSample image from Face Mask Detection
OpenSample annotation mask from Face Mask DetectionSample image from Face Mask Detection
OpenSample annotation mask from Face Mask DetectionSample image from Face Mask Detection
OpenSample annotation mask from Face Mask DetectionSample image from Face Mask Detection
OpenSample annotation mask from Face Mask DetectionSample image from Face Mask Detection
OpenSample annotation mask from Face Mask DetectionSample image from Face Mask Detection
OpenSample annotation mask from Face Mask DetectionSample image from Face Mask Detection
OpenSample annotation mask from Face Mask DetectionSample image from Face Mask Detection
OpenSample annotation mask from Face Mask DetectionSample image from Face Mask Detection
OpenSample annotation mask from Face Mask DetectionSample image from Face Mask Detection
OpenSample annotation mask from Face Mask DetectionSample image from Face Mask Detection
OpenSample annotation mask from Face Mask DetectionSample image from Face Mask Detection
OpenSample annotation mask from Face Mask DetectionSample image from Face Mask Detection
OpenSample annotation mask from Face Mask DetectionSample image from Face Mask Detection
OpenSample annotation mask from Face Mask DetectionSample image from Face Mask Detection
OpenSample annotation mask from Face Mask DetectionSample image from Face Mask Detection
OpenSample annotation mask from Face Mask DetectionSample image from Face Mask Detection
OpenSample annotation mask from Face Mask DetectionSample image from Face Mask Detection
OpenSample annotation mask from Face Mask DetectionSample image from Face Mask Detection
OpenSample annotation mask from Face Mask DetectionSample image from Face Mask Detection
OpenSample annotation mask from Face Mask DetectionSample image from Face Mask Detection
OpenSample annotation mask from Face Mask DetectionSample image from Face Mask Detection
OpenSample annotation mask from Face Mask DetectionSample image from Face Mask Detection
👀
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.

Search
Rows 1-3 of 3
Class
Images
Objects
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.

Search
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.

Spatial Heatmap

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.

Search
Rows 1-10 of 4072
Object ID
Class
Image name
click row to open
Image size
height x width
Height
Height
Width
Width
Area
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 = { mar },
  note = { visited on 2024-03-05 },
}

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.

. . .

Disclaimer #

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