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Multi-Class Face Segmentation Dataset

22188171
Tagsurveillance
Tasksemantic segmentation
Release YearMade in 2022
Licenseunknown

Summary #

Dataset LinkHomepage

Multi-Class Face Segmentation is a dataset for a semantic segmentation task. Possible applications of the dataset could be in the surveillance industry.

The dataset consists of 22188 images with 236935 labeled objects belonging to 17 different classes including face, nose, upper_lip, and other: underlip, hair, left_eyebrow, right_eyebrow, right_eye, left_eye, tongue, right_ear, left_ear, glasses, headdress, head, left_eyelashes, and right_eyelashes.

Images in the Multi-Class Face Segmentation dataset have pixel-level semantic segmentation annotations. All images are labeled (i.e. with annotations). There are 2 splits in the dataset: train (19535 images) and val (2653 images). The dataset was released in 2022.

Dataset Poster

Explore #

Multi-Class Face Segmentation dataset has 22188 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 Multi-Class Face SegmentationSample image from Multi-Class Face Segmentation
OpenSample annotation mask from Multi-Class Face SegmentationSample image from Multi-Class Face Segmentation
OpenSample annotation mask from Multi-Class Face SegmentationSample image from Multi-Class Face Segmentation
OpenSample annotation mask from Multi-Class Face SegmentationSample image from Multi-Class Face Segmentation
OpenSample annotation mask from Multi-Class Face SegmentationSample image from Multi-Class Face Segmentation
OpenSample annotation mask from Multi-Class Face SegmentationSample image from Multi-Class Face Segmentation
OpenSample annotation mask from Multi-Class Face SegmentationSample image from Multi-Class Face Segmentation
OpenSample annotation mask from Multi-Class Face SegmentationSample image from Multi-Class Face Segmentation
OpenSample annotation mask from Multi-Class Face SegmentationSample image from Multi-Class Face Segmentation
OpenSample annotation mask from Multi-Class Face SegmentationSample image from Multi-Class Face Segmentation
OpenSample annotation mask from Multi-Class Face SegmentationSample image from Multi-Class Face Segmentation
OpenSample annotation mask from Multi-Class Face SegmentationSample image from Multi-Class Face Segmentation
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Have a look at 22188 images
Because of dataset's license preview is limited to 12 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 17 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 1-10 of 17
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
faceβž”
mask
22183
22183
1
9.56%
noseβž”
mask
22006
22006
1
0.71%
upper_lipβž”
mask
21583
21583
1
0.15%
underlipβž”
mask
21464
21464
1
0.22%
hairβž”
mask
21334
21334
1
7.3%
left_eyebrowβž”
mask
21072
21072
1
0.16%
right_eyebrowβž”
mask
21045
21045
1
0.15%
right_eyeβž”
mask
20766
20766
1
0.08%
left_eyeβž”
mask
20714
20714
1
0.08%
tongueβž”
mask
12588
12588
1
0.21%

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 1-10 of 17
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
face
mask
22183
9.56%
76.86%
0.01%
27px
2.5%
2739px
100%
507px
47.23%
20px
1.51%
1833px
100%
nose
mask
22006
0.71%
25.15%
0%
7px
0.74%
1005px
57.59%
126px
11.72%
4px
0.16%
819px
75.83%
upper_lip
mask
21583
0.15%
5.76%
0%
1px
0.1%
333px
36.94%
39px
3.69%
1px
0.16%
912px
84.44%
underlip
mask
21464
0.22%
4.67%
0%
2px
0.21%
382px
34.44%
51px
4.79%
2px
0.1%
901px
83.43%
hair
mask
21334
7.3%
63.85%
0%
2px
0.1%
3798px
100%
548px
48.1%
2px
0.09%
3181px
100%
left_eyebrow
mask
21072
0.16%
2.65%
0%
1px
0.09%
325px
28.25%
40px
3.64%
1px
0.05%
925px
46.67%
right_eyebrow
mask
21045
0.15%
3.22%
0%
1px
0.08%
323px
39.38%
40px
3.7%
1px
0.08%
635px
43.8%
right_eye
mask
20766
0.08%
1.75%
0%
1px
0.05%
171px
30%
26px
2.46%
3px
0.16%
638px
30.65%
left_eye
mask
20714
0.08%
2.94%
0%
1px
0.09%
283px
39.31%
26px
2.44%
1px
0.05%
461px
36.02%
tongue
mask
12588
0.21%
6.22%
0%
1px
0.05%
436px
30.73%
36px
3.57%
1px
0.05%
712px
45%

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 98542 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 98542
Object ID
γ…€
Class
γ…€
Image name
click row to open
Image size
height x width
Height
γ…€
Height
γ…€
Width
γ…€
Width
γ…€
Area
γ…€
1βž”
face
mask
0993_0007.jpg
3840 x 2160
1065px
27.73%
632px
29.26%
5.46%
2βž”
left_eyebrow
mask
0993_0007.jpg
3840 x 2160
50px
1.3%
125px
5.79%
0.05%
3βž”
right_eyebrow
mask
0993_0007.jpg
3840 x 2160
101px
2.63%
252px
11.67%
0.11%
4βž”
left_eye
mask
0993_0007.jpg
3840 x 2160
45px
1.17%
158px
7.31%
0.05%
5βž”
right_eye
mask
0993_0007.jpg
3840 x 2160
39px
1.02%
150px
6.94%
0.05%
6βž”
nose
mask
0993_0007.jpg
3840 x 2160
272px
7.08%
212px
9.81%
0.47%
7βž”
underlip
mask
0993_0007.jpg
3840 x 2160
119px
3.1%
289px
13.38%
0.14%
8βž”
upper_lip
mask
0993_0007.jpg
3840 x 2160
68px
1.77%
284px
13.15%
0.07%
9βž”
hair
mask
0993_0007.jpg
3840 x 2160
2665px
69.4%
1338px
61.94%
16.72%
10βž”
right_ear
mask
0993_0007.jpg
3840 x 2160
290px
7.55%
74px
3.43%
0.13%

License #

License is unknown for the Multi-Class Face Segmentation dataset.

Source

Citation #

If you make use of the Multi-Class Face Segmentation data, please cite the following reference:

@dataset{Multi-Class Face Segmentation,
  author={Ashish Goswami},
  title={Multi-Class Face Segmentation},
  year={2022},
  url={https://www.kaggle.com/datasets/ashish2001/multiclass-face-segmentation}
}

Source

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

@misc{ visualization-tools-for-multi-class-face-segmentation-dataset,
  title = { Visualization Tools for Multi-Class Face Segmentation Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/multi-class-face-segmentation } },
  url = { https://datasetninja.com/multi-class-face-segmentation },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jun },
  note = { visited on 2024-06-25 },
}

Download #

Please visit dataset homepage to download the data.

. . .

Disclaimer #

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