Multi-Class Face Segmentation

Summary #

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.

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.

Sample annotation mask from Multi-Class Face Segmentation

Sample 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.

Rows 1-10 of 17

Class ㅤ	Images ㅤ	Objects ㅤ	Count on image average	Area on image average
face➔ mask	22183	22183	1	9.56%
nose➔ mask	22006	22006	1	0.71%
upper_lip➔ mask	21583	21583	1	0.15%
underlip➔ mask	21464	21464	1	0.22%
hair➔ mask	21334	21334	1	7.3%
left_eyebrow➔ mask	21072	21072	1	0.16%
right_eyebrow➔ mask	21045	21045	1	0.15%
right_eye➔ mask	20766	20766	1	0.08%
left_eye➔ mask	20714	20714	1	0.08%
tongue➔ 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.

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.

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.

Rows 1-10 of 98542

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➔	face mask	0993_0007.jpg	3840 x 2160	1065px	27.73%	632px	29.26%	5.46%
2➔	left_eyebrow mask	0993_0007.jpg	3840 x 2160	50px	1.3%	125px	5.79%	0.05%
3➔	right_eyebrow mask	0993_0007.jpg	3840 x 2160	101px	2.63%	252px	11.67%	0.11%
4➔	left_eye mask	0993_0007.jpg	3840 x 2160	45px	1.17%	158px	7.31%	0.05%
5➔	right_eye mask	0993_0007.jpg	3840 x 2160	39px	1.02%	150px	6.94%	0.05%
6➔	nose mask	0993_0007.jpg	3840 x 2160	272px	7.08%	212px	9.81%	0.47%
7➔	underlip mask	0993_0007.jpg	3840 x 2160	119px	3.1%	289px	13.38%	0.14%
8➔	upper_lip mask	0993_0007.jpg	3840 x 2160	68px	1.77%	284px	13.15%	0.07%
9➔	hair mask	0993_0007.jpg	3840 x 2160	2665px	69.4%	1338px	61.94%	16.72%
10➔	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 = { jul },
  note = { visited on 2024-07-27 },
}

Download #

Please visit dataset homepage to download the data.

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