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.
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.
Class ã…¤ | Images ã…¤ | Objects ã…¤ | Count on image average | Area on image average |
---|---|---|---|---|
faceâž” mask | 22183 | 22183 | 1 | 9.56% |
noseâž” mask | 22006 | 22006 | 1 | 0.71% |
upper_lipâž” mask | 21583 | 21583 | 1 | 0.15% |
underlipâž” mask | 21464 | 21464 | 1 | 0.22% |
hairâž” mask | 21334 | 21334 | 1 | 7.3% |
left_eyebrowâž” mask | 21072 | 21072 | 1 | 0.16% |
right_eyebrowâž” mask | 21045 | 21045 | 1 | 0.15% |
right_eyeâž” mask | 20766 | 20766 | 1 | 0.08% |
left_eyeâž” mask | 20714 | 20714 | 1 | 0.08% |
tongueâž” 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.
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.
Object ID ã…¤ | Class ã…¤ | Image name click row to open | Image size height x width | Height ã…¤ | Height ã…¤ | Width ã…¤ | Width ã…¤ | Area ã…¤ |
---|---|---|---|---|---|---|---|---|
1âž” | face mask | 0993_0007.jpg | 3840 x 2160 | 1065px | 27.73% | 632px | 29.26% | 5.46% |
2âž” | left_eyebrow mask | 0993_0007.jpg | 3840 x 2160 | 50px | 1.3% | 125px | 5.79% | 0.05% |
3âž” | right_eyebrow mask | 0993_0007.jpg | 3840 x 2160 | 101px | 2.63% | 252px | 11.67% | 0.11% |
4âž” | left_eye mask | 0993_0007.jpg | 3840 x 2160 | 45px | 1.17% | 158px | 7.31% | 0.05% |
5âž” | right_eye mask | 0993_0007.jpg | 3840 x 2160 | 39px | 1.02% | 150px | 6.94% | 0.05% |
6âž” | nose mask | 0993_0007.jpg | 3840 x 2160 | 272px | 7.08% | 212px | 9.81% | 0.47% |
7âž” | underlip mask | 0993_0007.jpg | 3840 x 2160 | 119px | 3.1% | 289px | 13.38% | 0.14% |
8âž” | upper_lip mask | 0993_0007.jpg | 3840 x 2160 | 68px | 1.77% | 284px | 13.15% | 0.07% |
9âž” | hair mask | 0993_0007.jpg | 3840 x 2160 | 2665px | 69.4% | 1338px | 61.94% | 16.72% |
10âž” | 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.
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}
}
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 = { dec },
note = { visited on 2024-12-07 },
}
Download #
Please visit dataset homepage to download the data.
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