Summary #
HomepagePerson Segmentation is a dataset for a semantic segmentation task. Possible applications of the dataset could be in the surveillance industry.
The dataset consists of 21011 images with 21011 labeled objects belonging to 1 single class (person). There are 2 splits in the dataset: test (567 images) and train (20444 images). The dataset was released in 2022.
Explore #
Person Segmentation dataset has 21011 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 1 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 |
|---|---|---|---|---|
personâž” mask | 21011 | 21011 | 1 | 25.81% |
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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
person mask | 21011 | 25.81% | 99.61% | 0.08% | 33px | 5.16% | 640px | 100% | 489px | 76.38% | 13px | 2.03% | 640px | 100% |
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 21011 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” | person mask | pexels-photo-237593_0.png | 640 x 640 | 502px | 78.44% | 220px | 34.38% | 6.81% |
2âž” | person mask | pexels-photo-207789_0.png | 640 x 640 | 640px | 100% | 439px | 68.59% | 35.61% |
3âž” | person mask | pexels-photo-756453_0.png | 640 x 640 | 475px | 74.22% | 258px | 40.31% | 23.94% |
4âž” | person mask | ball-bauble-christmas-decoration-41516_0.png | 640 x 640 | 621px | 97.03% | 576px | 90% | 51.87% |
5âž” | person mask | pexels-photo-813544_0.png | 640 x 640 | 519px | 81.09% | 421px | 65.78% | 20.25% |
6âž” | person mask | pexels-photo-415274_0.png | 640 x 640 | 432px | 67.5% | 136px | 21.25% | 7.44% |
7âž” | person mask | pexels-photo-852943_0.png | 640 x 640 | 279px | 43.59% | 56px | 8.75% | 2.04% |
8âž” | person mask | beautiful-face-female-girl-41533_0.png | 640 x 640 | 617px | 96.41% | 488px | 76.25% | 56.61% |
9âž” | person mask | pexels-photo-848699_0.png | 640 x 640 | 527px | 82.34% | 167px | 26.09% | 10.04% |
10âž” | person mask | pexels-photo-799965_0.png | 640 x 640 | 500px | 78.12% | 594px | 92.81% | 47.53% |
License #
License is unknown for the Person Segmentation dataset.
Citation #
If you make use of the Person Segmentation data, please cite the following reference:
@dataset{Person Segmentation,
author={Rajan Ghimir},
title={Person Segmentation},
year={2022},
url={https://www.kaggle.com/datasets/rajanghimire/person-segmentation}
}
If you are happy with Dataset Ninja and use provided visualizations and tools in your work, please cite us:
@misc{ visualization-tools-for-person-segmentation-dataset,
title = { Visualization Tools for Person Segmentation Dataset },
type = { Computer Vision Tools },
author = { Dataset Ninja },
howpublished = { \url{ https://datasetninja.com/person-segmentation } },
url = { https://datasetninja.com/person-segmentation },
journal = { Dataset Ninja },
publisher = { Dataset Ninja },
year = { 2024 },
month = { may },
note = { visited on 2024-05-07 },
}Download #
Please visit dataset homepage to download the data.
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