Full Body TikTok Dancing

Introduction #

Roman Kucev

The Segmentation Full Body TikTok Dancing Dataset includes 2615 images of a segmented dancing people. Videos of people dancing from TikTok were dowloaded and cut into frames. On each frame, all the dancing people were selected in Photoshop.

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Summary #

Segmentation Full Body TikTok Dancing Dataset is a dataset for instance segmentation, semantic segmentation, and object detection tasks. It is applicable or relevant across various domains.

The dataset consists of 2615 images with 2809 labeled objects belonging to 1 single class (dancing person).

Images in the Full Body TikTok Dancing dataset have pixel-level instance segmentation annotations. Due to the nature of the instance segmentation task, it can be automatically transformed into a semantic segmentation (only one mask for every class) or object detection (bounding boxes for every object) tasks. 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 2021.

Explore #

Full Body TikTok Dancing dataset has 2615 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 Full Body TikTok Dancing

Sample image from Full Body TikTok Dancing

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Have a look at 2615 images

View images along with annotations and tags, search and filter by various parameters

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.

Rows 1-1 of 1

Class ㅤ	Images ㅤ	Objects ㅤ	Count on image average	Area on image average
dancing person➔ mask	2615	2809	1.07	9.68%

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 1-1 of 1

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
dancing person mask	2809	9.01%	66.62%	0%	1px	0.1%	960px	100%	498px	52.3%	1px	0.17%	570px	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 2809 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 2809

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➔	dancing person mask	72_00420.png	960 x 540	462px	48.12%	133px	24.63%	6.28%
2➔	dancing person mask	38_00270.png	960 x 540	734px	76.46%	366px	67.78%	15.19%
3➔	dancing person mask	572_00030.png	960 x 540	355px	36.98%	108px	20%	3.96%
4➔	dancing person mask	477_00030.png	960 x 540	463px	48.23%	179px	33.15%	6.06%
5➔	dancing person mask	429_00270.png	960 x 540	516px	53.75%	184px	34.07%	8.63%
6➔	dancing person mask	409_00270.png	960 x 540	660px	68.75%	294px	54.44%	15.76%
7➔	dancing person mask	11_00240.png	960 x 540	489px	50.94%	179px	33.15%	7.97%
8➔	dancing person mask	603_00270.png	800 x 448	340px	42.5%	165px	36.83%	5.82%
9➔	dancing person mask	401_00150.png	960 x 540	553px	57.6%	157px	29.07%	8.63%
10➔	dancing person mask	143_00150.png	960 x 540	253px	26.35%	119px	22.04%	2.55%

License #

Segmentation Full Body TikTok Dancing Dataset is under CC BY-NC-ND 4.0 license.

Source

Citation #

If you make use of the Full Body TikTok Dancing data, please cite the following reference:

@dataset{Full Body TikTok Dancing,
	author={Talha Anwar and Kucev Roman and Kaggle Kerneler},
	title={Segmentation Full Body TikTok Dancing Dataset},
	year={2021},
	url={https://www.kaggle.com/datasets/tapakah68/segmentation-full-body-tiktok-dancing-dataset}
}

Source

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

@misc{ visualization-tools-for-full-body-tiktok-dancing-dataset-dataset,
  title = { Visualization Tools for Full Body TikTok Dancing Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/full-body-tiktok-dancing-dataset } },
  url = { https://datasetninja.com/full-body-tiktok-dancing-dataset },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { nov },
  note = { visited on 2024-11-21 },
}

Download #

Dataset Full Body TikTok Dancing 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='Full Body TikTok Dancing', 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.

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