Gesture v1.0 - Dataset Ninja

Introduction #

Zeng Yifu

The Gesture v1.0 dataset comprises 2,417 images, which collectively feature a diverse set of 4,159 labeled objects categorized into five distinct gesture classes: one, two, three, four, and five. To facilitate comprehensive model training and evaluation, the dataset has been thoughtfully divided into two subsets: a substantial train set containing 1,916 images and a val set with 501 images. With a strong focus on object detection and a commitment to open-source principles, the Gesture dataset serves as a robust asset for advancing research and applications in deep learning within this domain.

The dataset was collected by opencv-webcam-script v0.5, capturing every 5 frames, 5 categories, from number 1 to number 5. a total of 2500 pictures, and only took 13.55 minutes. The dataset is labeled with label-studio.

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Summary #

Gesture v1.0 is a dataset for an object detection task. Possible applications of the dataset could be in the entertainment industry.

The dataset consists of 2417 images with 4159 labeled objects belonging to 5 different classes including one, two, three, and other: four and five.

Images in the Gesture v1.0 dataset have bounding box annotations. All images are labeled (i.e. with annotations). There are 2 splits in the dataset: train (1916 images) and val (501 images). The dataset was released in 2022.

Explore #

Gesture v1.0 dataset has 2417 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 Gesture v1.0

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Have a look at 2417 images

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

Class balance #

There are 5 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 1-5 of 5

Class ㅤ	Images ㅤ	Objects ㅤ	Count on image average	Area on image average
one➔ rectangle	495	894	1.81	29.29%
two➔ rectangle	487	804	1.65	34.9%
three➔ rectangle	484	837	1.73	37.99%
four➔ rectangle	480	807	1.68	32.31%
five➔ rectangle	471	817	1.73	31.5%

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 1-5 of 5

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
one rectangle	894	16.55%	32.32%	5.15%	107px	22.29%	386px	80.42%	256px	53.36%	92px	14.38%	388px	60.62%
three rectangle	837	22.6%	34.94%	11.43%	152px	31.67%	406px	84.58%	270px	56.26%	130px	20.31%	403px	62.97%
five rectangle	817	18.46%	42.71%	0%	2px	0.42%	394px	82.08%	244px	50.83%	4px	0.62%	333px	52.03%
four rectangle	807	19.44%	38.04%	10.38%	117px	24.38%	386px	80.42%	251px	52.34%	154px	24.06%	413px	64.53%
two rectangle	804	21.88%	40.56%	0.01%	3px	0.62%	395px	82.29%	288px	60.09%	10px	1.56%	435px	67.97%

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 4159 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 4159

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➔	five rectangle	gesture-f849ebd2-frame-1345.jpg	480 x 640	214px	44.58%	199px	31.09%	13.86%
2➔	five rectangle	gesture-f849ebd2-frame-1345.jpg	480 x 640	225px	46.88%	206px	32.19%	15.09%
3➔	five rectangle	gesture-6dd61f18-frame-1720.jpg	480 x 640	230px	47.92%	207px	32.34%	15.5%
4➔	five rectangle	gesture-6dd61f18-frame-1720.jpg	480 x 640	243px	50.62%	185px	28.91%	14.63%
5➔	five rectangle	gesture-1126c96b-frame-2130.jpg	480 x 640	276px	57.5%	189px	29.53%	16.98%
6➔	five rectangle	gesture-1126c96b-frame-2130.jpg	480 x 640	284px	59.17%	212px	33.12%	19.6%
7➔	five rectangle	gesture-11151648-frame-2050.jpg	480 x 640	187px	38.96%	241px	37.66%	14.67%
8➔	five rectangle	gesture-11151648-frame-2050.jpg	480 x 640	261px	54.38%	212px	33.12%	18.01%
9➔	four rectangle	gesture-6c47a721-frame-2405.jpg	480 x 640	187px	38.96%	306px	47.81%	18.63%
10➔	four rectangle	gesture-6c47a721-frame-2405.jpg	480 x 640	203px	42.29%	277px	43.28%	18.3%

License #

Gesture v1.0 is under GNU GPL 3.0 license.

Source

Citation #

If you make use of the Gesture v1.0 data, please cite the following reference:

@dataset{Gesture v1.0,
  author={Zeng Yifu},
  title={Gesture v1.0},
  year={2022},
  url={https://www.kaggle.com/datasets/zenggis/gesture-v1}
}

Source

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

@misc{ visualization-tools-for-gesture-v1-dataset,
  title = { Visualization Tools for Gesture v1.0 Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/gesture-v1 } },
  url = { https://datasetninja.com/gesture-v1 },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { dec },
  note = { visited on 2024-12-07 },
}

Download #

Dataset Gesture v1.0 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='Gesture v1.0', 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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