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Gesture v1.0 Dataset

241751
Tagentertainment
Taskobject detection
Release YearMade in 2022
LicenseGNU GPL 3.0
Download175 MB

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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Dataset LinkHomepage

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.

Dataset Poster

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.

OpenSample annotation mask from Gesture v1.0Sample image from Gesture v1.0
OpenSample annotation mask from Gesture v1.0Sample image from Gesture v1.0
OpenSample annotation mask from Gesture v1.0Sample image from Gesture v1.0
OpenSample annotation mask from Gesture v1.0Sample image from Gesture v1.0
OpenSample annotation mask from Gesture v1.0Sample image from Gesture v1.0
OpenSample annotation mask from Gesture v1.0Sample image from Gesture v1.0
OpenSample annotation mask from Gesture v1.0Sample image from Gesture v1.0
OpenSample annotation mask from Gesture v1.0Sample image from Gesture v1.0
OpenSample annotation mask from Gesture v1.0Sample image from Gesture v1.0
OpenSample annotation mask from Gesture v1.0Sample image from Gesture v1.0
OpenSample annotation mask from Gesture v1.0Sample image from Gesture v1.0
OpenSample annotation mask from Gesture v1.0Sample image from Gesture v1.0
OpenSample annotation mask from Gesture v1.0Sample image from Gesture v1.0
OpenSample annotation mask from Gesture v1.0Sample image from Gesture v1.0
OpenSample annotation mask from Gesture v1.0Sample image from Gesture v1.0
OpenSample annotation mask from Gesture v1.0Sample image from Gesture v1.0
OpenSample annotation mask from Gesture v1.0Sample image from Gesture v1.0
OpenSample annotation mask from Gesture v1.0Sample image from Gesture v1.0
OpenSample annotation mask from Gesture v1.0Sample image from Gesture v1.0
OpenSample annotation mask from Gesture v1.0Sample image from Gesture v1.0
OpenSample annotation mask from Gesture v1.0Sample image from Gesture v1.0
OpenSample annotation mask from Gesture v1.0Sample image from Gesture v1.0
OpenSample annotation mask from Gesture v1.0Sample image from Gesture v1.0
OpenSample annotation mask from Gesture v1.0Sample image from Gesture v1.0
OpenSample annotation mask from Gesture v1.0Sample image from Gesture v1.0
OpenSample annotation mask from Gesture v1.0Sample image from Gesture v1.0
OpenSample annotation mask from Gesture v1.0Sample image from Gesture v1.0
OpenSample annotation mask from Gesture v1.0Sample image from Gesture v1.0
OpenSample annotation mask from Gesture v1.0Sample image from Gesture v1.0
OpenSample annotation mask from Gesture v1.0Sample image from Gesture v1.0
πŸ‘€
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.

Search
Rows 1-5 of 5
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
oneβž”
rectangle
495
894
1.81
29.29%
twoβž”
rectangle
487
804
1.65
34.9%
threeβž”
rectangle
484
837
1.73
37.99%
fourβž”
rectangle
480
807
1.68
32.31%
fiveβž”
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.

Search
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.

Spatial Heatmap

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.

Search
Rows 1-10 of 4159
Object ID
γ…€
Class
γ…€
Image name
click row to open
Image size
height x width
Height
γ…€
Height
γ…€
Width
γ…€
Width
γ…€
Area
γ…€
1βž”
five
rectangle
gesture-f849ebd2-frame-1345.jpg
480 x 640
214px
44.58%
199px
31.09%
13.86%
2βž”
five
rectangle
gesture-f849ebd2-frame-1345.jpg
480 x 640
225px
46.88%
206px
32.19%
15.09%
3βž”
five
rectangle
gesture-6dd61f18-frame-1720.jpg
480 x 640
230px
47.92%
207px
32.34%
15.5%
4βž”
five
rectangle
gesture-6dd61f18-frame-1720.jpg
480 x 640
243px
50.62%
185px
28.91%
14.63%
5βž”
five
rectangle
gesture-1126c96b-frame-2130.jpg
480 x 640
276px
57.5%
189px
29.53%
16.98%
6βž”
five
rectangle
gesture-1126c96b-frame-2130.jpg
480 x 640
284px
59.17%
212px
33.12%
19.6%
7βž”
five
rectangle
gesture-11151648-frame-2050.jpg
480 x 640
187px
38.96%
241px
37.66%
14.67%
8βž”
five
rectangle
gesture-11151648-frame-2050.jpg
480 x 640
261px
54.38%
212px
33.12%
18.01%
9βž”
four
rectangle
gesture-6c47a721-frame-2405.jpg
480 x 640
187px
38.96%
306px
47.81%
18.63%
10βž”
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 = { jun },
  note = { visited on 2024-06-25 },
}

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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