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RuCode Hand Segmentation 2021 Dataset

2602661741
Tagentertainment
Tasksemantic segmentation
Release YearMade in 2021
Licenseunknown

Introduction #

Tatiana Gaintseva

RuCode Hand Segmentation 2021 Dataset is a task from a Competition on semantic segmentation of hands that display various numbers from the ASL hard alphabet.

Dataset LinkHomepageDataset LinkKaggle

Summary #

RuCode Hand Segmentation 2021 is a dataset for a semantic segmentation task. Possible applications of the dataset could be in the entertainment industry.

The dataset consists of 26026 images with 124920 labeled objects belonging to 6 different classes including palm, thumb_finger, pointer_finger, and other: middle_finger, fourth_finger, and little_finger.

Images in the RuCode Hand Segmentation 2021 dataset have pixel-level semantic segmentation annotations. There are 5206 (20% of the total) unlabeled images (i.e. without annotations). There are 2 splits in the dataset: train (20820 images) and test (5206 images). Also dataset includes letter tags. The dataset was released in 2021.

Dataset Poster

Explore #

RuCode Hand Segmentation 2021 dataset has 26026 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 RuCode Hand Segmentation 2021Sample image from RuCode Hand Segmentation 2021
OpenSample annotation mask from RuCode Hand Segmentation 2021Sample image from RuCode Hand Segmentation 2021
OpenSample annotation mask from RuCode Hand Segmentation 2021Sample image from RuCode Hand Segmentation 2021
OpenSample annotation mask from RuCode Hand Segmentation 2021Sample image from RuCode Hand Segmentation 2021
OpenSample annotation mask from RuCode Hand Segmentation 2021Sample image from RuCode Hand Segmentation 2021
OpenSample annotation mask from RuCode Hand Segmentation 2021Sample image from RuCode Hand Segmentation 2021
OpenSample annotation mask from RuCode Hand Segmentation 2021Sample image from RuCode Hand Segmentation 2021
OpenSample annotation mask from RuCode Hand Segmentation 2021Sample image from RuCode Hand Segmentation 2021
OpenSample annotation mask from RuCode Hand Segmentation 2021Sample image from RuCode Hand Segmentation 2021
OpenSample annotation mask from RuCode Hand Segmentation 2021Sample image from RuCode Hand Segmentation 2021
OpenSample annotation mask from RuCode Hand Segmentation 2021Sample image from RuCode Hand Segmentation 2021
OpenSample annotation mask from RuCode Hand Segmentation 2021Sample image from RuCode Hand Segmentation 2021
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Have a look at 26026 images
Because of dataset's license preview is limited to 12 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 6 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 1-6 of 6
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
thumb_fingerβž”
mask
20820
20820
1
2.96%
pointer_fingerβž”
mask
20820
20820
1
1.02%
palmβž”
mask
20820
20820
1
3.54%
middle_fingerβž”
mask
20820
20820
1
1.15%
little_fingerβž”
mask
20820
20820
1
0.88%
fourth_fingerβž”
mask
20820
20820
1
1.03%

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 1-6 of 6
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
thumb_finger
mask
20820
2.96%
5.92%
0.61%
48px
10%
282px
58.75%
125px
26.09%
47px
7.34%
259px
40.47%
pointer_finger
mask
20820
1.02%
2.16%
0.17%
25px
5.21%
183px
38.12%
73px
15.13%
34px
5.31%
189px
29.53%
palm
mask
20820
3.54%
7.69%
0.79%
82px
17.08%
241px
50.21%
152px
31.63%
109px
17.03%
256px
40%
middle_finger
mask
20820
1.15%
2.24%
0.29%
27px
5.62%
186px
38.75%
73px
15.13%
37px
5.78%
205px
32.03%
little_finger
mask
20820
0.88%
1.69%
0.19%
23px
4.79%
137px
28.54%
55px
11.48%
41px
6.41%
163px
25.47%
fourth_finger
mask
20820
1.03%
2.23%
0.28%
28px
5.83%
307px
63.96%
64px
13.41%
45px
7.03%
490px
76.56%

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 124920 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 99864
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”
palm
mask
e000095.png
480 x 640
111px
23.12%
154px
24.06%
1.41%
2βž”
thumb_finger
mask
e000095.png
480 x 640
111px
23.12%
97px
15.16%
2.08%
3βž”
pointer_finger
mask
e000095.png
480 x 640
78px
16.25%
87px
13.59%
0.93%
4βž”
middle_finger
mask
e000095.png
480 x 640
79px
16.46%
96px
15%
1.14%
5βž”
fourth_finger
mask
e000095.png
480 x 640
67px
13.96%
95px
14.84%
1%
6βž”
little_finger
mask
e000095.png
480 x 640
54px
11.25%
88px
13.75%
0.78%
7βž”
palm
mask
p000683.png
480 x 640
151px
31.46%
189px
29.53%
2.56%
8βž”
thumb_finger
mask
p000683.png
480 x 640
87px
18.12%
212px
33.12%
2.97%
9βž”
pointer_finger
mask
p000683.png
480 x 640
83px
17.29%
120px
18.75%
1.3%
10βž”
middle_finger
mask
p000683.png
480 x 640
49px
10.21%
83px
12.97%
0.9%

License #

License is unknown for the RuCode Hand Segmentation 2021 dataset.

Source

Citation #

If you make use of the RuCode Hand Segmentation 2021 data, please cite the following reference:

@dataset{RuCode Hand Segmentation,
  author={Tatiana Gaintseva},
  title={RuCode Hand Segmentation},
  year={2021},
  url={https://www.kaggle.com/competitions/rucode-hand-segmentation/}
}

Source

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

@misc{ visualization-tools-for-rucode-hand-segmentation-dataset,
  title = { Visualization Tools for RuCode Hand Segmentation 2021 Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/rucode-hand-segmentation-2021 } },
  url = { https://datasetninja.com/rucode-hand-segmentation-2021 },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { mar },
  note = { visited on 2024-03-05 },
}

Download #

Please visit dataset homepage to download the data.

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