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Dentalai Dataset

249542057
Tagmedical
Taskinstance segmentation
Release YearMade in 2023
LicenseCC BY 4.0
Download1018 MB

Summary #

Dataset LinkHomepage

Dentalai Computer Vision Project is a dataset for instance segmentation, semantic segmentation, and object detection tasks. It is used in the medical industry.

The dataset consists of 2495 images with 28904 labeled objects belonging to 4 different classes including tooth, caries, cavity, and other: crack.

Images in the Dentalai 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 3 splits in the dataset: train (1991 images), valid (254 images), and test (250 images). The dataset was released in 2023.

Dataset Poster

Explore #

Dentalai dataset has 2495 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 DentalaiSample image from Dentalai
OpenSample annotation mask from DentalaiSample image from Dentalai
OpenSample annotation mask from DentalaiSample image from Dentalai
OpenSample annotation mask from DentalaiSample image from Dentalai
OpenSample annotation mask from DentalaiSample image from Dentalai
OpenSample annotation mask from DentalaiSample image from Dentalai
OpenSample annotation mask from DentalaiSample image from Dentalai
OpenSample annotation mask from DentalaiSample image from Dentalai
OpenSample annotation mask from DentalaiSample image from Dentalai
OpenSample annotation mask from DentalaiSample image from Dentalai
OpenSample annotation mask from DentalaiSample image from Dentalai
OpenSample annotation mask from DentalaiSample image from Dentalai
OpenSample annotation mask from DentalaiSample image from Dentalai
OpenSample annotation mask from DentalaiSample image from Dentalai
OpenSample annotation mask from DentalaiSample image from Dentalai
OpenSample annotation mask from DentalaiSample image from Dentalai
OpenSample annotation mask from DentalaiSample image from Dentalai
OpenSample annotation mask from DentalaiSample image from Dentalai
OpenSample annotation mask from DentalaiSample image from Dentalai
OpenSample annotation mask from DentalaiSample image from Dentalai
OpenSample annotation mask from DentalaiSample image from Dentalai
OpenSample annotation mask from DentalaiSample image from Dentalai
OpenSample annotation mask from DentalaiSample image from Dentalai
OpenSample annotation mask from DentalaiSample image from Dentalai
OpenSample annotation mask from DentalaiSample image from Dentalai
OpenSample annotation mask from DentalaiSample image from Dentalai
OpenSample annotation mask from DentalaiSample image from Dentalai
OpenSample annotation mask from DentalaiSample image from Dentalai
OpenSample annotation mask from DentalaiSample image from Dentalai
OpenSample annotation mask from DentalaiSample image from Dentalai
πŸ‘€
Have a look at 2495 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 4 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 1-4 of 4
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
toothβž”
polygon
2493
22731
9.12
26.38%
cariesβž”
polygon
1190
4212
3.54
0.91%
cavityβž”
polygon
999
1781
1.78
4.55%
crackβž”
polygon
154
180
1.17
1.43%

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 1-4 of 4
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
tooth
polygon
22731
2.85%
87.75%
0%
3px
0.42%
2306px
100%
329px
22.2%
4px
0.33%
2251px
100%
caries
polygon
4212
0.24%
62.2%
0%
2px
0.11%
1237px
89.3%
56px
5.25%
2px
0.09%
1107px
99.1%
cavity
polygon
1781
2.5%
47.54%
0%
6px
0.82%
1777px
98.85%
176px
19.05%
6px
0.54%
1760px
85.62%
crack
polygon
180
1.16%
29.96%
0.01%
6px
2.33%
917px
82.14%
139px
15.74%
4px
0.87%
1120px
63.17%

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 28904 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 28904
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”
tooth
polygon
2519_jpg.rf.2f3e774194124faa477bcac6086fc4f9.jpg
194 x 259
33px
17.01%
31px
11.97%
1.28%
2βž”
tooth
polygon
2519_jpg.rf.2f3e774194124faa477bcac6086fc4f9.jpg
194 x 259
33px
17.01%
23px
8.88%
1.02%
3βž”
tooth
polygon
2519_jpg.rf.2f3e774194124faa477bcac6086fc4f9.jpg
194 x 259
31px
15.98%
17px
6.56%
0.41%
4βž”
tooth
polygon
2519_jpg.rf.2f3e774194124faa477bcac6086fc4f9.jpg
194 x 259
38px
19.59%
29px
11.2%
1.59%
5βž”
tooth
polygon
2519_jpg.rf.2f3e774194124faa477bcac6086fc4f9.jpg
194 x 259
42px
21.65%
33px
12.74%
1.83%
6βž”
tooth
polygon
2519_jpg.rf.2f3e774194124faa477bcac6086fc4f9.jpg
194 x 259
27px
13.92%
16px
6.18%
0.6%
7βž”
tooth
polygon
2519_jpg.rf.2f3e774194124faa477bcac6086fc4f9.jpg
194 x 259
38px
19.59%
38px
14.67%
2.28%
8βž”
tooth
polygon
2519_jpg.rf.2f3e774194124faa477bcac6086fc4f9.jpg
194 x 259
22px
11.34%
26px
10.04%
0.62%
9βž”
tooth
polygon
2519_jpg.rf.2f3e774194124faa477bcac6086fc4f9.jpg
194 x 259
35px
18.04%
18px
6.95%
0.88%
10βž”
tooth
polygon
2519_jpg.rf.2f3e774194124faa477bcac6086fc4f9.jpg
194 x 259
28px
14.43%
33px
12.74%
1.37%

License #

Dentalai Computer Vision Project is under CC BY 4.0 license.

Source

Citation #

If you make use of the Dentalai data, please cite the following reference:

@dataset{Dentalai,
  author={Pawan Valluri},
  title={Dentalai Computer Vision Project},
  year={2023},
  url={https://www.kaggle.com/datasets/pawanvalluri/dental-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-dentalai-dataset,
  title = { Visualization Tools for Dentalai Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/dentalai } },
  url = { https://datasetninja.com/dentalai },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { mar },
  note = { visited on 2024-03-05 },
}

Download #

Dataset Dentalai 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='Dentalai', 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.

. . .

Disclaimer #

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