Summary #
HomepageBone Classification and Detection Dataset is a dataset for an object detection task. Possible applications of the dataset could be in the medical industry.
The dataset consists of 2865 images with 3012 labeled objects belonging to 3 different classes including wrist, elbow, and hand.
Images in the Bone Classification and Detection Dataset dataset have bounding box annotations. All images are labeled (i.e. with annotations). There are 2 splits in the dataset: train (2565 images) and valid (300 images). The dataset was released in 2021.
Explore #
Bone Classification and Detection Dataset dataset has 2865 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.
Class balance #
There are 3 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.
Class ㅤ | Images ㅤ | Objects ㅤ | Count on image average | Area on image average |
|---|---|---|---|---|
wrist➔ rectangle | 1000 | 1085 | 1.08 | 6.43% |
elbow➔ rectangle | 1000 | 1004 | 1 | 9.44% |
hand➔ rectangle | 865 | 923 | 1.07 | 37.23% |
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.
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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
wrist rectangle | 1085 | 5.93% | 54.42% | 0.88% | 42px | 8.2% | 385px | 75.2% | 88px | 17.8% | 42px | 8.2% | 364px | 77.23% |
elbow rectangle | 1004 | 9.4% | 23.13% | 1.14% | 43px | 8.4% | 263px | 57.64% | 139px | 28.55% | 51px | 9.96% | 232px | 60.88% |
hand rectangle | 923 | 34.9% | 65.91% | 5.91% | 93px | 22.91% | 379px | 74.02% | 281px | 56.03% | 87px | 21.38% | 418px | 93.74% |
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 3012 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.
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➔ | hand rectangle | el_525.png | 512 x 406 | 306px | 59.77% | 325px | 80.05% | 47.84% |
2➔ | elbow rectangle | dirsek_827.png | 512 x 493 | 180px | 35.16% | 175px | 35.5% | 12.48% |
3➔ | hand rectangle | el_522.png | 512 x 405 | 321px | 62.7% | 320px | 79.01% | 49.54% |
4➔ | wrist rectangle | bilek_207.png | 512 x 424 | 68px | 13.28% | 95px | 22.41% | 2.98% |
5➔ | wrist rectangle | bilek_435.png | 512 x 309 | 167px | 32.62% | 187px | 60.52% | 19.74% |
6➔ | hand rectangle | el_408.png | 512 x 373 | 342px | 66.8% | 296px | 79.36% | 53.01% |
7➔ | elbow rectangle | dirsek_73.png | 512 x 250 | 168px | 32.81% | 128px | 51.2% | 16.8% |
8➔ | elbow rectangle | dirsek_468.png | 512 x 507 | 177px | 34.57% | 156px | 30.77% | 10.64% |
9➔ | hand rectangle | el_295.png | 512 x 346 | 306px | 59.77% | 253px | 73.12% | 43.7% |
10➔ | hand rectangle | el_235.png | 512 x 353 | 330px | 64.45% | 231px | 65.44% | 42.18% |
License #
License is unknown for the Bone Classification and Detection Dataset dataset.
Citation #
If you make use of the Bone Classification and Detection Dataset data, please cite the following reference:
@dataset{Bone Classification and Detection Dataset,
author={Ardacan Uçkan},
title={Bone Classification and Detection Dataset},
year={2021},
url={https://www.kaggle.com/datasets/ardacanuckan/bone-classification-and-detection-dataset}
}
If you are happy with Dataset Ninja and use provided visualizations and tools in your work, please cite us:
@misc{ visualization-tools-for-bone-classification-detection-dataset,
title = { Visualization Tools for Bone Classification and Detection Dataset Dataset },
type = { Computer Vision Tools },
author = { Dataset Ninja },
howpublished = { \url{ https://datasetninja.com/bone-classification-detection } },
url = { https://datasetninja.com/bone-classification-detection },
journal = { Dataset Ninja },
publisher = { Dataset Ninja },
year = { 2024 },
month = { oct },
note = { visited on 2024-10-15 },
}Download #
Please visit dataset homepage to download the data.
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