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Accurate Drone Shapes Dataset

904912063
Tagdrones
Tasksemantic segmentation
Release YearMade in 2021
LicenseCC BY 4.0
Download711 MB

Introduction #

The Accurate Drone Shapes/Segmentation dataset was created by the MetaVision team using their own original content. They applied various segmentation techniques to create precise masks from the original video streams while retaining the natural variations and original shapes.

Dataset LinkHomepage

Summary #

Accurate Drone Shapes/Segmentation is a dataset for a semantic segmentation task. Possible applications of the dataset could be in the drone inspection domain.

The dataset consists of 9049 images with 9047 labeled objects belonging to 1 single class (drone).

Images in the Accurate Drone Shapes dataset have pixel-level semantic segmentation annotations. There are 2 (0% of the total) unlabeled images (i.e. without annotations). There are no pre-defined train/val/test splits in the dataset. The dataset was released in 2021 by the MetaVision, Ukraine.

Dataset Poster

Explore #

Accurate Drone Shapes dataset has 9049 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 Accurate Drone ShapesSample image from Accurate Drone Shapes
OpenSample annotation mask from Accurate Drone ShapesSample image from Accurate Drone Shapes
OpenSample annotation mask from Accurate Drone ShapesSample image from Accurate Drone Shapes
OpenSample annotation mask from Accurate Drone ShapesSample image from Accurate Drone Shapes
OpenSample annotation mask from Accurate Drone ShapesSample image from Accurate Drone Shapes
OpenSample annotation mask from Accurate Drone ShapesSample image from Accurate Drone Shapes
OpenSample annotation mask from Accurate Drone ShapesSample image from Accurate Drone Shapes
OpenSample annotation mask from Accurate Drone ShapesSample image from Accurate Drone Shapes
OpenSample annotation mask from Accurate Drone ShapesSample image from Accurate Drone Shapes
OpenSample annotation mask from Accurate Drone ShapesSample image from Accurate Drone Shapes
OpenSample annotation mask from Accurate Drone ShapesSample image from Accurate Drone Shapes
OpenSample annotation mask from Accurate Drone ShapesSample image from Accurate Drone Shapes
OpenSample annotation mask from Accurate Drone ShapesSample image from Accurate Drone Shapes
OpenSample annotation mask from Accurate Drone ShapesSample image from Accurate Drone Shapes
OpenSample annotation mask from Accurate Drone ShapesSample image from Accurate Drone Shapes
OpenSample annotation mask from Accurate Drone ShapesSample image from Accurate Drone Shapes
OpenSample annotation mask from Accurate Drone ShapesSample image from Accurate Drone Shapes
OpenSample annotation mask from Accurate Drone ShapesSample image from Accurate Drone Shapes
OpenSample annotation mask from Accurate Drone ShapesSample image from Accurate Drone Shapes
OpenSample annotation mask from Accurate Drone ShapesSample image from Accurate Drone Shapes
OpenSample annotation mask from Accurate Drone ShapesSample image from Accurate Drone Shapes
OpenSample annotation mask from Accurate Drone ShapesSample image from Accurate Drone Shapes
OpenSample annotation mask from Accurate Drone ShapesSample image from Accurate Drone Shapes
OpenSample annotation mask from Accurate Drone ShapesSample image from Accurate Drone Shapes
OpenSample annotation mask from Accurate Drone ShapesSample image from Accurate Drone Shapes
OpenSample annotation mask from Accurate Drone ShapesSample image from Accurate Drone Shapes
OpenSample annotation mask from Accurate Drone ShapesSample image from Accurate Drone Shapes
OpenSample annotation mask from Accurate Drone ShapesSample image from Accurate Drone Shapes
OpenSample annotation mask from Accurate Drone ShapesSample image from Accurate Drone Shapes
OpenSample annotation mask from Accurate Drone ShapesSample image from Accurate Drone Shapes
πŸ‘€
Have a look at 9049 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 1 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 1-1 of 1
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
droneβž”
mask
9047
9047
1
0.03%

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 1-1 of 1
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
drone
mask
9047
0.03%
0.29%
0%
5px
0.55%
167px
7.08%
29px
2.13%
9px
0.62%
176px
12.22%

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 9047 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 9047
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”
drone
mask
image00005217.jpg
720 x 1280
19px
2.64%
31px
2.42%
0.03%
2βž”
drone
mask
image00007832.jpg
720 x 1280
28px
3.89%
37px
2.89%
0.05%
3βž”
drone
mask
image00005768.jpg
720 x 1280
26px
3.61%
79px
6.17%
0.07%
4βž”
drone
mask
image00002063.jpg
2560 x 1440
41px
1.6%
60px
4.17%
0.04%
5βž”
drone
mask
image00008379.jpg
720 x 1280
31px
4.31%
41px
3.2%
0.04%
6βž”
drone
mask
image00000334.jpg
2560 x 1440
43px
1.68%
35px
2.43%
0.02%
7βž”
drone
mask
image00008585.jpg
720 x 1280
21px
2.92%
32px
2.5%
0.02%
8βž”
drone
mask
image00002050.jpg
2560 x 1440
45px
1.76%
62px
4.31%
0.03%
9βž”
drone
mask
image00005300.jpg
720 x 1280
16px
2.22%
31px
2.42%
0.02%
10βž”
drone
mask
image00001371.jpg
2560 x 1440
34px
1.33%
38px
2.64%
0.02%

License #

Accurate Drone Shapes/Segmentation is under CC BY 4.0 license.

Source

Citation #

If you make use of the Accurate Drone Shapes data, please cite the following reference:

@dataset{Accurate Drone Shapes,
	author={metavision},
	title={Accurate Drone Shapes/Segmentation},
	year={2021},
	url={https://www.kaggle.com/datasets/metavision/accurate-drone-shapessegmentation}
}

Source

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

@misc{ visualization-tools-for-accurate-drone-dataset,
  title = { Visualization Tools for Accurate Drone Shapes Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/accurate-drone } },
  url = { https://datasetninja.com/accurate-drone },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { mar },
  note = { visited on 2024-03-05 },
}

Download #

Dataset Accurate Drone Shapes 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='Accurate Drone Shapes', 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.

. . .

Disclaimer #

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