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Guns in an Active State Detection Dataset

131021979
Tagsecurity, surveillance
Taskobject detection
Release YearMade in 2023
LicenseDbCL v1.0
Download265 MB

Summary #

Dataset LinkHomepage

Guns in an Active State Detection is a dataset for an object detection task. Possible applications of the dataset could be in the security industry.

The dataset consists of 1310 images with 5968 labeled objects belonging to 2 different classes including human and gun.

Images in the Guns in an Active State Detection dataset have bounding box annotations. There is 1 unlabeled image (i.e. without annotations). There are no pre-defined train/val/test splits in the dataset. The dataset was released in 2023.

Dataset Poster

Explore #

Guns in an Active State Detection dataset has 1310 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 Guns in an Active State DetectionSample image from Guns in an Active State Detection
OpenSample annotation mask from Guns in an Active State DetectionSample image from Guns in an Active State Detection
OpenSample annotation mask from Guns in an Active State DetectionSample image from Guns in an Active State Detection
OpenSample annotation mask from Guns in an Active State DetectionSample image from Guns in an Active State Detection
OpenSample annotation mask from Guns in an Active State DetectionSample image from Guns in an Active State Detection
OpenSample annotation mask from Guns in an Active State DetectionSample image from Guns in an Active State Detection
OpenSample annotation mask from Guns in an Active State DetectionSample image from Guns in an Active State Detection
OpenSample annotation mask from Guns in an Active State DetectionSample image from Guns in an Active State Detection
OpenSample annotation mask from Guns in an Active State DetectionSample image from Guns in an Active State Detection
OpenSample annotation mask from Guns in an Active State DetectionSample image from Guns in an Active State Detection
OpenSample annotation mask from Guns in an Active State DetectionSample image from Guns in an Active State Detection
OpenSample annotation mask from Guns in an Active State DetectionSample image from Guns in an Active State Detection
OpenSample annotation mask from Guns in an Active State DetectionSample image from Guns in an Active State Detection
OpenSample annotation mask from Guns in an Active State DetectionSample image from Guns in an Active State Detection
OpenSample annotation mask from Guns in an Active State DetectionSample image from Guns in an Active State Detection
OpenSample annotation mask from Guns in an Active State DetectionSample image from Guns in an Active State Detection
OpenSample annotation mask from Guns in an Active State DetectionSample image from Guns in an Active State Detection
OpenSample annotation mask from Guns in an Active State DetectionSample image from Guns in an Active State Detection
OpenSample annotation mask from Guns in an Active State DetectionSample image from Guns in an Active State Detection
OpenSample annotation mask from Guns in an Active State DetectionSample image from Guns in an Active State Detection
OpenSample annotation mask from Guns in an Active State DetectionSample image from Guns in an Active State Detection
OpenSample annotation mask from Guns in an Active State DetectionSample image from Guns in an Active State Detection
OpenSample annotation mask from Guns in an Active State DetectionSample image from Guns in an Active State Detection
OpenSample annotation mask from Guns in an Active State DetectionSample image from Guns in an Active State Detection
OpenSample annotation mask from Guns in an Active State DetectionSample image from Guns in an Active State Detection
OpenSample annotation mask from Guns in an Active State DetectionSample image from Guns in an Active State Detection
OpenSample annotation mask from Guns in an Active State DetectionSample image from Guns in an Active State Detection
OpenSample annotation mask from Guns in an Active State DetectionSample image from Guns in an Active State Detection
OpenSample annotation mask from Guns in an Active State DetectionSample image from Guns in an Active State Detection
OpenSample annotation mask from Guns in an Active State DetectionSample image from Guns in an Active State Detection
πŸ‘€
Have a look at 1310 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 2 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 1-2 of 2
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
humanβž”
rectangle
1303
4607
3.54
13.5%
gunβž”
rectangle
993
1361
1.37
1.19%

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 1-2 of 2
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
human
rectangle
4607
3.97%
59.13%
0.01%
14px
1.94%
1000px
99.85%
234px
33.15%
7px
0.55%
773px
74.12%
gun
rectangle
1361
0.87%
75.8%
0.01%
10px
1.39%
904px
88.28%
59px
8.38%
5px
0.39%
935px
91.31%

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 5968 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 5968
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”
human
rectangle
CNN has obtained videos from inside the Westgate Mall (94).jpg
720 x 1280
203px
28.19%
77px
6.02%
1.7%
2βž”
human
rectangle
CNN has obtained videos from inside the Westgate Mall (94).jpg
720 x 1280
282px
39.17%
75px
5.86%
2.29%
3βž”
gun
rectangle
CNN has obtained videos from inside the Westgate Mall (94).jpg
720 x 1280
68px
9.44%
38px
2.97%
0.28%
4βž”
gun
rectangle
CNN has obtained videos from inside the Westgate Mall (94).jpg
720 x 1280
50px
6.94%
66px
5.16%
0.36%
5βž”
human
rectangle
Brussels gun attacker caught on CCTV (21).jpg
368 x 640
98px
26.63%
36px
5.62%
1.5%
6βž”
human
rectangle
Brussels gun attacker caught on CCTV (21).jpg
368 x 640
97px
26.36%
39px
6.09%
1.61%
7βž”
human
rectangle
Kenya Attack Security Footage Shows Al-Shabab Gunmen Entering Nairobi Hotel (116).jpg
720 x 1280
629px
87.36%
569px
44.45%
38.83%
8βž”
gun
rectangle
Kenya Attack Security Footage Shows Al-Shabab Gunmen Entering Nairobi Hotel (116).jpg
720 x 1280
257px
35.69%
426px
33.28%
11.88%
9βž”
human
rectangle
Surveillance Camera Records Deadly State Street Shootout (82).jpg
720 x 1280
359px
49.86%
244px
19.06%
9.5%
10βž”
human
rectangle
Surveillance Camera Records Deadly State Street Shootout (82).jpg
720 x 1280
87px
12.08%
45px
3.52%
0.42%

License #

Guns in an Active State Detection is under DbCL v1.0 license.

Source

Citation #

If you make use of the Guns in an Active State Detection data, please cite the following reference:

@dataset{Guns in an Active State Detection,
  author={Ugorji Richard},
  title={Guns in an Active State Detection},
  year={2023},
  url={https://www.kaggle.com/datasets/ugorjiir/gun-detection}
}

Source

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

@misc{ visualization-tools-for-gun-in-an-active-state-detection-dataset,
  title = { Visualization Tools for Guns in an Active State Detection Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/gun-in-an-active-state-detection } },
  url = { https://datasetninja.com/gun-in-an-active-state-detection },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jul },
  note = { visited on 2024-07-25 },
}

Download #

Dataset Guns in an Active State Detection 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='Guns in an Active State Detection', 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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