Guns in an Active State Detection

Summary #

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.

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.

Sample annotation mask from Guns in an Active State Detection

Sample image from Guns in an Active State Detection

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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.

Rows 1-2 of 2

Class ㅤ	Images ㅤ	Objects ㅤ	Count on image average	Area on image average
human➔ rectangle	1303	4607	3.54	13.5%
gun➔ 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.

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.

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.

Rows 1-10 of 5968

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➔	human rectangle	CNN has obtained videos from inside the Westgate Mall (94).jpg	720 x 1280	203px	28.19%	77px	6.02%	1.7%
2➔	human rectangle	CNN has obtained videos from inside the Westgate Mall (94).jpg	720 x 1280	282px	39.17%	75px	5.86%	2.29%
3➔	gun rectangle	CNN has obtained videos from inside the Westgate Mall (94).jpg	720 x 1280	68px	9.44%	38px	2.97%	0.28%
4➔	gun rectangle	CNN has obtained videos from inside the Westgate Mall (94).jpg	720 x 1280	50px	6.94%	66px	5.16%	0.36%
5➔	human rectangle	Brussels gun attacker caught on CCTV (21).jpg	368 x 640	98px	26.63%	36px	5.62%	1.5%
6➔	human rectangle	Brussels gun attacker caught on CCTV (21).jpg	368 x 640	97px	26.36%	39px	6.09%	1.61%
7➔	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%
8➔	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%
9➔	human rectangle	Surveillance Camera Records Deadly State Street Shootout (82).jpg	720 x 1280	359px	49.86%	244px	19.06%	9.5%
10➔	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 = { nov },
  note = { visited on 2024-11-21 },
}

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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Disclaimer #

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