Weapons in Images - Dataset Ninja

Introduction #

A.N.M. Jubaer

The author of the dataset was engaged in a project related to weapon detection in CCTV footage and encountered difficulties in finding a suitable pre-existing dataset for their research. Consequently, they decided to create the new dataset. It primarily consists of segmented videos (sourced mainly from YouTube) and images (other sources).

Homepage

Summary #

Weapons in Images: Images of Weapons with YOLO Annotations for Detecting Weapons is a dataset for an object detection task. Possible applications of the dataset could be in the security industry.

The dataset consists of 5695 images with 9304 labeled objects belonging to 1 single class (weapon).

Images in the Weapons in Images dataset have bounding box annotations. There are 1349 (24% of the total) unlabeled images (i.e. without annotations). There are 2 splits in the dataset: train-weapons_in_images (4375 images) and test-cctv (1320 images). The dataset was released in 2020.

Explore #

Weapons in Images dataset has 5695 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 Weapons in Images

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Have a look at 5695 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.

Rows 1-1 of 1

Class ㅤ	Images ㅤ	Objects ㅤ	Count on image average	Area on image average
weapon➔ rectangle	4346	9304	2.14	18.2%

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 1-1 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
weapon rectangle	9304	8.99%	99.92%	0%	2px	0.33%	1024px	100%	200px	27.58%	1px	0.1%	1279px	100%

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 9304 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 9304

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➔	weapon rectangle	5038299639e177eb.jpg	1024 x 681	1009px	98.54%	335px	49.19%	48.47%
2➔	weapon rectangle	a8bb38e1f134279a.jpg	1024 x 803	209px	20.41%	220px	27.4%	5.59%
3➔	weapon rectangle	PUBGGunsInRealLife!14891.jpg	720 x 1280	391px	54.31%	622px	48.59%	26.39%
4➔	weapon rectangle	PUBGGunsInRealLife!14891.jpg	720 x 1280	84px	11.67%	59px	4.61%	0.54%
5➔	weapon rectangle	PUBGGunsInRealLife!14891.jpg	720 x 1280	91px	12.64%	66px	5.16%	0.65%
6➔	weapon rectangle	PUBGGunsInRealLife!14891.jpg	720 x 1280	100px	13.89%	117px	9.14%	1.27%
7➔	weapon rectangle	PUBGGunsInRealLife!14891.jpg	720 x 1280	129px	17.92%	126px	9.84%	1.76%
8➔	weapon rectangle	PUBGGunsInRealLife!14891.jpg	720 x 1280	144px	20%	188px	14.69%	2.94%
9➔	weapon rectangle	WhiteHouseShootingSecretServiceShootGun-wieldingMan[CAUGHTONTAPE](302).jpg	720 x 1280	38px	5.28%	64px	5%	0.26%
10➔	weapon rectangle	WhiteHouseShootingSecretServiceShootGun-wieldingMan[CAUGHTONTAPE](302).jpg	720 x 1280	36px	5%	52px	4.06%	0.2%

License #

Weapons in Images: Images of Weapons with YOLO Annotations for Detecting Weapons is under DbCL v1.0 license.

Source

Citation #

If you make use of the Weapons in Images data, please cite the following reference:

@dataset{Weapons in Images,
  author={A.N.M. Jubaer},
  title={Weapons in Images: Images of Weapons with YOLO Annotations for Detecting Weapons},
  year={2020},
  url={https://www.kaggle.com/datasets/jubaerad/weapons-in-images-segmented-videos}
}

Source

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

@misc{ visualization-tools-for-weapons-in-images-dataset,
  title = { Visualization Tools for Weapons in Images Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/weapons-in-images } },
  url = { https://datasetninja.com/weapons-in-images },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { nov },
  note = { visited on 2024-11-21 },
}

Download #

Dataset Weapons in Images 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='Weapons in Images', 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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