Introduction #
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).
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.
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.
Class ã…¤ | Images ã…¤ | Objects ã…¤ | Count on image average | Area on image average |
---|---|---|---|---|
weaponâž” 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.
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.
Object ID ã…¤ | Class ã…¤ | Image name click row to open | Image size height x width | Height ã…¤ | Height ã…¤ | Width ã…¤ | Width ã…¤ | Area ã…¤ |
---|---|---|---|---|---|---|---|---|
1âž” | weapon rectangle | 5038299639e177eb.jpg | 1024 x 681 | 1009px | 98.54% | 335px | 49.19% | 48.47% |
2âž” | weapon rectangle | a8bb38e1f134279a.jpg | 1024 x 803 | 209px | 20.41% | 220px | 27.4% | 5.59% |
3âž” | weapon rectangle | PUBGGunsInRealLife!14891.jpg | 720 x 1280 | 391px | 54.31% | 622px | 48.59% | 26.39% |
4âž” | weapon rectangle | PUBGGunsInRealLife!14891.jpg | 720 x 1280 | 84px | 11.67% | 59px | 4.61% | 0.54% |
5âž” | weapon rectangle | PUBGGunsInRealLife!14891.jpg | 720 x 1280 | 91px | 12.64% | 66px | 5.16% | 0.65% |
6âž” | weapon rectangle | PUBGGunsInRealLife!14891.jpg | 720 x 1280 | 100px | 13.89% | 117px | 9.14% | 1.27% |
7âž” | weapon rectangle | PUBGGunsInRealLife!14891.jpg | 720 x 1280 | 129px | 17.92% | 126px | 9.84% | 1.76% |
8âž” | weapon rectangle | PUBGGunsInRealLife!14891.jpg | 720 x 1280 | 144px | 20% | 188px | 14.69% | 2.94% |
9âž” | weapon rectangle | WhiteHouseShootingSecretServiceShootGun-wieldingMan[CAUGHTONTAPE](302).jpg | 720 x 1280 | 38px | 5.28% | 64px | 5% | 0.26% |
10âž” | weapon rectangle | WhiteHouseShootingSecretServiceShootGun-wieldingMan[CAUGHTONTAPE](302).jpg | 720 x 1280 | 36px | 5% | 52px | 4.06% | 0.2% |
License #
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}
}
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 = { oct },
note = { visited on 2024-10-15 },
}
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.
Disclaimer #
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