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Non-Metal Lighter Target Detection Under X-Ray Dataset

88311
Tagsecurity, surveillance
Taskobject detection
Release YearMade in 2021
LicenseGNU GPL 2.0
Download144 MB

Summary #

Dataset LinkHomepage

Non-Metal Lighter Target Detection Under X-Ray is a dataset for an object detection task. Possible applications of the dataset could be in the security industry.

The dataset consists of 883 images with 769 labeled objects belonging to 1 single class (non-metal lighter).

Images in the Non-Metal Lighter Target Detection Under X-Ray dataset have bounding box annotations. There are 177 (20% of the total) unlabeled images (i.e. without annotations). There are 2 splits in the dataset: train (706 images) and test (177 images). The dataset was released in 2021.

Dataset Poster

Explore #

Non-Metal Lighter Target Detection Under X-Ray dataset has 883 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 Non-Metal Lighter Target Detection Under X-RaySample image from Non-Metal Lighter Target Detection Under X-Ray
OpenSample annotation mask from Non-Metal Lighter Target Detection Under X-RaySample image from Non-Metal Lighter Target Detection Under X-Ray
OpenSample annotation mask from Non-Metal Lighter Target Detection Under X-RaySample image from Non-Metal Lighter Target Detection Under X-Ray
OpenSample annotation mask from Non-Metal Lighter Target Detection Under X-RaySample image from Non-Metal Lighter Target Detection Under X-Ray
OpenSample annotation mask from Non-Metal Lighter Target Detection Under X-RaySample image from Non-Metal Lighter Target Detection Under X-Ray
OpenSample annotation mask from Non-Metal Lighter Target Detection Under X-RaySample image from Non-Metal Lighter Target Detection Under X-Ray
OpenSample annotation mask from Non-Metal Lighter Target Detection Under X-RaySample image from Non-Metal Lighter Target Detection Under X-Ray
OpenSample annotation mask from Non-Metal Lighter Target Detection Under X-RaySample image from Non-Metal Lighter Target Detection Under X-Ray
OpenSample annotation mask from Non-Metal Lighter Target Detection Under X-RaySample image from Non-Metal Lighter Target Detection Under X-Ray
OpenSample annotation mask from Non-Metal Lighter Target Detection Under X-RaySample image from Non-Metal Lighter Target Detection Under X-Ray
OpenSample annotation mask from Non-Metal Lighter Target Detection Under X-RaySample image from Non-Metal Lighter Target Detection Under X-Ray
OpenSample annotation mask from Non-Metal Lighter Target Detection Under X-RaySample image from Non-Metal Lighter Target Detection Under X-Ray
OpenSample annotation mask from Non-Metal Lighter Target Detection Under X-RaySample image from Non-Metal Lighter Target Detection Under X-Ray
OpenSample annotation mask from Non-Metal Lighter Target Detection Under X-RaySample image from Non-Metal Lighter Target Detection Under X-Ray
OpenSample annotation mask from Non-Metal Lighter Target Detection Under X-RaySample image from Non-Metal Lighter Target Detection Under X-Ray
OpenSample annotation mask from Non-Metal Lighter Target Detection Under X-RaySample image from Non-Metal Lighter Target Detection Under X-Ray
OpenSample annotation mask from Non-Metal Lighter Target Detection Under X-RaySample image from Non-Metal Lighter Target Detection Under X-Ray
OpenSample annotation mask from Non-Metal Lighter Target Detection Under X-RaySample image from Non-Metal Lighter Target Detection Under X-Ray
OpenSample annotation mask from Non-Metal Lighter Target Detection Under X-RaySample image from Non-Metal Lighter Target Detection Under X-Ray
OpenSample annotation mask from Non-Metal Lighter Target Detection Under X-RaySample image from Non-Metal Lighter Target Detection Under X-Ray
OpenSample annotation mask from Non-Metal Lighter Target Detection Under X-RaySample image from Non-Metal Lighter Target Detection Under X-Ray
OpenSample annotation mask from Non-Metal Lighter Target Detection Under X-RaySample image from Non-Metal Lighter Target Detection Under X-Ray
OpenSample annotation mask from Non-Metal Lighter Target Detection Under X-RaySample image from Non-Metal Lighter Target Detection Under X-Ray
OpenSample annotation mask from Non-Metal Lighter Target Detection Under X-RaySample image from Non-Metal Lighter Target Detection Under X-Ray
OpenSample annotation mask from Non-Metal Lighter Target Detection Under X-RaySample image from Non-Metal Lighter Target Detection Under X-Ray
OpenSample annotation mask from Non-Metal Lighter Target Detection Under X-RaySample image from Non-Metal Lighter Target Detection Under X-Ray
OpenSample annotation mask from Non-Metal Lighter Target Detection Under X-RaySample image from Non-Metal Lighter Target Detection Under X-Ray
OpenSample annotation mask from Non-Metal Lighter Target Detection Under X-RaySample image from Non-Metal Lighter Target Detection Under X-Ray
OpenSample annotation mask from Non-Metal Lighter Target Detection Under X-RaySample image from Non-Metal Lighter Target Detection Under X-Ray
OpenSample annotation mask from Non-Metal Lighter Target Detection Under X-RaySample image from Non-Metal Lighter Target Detection Under X-Ray
πŸ‘€
Have a look at 883 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
non-metal lighterβž”
rectangle
706
769
1.09
0.8%

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
non-metal lighter
rectangle
769
0.74%
4.66%
0.1%
30px
2.88%
184px
17.69%
83px
7.94%
28px
1.5%
174px
51.43%

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 769 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 769
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”
non-metal lighter
rectangle
003723301009804.jpg
1040 x 652
141px
13.56%
80px
12.27%
1.66%
2βž”
non-metal lighter
rectangle
001666601012151.jpg
1040 x 1032
79px
7.6%
84px
8.14%
0.62%
3βž”
non-metal lighter
rectangle
000350901017294.jpg
1040 x 1256
50px
4.81%
122px
9.71%
0.47%
4βž”
non-metal lighter
rectangle
005983401003635.jpg
1040 x 1796
55px
5.29%
109px
6.07%
0.32%
5βž”
non-metal lighter
rectangle
002289301000908.jpg
1040 x 676
92px
8.85%
142px
21.01%
1.86%
6βž”
non-metal lighter
rectangle
006219601025157.jpg
1040 x 668
92px
8.85%
102px
15.27%
1.35%
7βž”
non-metal lighter
rectangle
003689901009470.jpg
1040 x 1280
73px
7.02%
88px
6.88%
0.48%
8βž”
non-metal lighter
rectangle
001313301009918.jpg
1040 x 2000
45px
4.33%
101px
5.05%
0.22%
9βž”
non-metal lighter
rectangle
004564401007032.jpg
1040 x 1892
58px
5.58%
74px
3.91%
0.22%
10βž”
non-metal lighter
rectangle
004564401007032.jpg
1040 x 1892
91px
8.75%
47px
2.48%
0.22%

License #

Non-Metal Lighter Target Detection Under X-Ray is under GNU GPL 2.0 license.

Citation #

If you make use of the Lighter detection under x-ray data, please cite the following reference:

@dataset{Lighter detection under x-ray,
  author={Voler},
  title={Non-metal lighter target detection under X-ray},
  year={2021},
  url={https://www.kaggle.com/datasets/voler2333/lighter-detection-under-xray}
}

Source

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

@misc{ visualization-tools-for-lighter-detection-dataset,
  title = { Visualization Tools for Non-Metal Lighter Target Detection Under X-Ray Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/lighter-detection } },
  url = { https://datasetninja.com/lighter-detection },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jun },
  note = { visited on 2024-06-25 },
}

Download #

Dataset Non-Metal Lighter Target Detection Under X-Ray 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='Non-Metal Lighter Target Detection Under X-Ray', 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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