Summary #
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.
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.
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 |
---|---|---|---|---|
non-metal lighterâž” 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.
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.
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.
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” | non-metal lighter rectangle | 003723301009804.jpg | 1040 x 652 | 141px | 13.56% | 80px | 12.27% | 1.66% |
2âž” | non-metal lighter rectangle | 001666601012151.jpg | 1040 x 1032 | 79px | 7.6% | 84px | 8.14% | 0.62% |
3âž” | non-metal lighter rectangle | 000350901017294.jpg | 1040 x 1256 | 50px | 4.81% | 122px | 9.71% | 0.47% |
4âž” | non-metal lighter rectangle | 005983401003635.jpg | 1040 x 1796 | 55px | 5.29% | 109px | 6.07% | 0.32% |
5âž” | non-metal lighter rectangle | 002289301000908.jpg | 1040 x 676 | 92px | 8.85% | 142px | 21.01% | 1.86% |
6âž” | non-metal lighter rectangle | 006219601025157.jpg | 1040 x 668 | 92px | 8.85% | 102px | 15.27% | 1.35% |
7âž” | non-metal lighter rectangle | 003689901009470.jpg | 1040 x 1280 | 73px | 7.02% | 88px | 6.88% | 0.48% |
8âž” | non-metal lighter rectangle | 001313301009918.jpg | 1040 x 2000 | 45px | 4.33% | 101px | 5.05% | 0.22% |
9âž” | non-metal lighter rectangle | 004564401007032.jpg | 1040 x 1892 | 58px | 5.58% | 74px | 3.91% | 0.22% |
10âž” | 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}
}
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 = { oct },
note = { visited on 2024-10-31 },
}
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.
Disclaimer #
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