Iraqi Money - Dataset Ninja

Introduction #

Husam Aamer

The Iraqi Money dataset is specifically designed for an object detection task, comprising 7260 images with a total of 7260 labeled objects distributed among 14 distinct classes. These classes encompass a variety of Iraqi currency denominations, such as 1000en, 500en, and 5000ar, alongside other notes including 500ar, 1000ar, 10000en, 250ar, 250en, 10000ar, 5000en, 25000ar, 25000en, 50000ar, and 50000en. The dataset serves as a fundamental resource in training the CoreML model for the MoneyReader app developed for iOS, aiding in the accurate recognition and classification of Iraqi currency notes.

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Summary #

Iraqi Money is a dataset for an object detection task. It is used in the optical character recognition (OCR) domain.

The dataset consists of 7260 images with 7260 labeled objects belonging to 14 different classes including 1000en, 500en, 5000ar, and other: 500ar, 1000ar, 10000en, 250ar, 250en, 10000ar, 5000en, 25000ar, 25000en, 50000ar, and 50000en.

Images in the Iraqi Money dataset have bounding box annotations. All images are labeled (i.e. with annotations). There are no pre-defined train/val/test splits in the dataset. The dataset was released in 2020 by the AppChief.net.

Explore #

Iraqi Money dataset has 7260 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.

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Have a look at 7260 images

Because of dataset's license preview is limited to 12 images

View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 14 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 1-10 of 14

Class ㅤ	Images ㅤ	Objects ㅤ	Count on image average	Area on image average
1000en➔ rectangle	638	638	1	28.86%
500en➔ rectangle	616	616	1	32.06%
5000ar➔ rectangle	605	605	1	29.83%
500ar➔ rectangle	572	572	1	30.75%
1000ar➔ rectangle	561	561	1	32.22%
10000en➔ rectangle	561	561	1	30.92%
250en➔ rectangle	550	550	1	33.77%
250ar➔ rectangle	550	550	1	33.41%
10000ar➔ rectangle	528	528	1	30.84%
5000en➔ rectangle	473	473	1	27.34%

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 1-10 of 14

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
1000en rectangle	638	28.86%	52.15%	12.69%	109px	27.25%	409px	99.25%	219px	48.36%	109px	27.25%	409px	100%
500en rectangle	616	32.06%	52.4%	18.53%	119px	29.75%	409px	99.25%	232px	51.21%	119px	29.75%	409px	99.5%
5000ar rectangle	605	29.83%	52.15%	8.94%	101px	25.25%	409px	96.25%	222px	49.15%	101px	25.25%	409px	100%
500ar rectangle	572	30.75%	52.4%	16.16%	119px	29.75%	409px	99.25%	227px	50.19%	119px	29.75%	409px	100%
1000ar rectangle	561	32.22%	52.4%	13.99%	111px	27.75%	409px	99.25%	231px	51.13%	111px	27.75%	409px	100%
10000en rectangle	561	30.92%	52.15%	13.84%	113px	28.25%	409px	98.25%	227px	50.27%	113px	28.25%	409px	100%
250en rectangle	550	33.77%	55.52%	15.02%	121px	30.25%	421px	99.25%	238px	52.72%	120px	30.25%	421px	100%
250ar rectangle	550	33.41%	55.52%	14.42%	121px	30.25%	421px	99.25%	236px	52.29%	121px	30.25%	421px	99.49%
10000ar rectangle	528	30.84%	53.17%	13.5%	113px	28.25%	413px	99.25%	227px	50.27%	113px	28.25%	413px	99.25%
5000en rectangle	473	27.34%	54.21%	5.82%	83px	20.75%	417px	98.25%	210px	46.52%	83px	20.75%	417px	99.49%

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 7260 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 7260

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➔	10000en rectangle	6_2217882748_554160058.136446.jpg	400 x 400	167px	41.75%	353px	88.25%	36.84%
2➔	10000en rectangle	6_3503475142_554160129.384252.jpg	400 x 400	145px	36.25%	305px	76.25%	27.64%
3➔	1000en rectangle	14_554413670.475245_rotated_-45.0.jpg	565 x 565	321px	56.81%	321px	56.81%	32.28%
4➔	5000ar rectangle	10_554401683.265442_rotated_-90.0.jpg	400 x 400	365px	91.25%	183px	45.75%	41.75%
5➔	25000en rectangle	8_554588972.877099_half_cropped.jpg	400 x 204	153px	38.25%	169px	82.84%	31.69%
6➔	50000ar rectangle	13_554412961.796653_rotated_180.0.jpg	400 x 400	101px	25.25%	241px	60.25%	15.21%
7➔	500en rectangle	19_554593372.286726_rotated_-135.0.jpg	565 x 565	371px	65.66%	371px	65.66%	43.12%
8➔	500ar rectangle	18_554589887.725403_dark_-0.5.jpg	400 x 400	179px	44.75%	393px	98.25%	43.97%
9➔	50000ar rectangle	13_554413272.415882.jpg	400 x 400	163px	40.75%	393px	98.25%	40.04%
10➔	500ar rectangle	18_554589781.909195_rotated_-45.0.jpg	565 x 565	409px	72.39%	409px	72.39%	52.4%

License #

License is unknown for the Iraqi Money dataset.

Source

Citation #

If you make use of the Iraqi Money data, please cite the following reference:

@dataset{Iraqi Money,
  author={Husam Aamer},
  title={Iraqi Money},
  year={2020},
  url={https://www.kaggle.com/datasets/husamaamer/iraqi-currency-}
}

Source

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

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

Download #

Please visit dataset homepage to download the data.

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