Dataset Ninja LogoDataset Ninja:

Iraqi Money Dataset

7260141
Taggeneral
Taskobject detection
Release YearMade in 2020
Licenseunknown

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.

ExpandExpand
Dataset LinkHomepage

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.

Dataset Poster

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.

OpenSample annotation mask from Iraqi MoneySample image from Iraqi Money
OpenSample annotation mask from Iraqi MoneySample image from Iraqi Money
OpenSample annotation mask from Iraqi MoneySample image from Iraqi Money
OpenSample annotation mask from Iraqi MoneySample image from Iraqi Money
OpenSample annotation mask from Iraqi MoneySample image from Iraqi Money
OpenSample annotation mask from Iraqi MoneySample image from Iraqi Money
OpenSample annotation mask from Iraqi MoneySample image from Iraqi Money
OpenSample annotation mask from Iraqi MoneySample image from Iraqi Money
OpenSample annotation mask from Iraqi MoneySample image from Iraqi Money
OpenSample annotation mask from Iraqi MoneySample image from Iraqi Money
OpenSample annotation mask from Iraqi MoneySample image from Iraqi Money
OpenSample annotation mask from Iraqi MoneySample image from Iraqi Money
πŸ‘€
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.

Search
Rows 1-10 of 14
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
1000enβž”
rectangle
638
638
1
28.86%
500enβž”
rectangle
616
616
1
32.06%
5000arβž”
rectangle
605
605
1
29.83%
500arβž”
rectangle
572
572
1
30.75%
1000arβž”
rectangle
561
561
1
32.22%
10000enβž”
rectangle
561
561
1
30.92%
250enβž”
rectangle
550
550
1
33.77%
250arβž”
rectangle
550
550
1
33.41%
10000arβž”
rectangle
528
528
1
30.84%
5000enβž”
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.

Search
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.

Spatial Heatmap

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.

Search
Rows 1-10 of 7260
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”
10000en
rectangle
6_2217882748_554160058.136446.jpg
400 x 400
167px
41.75%
353px
88.25%
36.84%
2βž”
10000en
rectangle
6_3503475142_554160129.384252.jpg
400 x 400
145px
36.25%
305px
76.25%
27.64%
3βž”
1000en
rectangle
14_554413670.475245_rotated_-45.0.jpg
565 x 565
321px
56.81%
321px
56.81%
32.28%
4βž”
5000ar
rectangle
10_554401683.265442_rotated_-90.0.jpg
400 x 400
365px
91.25%
183px
45.75%
41.75%
5βž”
25000en
rectangle
8_554588972.877099_half_cropped.jpg
400 x 204
153px
38.25%
169px
82.84%
31.69%
6βž”
50000ar
rectangle
13_554412961.796653_rotated_180.0.jpg
400 x 400
101px
25.25%
241px
60.25%
15.21%
7βž”
500en
rectangle
19_554593372.286726_rotated_-135.0.jpg
565 x 565
371px
65.66%
371px
65.66%
43.12%
8βž”
500ar
rectangle
18_554589887.725403_dark_-0.5.jpg
400 x 400
179px
44.75%
393px
98.25%
43.97%
9βž”
50000ar
rectangle
13_554413272.415882.jpg
400 x 400
163px
40.75%
393px
98.25%
40.04%
10βž”
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 = { jun },
  note = { visited on 2024-06-25 },
}

Download #

Please visit dataset homepage to download the data.

. . .

Disclaimer #

Our gal from the legal dep told us we need to post this:

Dataset Ninja provides visualizations and statistics for some datasets that can be found online and can be downloaded by general audience. Dataset Ninja is not a dataset hosting platform and can only be used for informational purposes. The platform does not claim any rights for the original content, including images, videos, annotations and descriptions. Joint publishing is prohibited.

You take full responsibility when you use datasets presented at Dataset Ninja, as well as other information, including visualizations and statistics we provide. You are in charge of compliance with any dataset license and all other permissions. You are required to navigate datasets homepage and make sure that you can use it. In case of any questions, get in touch with us at hello@datasetninja.com.