Datasets:

DAISLab-Unisa
/

EURO_Coin_Dataset

license: cc-by-nc-4.0
task_categories:
  - image-classification
language:
  - en
tags:
  - euro
  - coins
  - currency
  - numismatics
  - image-classification
  - computer-vision
  - web-crawled
pretty_name: EURO-Coin Dataset
size_categories:
  - 10K<n<100K

EURO-Coin Dataset

Dataset Summary

The EURO-Coin Dataset is a collection of 12,878 images of euro coins from 23 issuing countries, spanning all 8 denominations (1 cent to 2 euro). Images were gathered via an automated web crawler that extracted publicly available pictures from Google, Bing, and Baidu image search engines.

The dataset is intended exclusively for non-commercial research purposes (e.g., computer vision, deep learning, currency recognition). The original repository is available at https://github.com/cciro94/EuroCoinDataset.

Supported Tasks

Task	Description
Country classification	Predict the issuing country of a coin from its image (23 classes).
Value classification	Predict the denomination of a coin from its image (8 classes).
Joint classification	Predict both country and denomination simultaneously.

Dataset Structure

The repository includes two parallel directory trees, each containing the same 12,878 images organised from a different perspective:

EuroCoinDataset/
├── country_dataset/          # Organised by issuing country (23 sub-folders)
│   ├── Andorra/
│   ├── Austria/
│   ├── Belgium/
│   ├── ...
│   └── Vatican-City/
│
└── denomination_dataset/     # Organised by denomination (8 sub-folders)
    ├── 1-cent/
    ├── 2-cent/
    ├── 5-cent/
    ├── 10-cent/
    ├── 20-cent/
    ├── 50-cent/
    ├── 1-euro/
    └── 2-euro/

File naming convention

country_dataset: <Country>_<index>.<ext> (e.g., Germany_42.jpg)
denomination_dataset: <denomination>-<Country>_<index>.<ext> (e.g., 2-euro-Germany_42.jpg)

Images are provided in .jpg and .jpeg format at their original resolution as retrieved from the web.

Data Statistics

Country	1c	2c	5c	10c	20c	50c	1€	2€	Total
Andorra	42	46	71	43	49	57	60	220	588
Austria	83	64	76	72	49	57	73	94	568
Belgium	86	29	99	98	112	61	78	144	707
Cyprus	13	23	38	40	25	28	84	107	358
Estonia	58	44	45	41	44	30	61	102	425
Finland	49	38	61	36	72	58	99	201	614
France	34	57	67	52	59	73	57	128	527
Germany	163	131	60	163	80	62	90	178	927
Greece	121	52	58	63	69	59	117	169	708
Ireland	56	65	50	83	76	45	66	124	565
Italy	121	36	67	59	72	79	70	119	623
Latvia	71	30	45	17	21	45	91	232	552
Lithuania	94	15	25	28	35	31	90	181	499
Luxembourg	43	29	35	31	52	35	90	173	488
Malta	74	52	64	76	83	79	76	154	658
Monaco	14	3	33	48	57	47	110	107	419
Netherlands	35	32	64	76	72	69	62	166	576
Portugal	55	57	69	80	40	37	101	132	571
San Marino	47	56	57	60	62	77	109	114	582
Slovakia	34	26	21	20	19	32	39	156	347
Slovenia	56	45	42	50	52	45	65	136	491
Spain	23	41	46	48	52	55	97	174	536
Vatican City	52	30	34	58	50	91	62	172	549
Total	1424	1001	1227	1342	1302	1252	1847	3483	12878

Key figures:

Total images: 12,878
Countries: 23
Denominations: 8 (1c, 2c, 5c, 10c, 20c, 50c, 1€, 2€)
Image formats: JPEG
Class balance: Germany has the most images (927); Slovakia the fewest (347)

Dataset Creation

Collection Method

Images were collected using a custom web crawler that submitted country + denomination queries (e.g., "Germany 2 euro coin") to Google Images, Bing Images, and Baidu Images, then downloaded all publicly accessible results. No manual annotation was required: the country and denomination labels are implicit in the search query used to retrieve each image.

Preprocessing

Images are stored at their original resolution and aspect ratio as retrieved from the web. No cropping, resizing, or colour normalisation was applied. Some images may contain multiple coins, backgrounds, watermarks, or text overlays, reflecting the natural variability of web-scraped data.

Curation

Duplicate and clearly corrupt images were removed manually. The dataset covers all euro-area member states that issue coins, plus Monaco, San Marino, Andorra, and Vatican City, which mint euro coins under special agreement with the EU.

Experiments and Baselines

The dataset was used in the following peer-reviewed study to benchmark deep learning models for simultaneous country and value classification:

Cirillo, S., Solimando, G., & Virgili, L. (2023). A deep learning approach to classify country and value of modern coins. Neural Computing and Applications, 1–17. Springer. https://doi.org/10.1007/s00521-023-08615-9

Key experimental results reported in the paper:

Several CNN architectures were evaluated (custom CNNs and pre-trained models via transfer learning).
Best models achieved high top-1 accuracy on both country and denomination classification tasks.
The dataset was split into training, validation, and test sets with stratified sampling.
The dual-folder structure (country_dataset / denomination_dataset) allows straightforward use for either single-label or multi-label classification scenarios.

Considerations for Using the Data

License and Permitted Use

This dataset is released under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license. Use for commercial purposes is not permitted. Any publication or derivative work must include the citation below.

Limitations

Images are web-scraped and may contain noise, watermarks, or irrelevant content.
Class sizes are unbalanced (e.g., Germany: 927 vs. Slovakia: 347).
The dataset does not cover commemorative or special-edition coin designs exhaustively.
No bounding-box or segmentation annotations are provided.

Personal and Sensitive Information

The dataset contains only images of physical coins. No personally identifiable information (PII) is present.

Additional Information

Original Repository

https://github.com/cciro94/EuroCoinDataset

Dataset Curators

Stefano Cirillo
Giandomenico Solimando
Luca Virgili

Citation

If you use this dataset, please cite:

@article{cirillo2023deep,
  title={A deep learning approach to classify country and value of modern coins},
  author={Cirillo, Stefano and Solimando, Giandomenico and Virgili, Luca},
  journal={Neural Computing and Applications},
  pages={1--17},
  year={2023},
  publisher={Springer}
}