---
license: cc-by-4.0
pretty_name: Bodnár-Huawei Syn10k — synthetic + real barcode images with masks
task_categories:
- image-segmentation
- object-detection
tags:
- qr-code
- barcode
- segmentation
- synthetic
size_categories:
- 10K<n<100K
configs:
- config_name: warp2014
  data_files:
  - split: train
    path: data/warp2014/train*.parquet
- config_name: huawei
  data_files:
  - split: train
    path: data/huawei/train*.parquet
---

# Bodnár-Huawei Syn10k

A **mirror** of the `syn10k_plus_huawei` barcode dataset from the Szeged group
(Bodnár, Grósz, Tóth), repackaged as parquet with each image joined to its
ground-truth mask in the same row.

Released under **CC BY 4.0**, so this mirror is permitted with attribution.

## Configs

| config | rows | content | with mask |
|---|---:|---|---:|
| `warp2014` | 10,000 | synthetic warped barcode images | 10,000 |
| `huawei` | 98 | real photographs | 98 |

**Every image has its mask** — the pairing was verified by stem, and zero images
in either config are missing one.

## Fields

| field | notes |
|---|---|
| `image` | the picture (JPEG) |
| `mask` | the ground-truth mask (PNG) |
| `file_name`, `stem` | original filename and its stem |
| `subset` | `warp2014` or `huawei` |
| `has_mask` | always true in this build; kept so the schema stays honest if a future revision has gaps |

## Two notes on how to read this

**The split name is a container, not a protocol.** Both configs expose a single
split called `train` because HuggingFace requires a split name — it does **not**
encode the authors' intended train/test partition. `warp2014` is synthetic and
`huawei` is real photographs, which is the natural synthetic-train / real-test
arrangement, but this mirror does not assert that the authors used it that way.
Consult the source publication before reporting numbers as theirs.

**This is a barcode dataset, not a QR-only one.** It is included in a QR
collection because it is standard in the barcode-localisation literature and is
one of the few sets shipping pixel masks. Check the masks for what you need
rather than assuming every symbol is a QR code.

## Usage

```python
from datasets import load_dataset

syn  = load_dataset("devmandan/syn10k-huawei-barcodes", "warp2014", split="train")
real = load_dataset("devmandan/syn10k-huawei-barcodes", "huawei",   split="train")
syn[0]["image"], syn[0]["mask"]      # PIL images, aligned
```

## Citation

Original source:
<https://www.inf.u-szeged.hu/~bodnaar/barcode_database/syn10k_plus_huawei.zip>

```bibtex
@misc{bodnar_syn10k,
  title  = {Syn10k + Huawei barcode database},
  author = {Bodn\'ar, P\'eter and Gr\'osz, Tam\'as and T\'oth, L\'aszl\'o},
  note   = {Synthetic and real barcode images with ground-truth masks},
  url    = {https://www.inf.u-szeged.hu/~bodnaar/barcode_database/}
}
```

Licence: CC BY 4.0 — <https://creativecommons.org/licenses/by/4.0/>
Please cite the authors above, not this mirror.