| --- |
| license: cc-by-4.0 |
| pretty_name: SBD QR Subset (low resolution) — barcode ROIs with instance masks |
| task_categories: |
| - image-segmentation |
| - object-detection |
| tags: |
| - qr-code |
| - barcode |
| - instance-segmentation |
| - synthetic |
| size_categories: |
| - 10K<n<100K |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train/*.parquet |
| - split: validation |
| path: data/val/*.parquet |
| - split: test |
| path: data/test/*.parquet |
| --- |
| |
| # SBD QR Subset — low resolution |
|
|
| A **mirror** of the low-resolution ROI split of the Synthetic Barcode Dataset |
| (Quenum, Wang, Zakhor), repackaged from **749,682 loose files into parquet**. |
|
|
| | split | ROIs | instances | |
| |---|---:|---:| |
| | train | 80,000 | 439,731 | |
| | validation | 10,000 | 55,072 | |
| | test | 10,000 | 54,876 | |
| | **total** | **100,000** | **549,679** | |
|
|
| ## Why this repackaging exists |
|
|
| Upstream, this split is three-quarters of a million individual PNG and JPEG |
| files. That is unpleasant to move, impossible to browse, and slow to load. Here |
| each ROI is **one row** carrying its image, its combined mask, all of its |
| per-instance masks, and its bounding boxes — so the whole thing loads with one |
| call and renders in the dataset viewer. |
|
|
| ## Fields |
|
|
| | field | notes | |
| |---|---| |
| | `image` | the 400×400 grayscale ROI | |
| | `mask` | the combined mask for the ROI | |
| | `instance_masks` | list of per-instance masks, one per barcode | |
| | `boxes` | list of `[x1, y1, x2, y2]`, aligned index-for-index with `instance_masks` | |
| | `n_instances` | number of barcodes in the ROI | |
| | `roi_id` | upstream ROI index (`roi<N>.png` ↔ `img_<N>` in `all_bboxes.json`) | |
|
|
| ### The box↔mask alignment was verified, not assumed |
|
|
| `boxes[k]` and `instance_masks[k]` are the same object. The packer checked the |
| count of boxes against the count of instance-mask files for **every one of the |
| 100,000 ROIs and found zero mismatches**, and no ROI was missing its combined |
| mask. If they had disagreed, the join would have been silently wrong in a way no |
| loader would flag — so it is checked rather than trusted. |
|
|
| ## Usage |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("devmandan/sbd-qr-subset", split="train", streaming=True) |
| row = next(iter(ds)) |
| row["image"], row["mask"], row["instance_masks"][0], row["boxes"][0] |
| |
| # crowded ROIs |
| full = load_dataset("devmandan/sbd-qr-subset", split="test") |
| crowded = full.filter(lambda r: r["n_instances"] >= 8) |
| ``` |
|
|
| `streaming=True` is worth using here — the train split is ~3 GB. |
|
|
| ## Scope, stated plainly |
|
|
| This mirror carries the **`low_resolution` ROI split only**. The upstream SBD |
| release also has ultra-high-resolution splits and full-scene imagery that are |
| **not** included here. Do not describe results on this mirror as results on SBD. |
| |
| It is also a **barcode** dataset covering multiple symbologies, not a QR-only |
| one; it appears in a QR collection for its scale and its instance masks. |
| |
| ## Citation |
| |
| ```bibtex |
| @misc{sbd_synthetic_barcode_dataset, |
| title = {Synthetic Barcode Dataset (SBD)}, |
| author = {Quenum, Jerome and Wang, Kehan and Zakhor, Avideh}, |
| note = {Synthetic barcode detection and segmentation dataset} |
| } |
| ``` |
| |
| Licence: **CC BY 4.0** as recorded in the dataset registry (the authors' code |
| repository is separately MIT). Attribute the authors above, not this mirror. |
|
|
