---
license: other
license_name: per-source-research-terms
license_link: https://huggingface.co/datasets/FrenchCastle/xray-baggages-customs#licensing
pretty_name: MIRO X-ray Security Scans (Baggage)
task_categories:
- object-detection
tags:
- x-ray
- security-screening
- customs
- baggage
- prohibited-items
- coco
- benchmark
annotations_creators:
- found
source_datasets:
- extended|clcxray
- extended|dvxray
- extended|hixray
- extended|opixray
- extended|sixray_yolo_subset
size_categories:
- 100K<n<1M
viewer: false
extra_gated_heading: Access to the MIRO X-ray security scans (baggage)
extra_gated_prompt: >-
  This collection redistributes images and annotations from third-party X-ray datasets
  under their original terms, which differ per source. By requesting
  access you agree to follow the terms of each source dataset listed on this card, to cite
  the original authors, and not to use the data to develop methods for evading security
  screening.
extra_gated_fields:
  Name: text
  Affiliation: text
  I will use this dataset for non-commercial research only and follow each source's terms: checkbox
---

# MIRO X-ray Security Scans: Baggage

109,944 baggage X-ray scans with 170,827 bounding boxes, gathered from 5
public X-ray security datasets and relabelled into one taxonomy, in COCO format, with
per-image provenance and licence. The cargo scans live in the sibling repository [FrenchCastle/miro-xray-cargo](https://huggingface.co/datasets/FrenchCastle/miro-xray-cargo), with the same layout and taxonomy.

The collection was built for MIRO-VLM, a project that evaluates vision-language models as
assistants to customs and security officers: reading a scan, finding threats and
checking the goods against the declaration. Each source keeps its own split and its
own terms; nothing is pooled across sources without a `dataset` field to tell them apart.

> **Licensing in one line.** This is a redistribution of third-party data under their
> original terms, which differ per source; several are **academic, non-commercial use only**. Read
> [Licensing](#licensing) before use and cite the original authors.

## Quick start

Download everything (metadata and all image zips), then unpack:

```bash
hf download FrenchCastle/xray-baggages-customs --repo-type dataset --local-dir xray_baggage
cd xray_baggage && for z in images_*.zip; do unzip -q "$z" && rm "$z"; done
```

Or only one source:

```bash
hf download FrenchCastle/xray-baggages-customs --repo-type dataset --local-dir xray_baggage \
  --include "annotations/clcxray_coco.json" "images_clcxray.zip" MANIFEST.csv
```

Load it with `pycocotools` (any COCO reader works):

```python
from pycocotools.coco import COCO

coco = COCO("xray_baggage/annotations/clcxray_coco.json")
img = coco.loadImgs(coco.getImgIds()[0])[0]  # file_name is relative to xray_baggage/
boxes = coco.loadAnns(coco.getAnnIds(imgIds=img["id"]))
print(img["file_name"], img["split"], [(coco.cats[b["category_id"]]["name"], b["bbox"]) for b in boxes])
```

The Hub dataset viewer is off: the images ship as zips so that each source can be
fetched, cited and removed independently.

## Dataset structure

### Sources and splits

| source | images | splits | boxes | images without boxes | zip |
|---|---:|---|---:|---:|---:|
| [CLCXray](https://github.com/GreysonPhoenix/CLCXray) | 9,564 | test 956, train 7,652, val 956 | 22,111 | 0 | 6.4 GB |
| [DvXray](https://github.com/Mbwslib/DvXray) | 32,000 | test 3,180, train 25,612, val 3,208 | 10,583 | 22,400 | 7.2 GB |
| [HiXray](https://github.com/HiXray-author/HiXray) | 45,364 | test 9,069, train 36,295 | 102,928 | 0 | 6.8 GB |
| [OPIXray](https://github.com/OPIXray-author/OPIXray) | 8,885 | test 1,776, train 7,109 | 8,919 | 0 | 2.6 GB |
| [SIXray (Kaggle YOLO subset)](https://www.kaggle.com/datasets/khanhbtq99/sixray) | 14,131 | test 831, train 11,638, val 1,662 | 26,286 | 119 | 909.4 MB |

Splits are the ones each source publishes (or, where a source has none, the one its
distributor provides); they are not re-drawn. Use them per source. There is no
cross-source test set, and no de-duplication across sources was attempted.

### Files

| path | content |
|---|---|
| `annotations/<source>_coco.json` | COCO detection file per source, unified class names |
| `images_<source>.zip` | images of that source, stored (uncompressed); members are `images/<source>/...`, exactly the COCO `file_name` |
| `MANIFEST.csv` | one row per image: `image_id, file_name, dataset, domain, split, license, width, height, n_boxes` |
| `label_report.json` | per source, original label to unified class |
| `classes_mapping.json` | the normalisation table and the unified class ids |

### COCO fields

Standard COCO detection, with extra keys:

- `images[]`: `id`, `file_name`, `width`, `height`, plus `dataset` (source key),
  `domain` (`baggage`), `split` (`train` / `val` / `test`) and `license` (the source's terms).
- `annotations[]`: `id`, `image_id`, `category_id`, `bbox` (`[x, y, width, height]`, pixels),
  `area`, `iscrowd`, `segmentation` where the source provides masks or polygons, and
  `attributes.original_label`, the label as the source wrote it.
- `categories[]`: the full unified taxonomy, shared by both domain repositories, so
  class ids are stable across files. Classes absent from a file simply have no boxes.

Images without boxes are negatives (nothing of interest annotated), kept as the
source ships them.

## Taxonomy

Source labels are mapped to one taxonomy: threats and declarable items
(`knife_cutter`, `firearm`, `battery_powerbank`, `liquid_container`...) and, for cargo,
goods categories prefixed `cargo_`. The original label is always kept in
`attributes.original_label`, so any other grouping can be rebuilt.

### Class counts in this repository

| class | boxes | images containing it |
|---|---:|---:|
| `phone` | 53,835 | 37,430 |
| `battery_powerbank` | 20,935 | 18,017 |
| `knife_cutter` | 18,900 | 14,774 |
| `liquid_container` | 15,366 | 10,750 |
| `laptop` | 10,042 | 9,602 |
| `cosmetic` | 9,949 | 7,662 |
| `pliers` | 9,625 | 7,290 |
| `firearm` | 8,062 | 5,417 |
| `scissors` | 6,913 | 6,360 |
| `wrench` | 5,385 | 4,092 |
| `tablet` | 4,918 | 4,719 |
| `sprayer` | 1,776 | 1,650 |
| `lighter` | 1,650 | 1,553 |
| `hammer` | 749 | 741 |
| `baton` | 714 | 714 |
| `screwdriver` | 690 | 682 |
| `dart` | 660 | 652 |
| `fireworks` | 658 | 649 |

### Label mapping

<details>
<summary>Original label to unified class, per source</summary>

| source | original label | unified class |
|---|---|---|
| CLCXray | `Cans` | `liquid_container` |
| CLCXray | `CartonDrinks` | `liquid_container` |
| CLCXray | `GlassBottle` | `liquid_container` |
| CLCXray | `PlasticBottle` | `liquid_container` |
| CLCXray | `SprayCans` | `sprayer` |
| CLCXray | `SwissArmyKnife` | `knife_cutter` |
| CLCXray | `Tin` | `liquid_container` |
| CLCXray | `VacuumCup` | `liquid_container` |
| CLCXray | `blade` | `knife_cutter` |
| CLCXray | `dagger` | `knife_cutter` |
| CLCXray | `knife` | `knife_cutter` |
| CLCXray | `scissors` | `scissors` |
| DvXray | `Bat` | `baton` |
| DvXray | `Battery` | `battery_powerbank` |
| DvXray | `Dart` | `dart` |
| DvXray | `Fireworks` | `fireworks` |
| DvXray | `Gun` | `firearm` |
| DvXray | `Hammer` | `hammer` |
| DvXray | `Knife` | `knife_cutter` |
| DvXray | `Lighter` | `lighter` |
| DvXray | `Pliers` | `pliers` |
| DvXray | `Pressure_vessel` | `sprayer` |
| DvXray | `Razor_blade` | `knife_cutter` |
| DvXray | `Saw_blade` | `knife_cutter` |
| DvXray | `Scissors` | `scissors` |
| DvXray | `Screwdriver` | `screwdriver` |
| DvXray | `Wrench` | `wrench` |
| HiXray | `Cosmetic` | `cosmetic` |
| HiXray | `Laptop` | `laptop` |
| HiXray | `Mobile_Phone` | `phone` |
| HiXray | `Nonmetallic_Lighter` | `lighter` |
| HiXray | `Portable_Charger_1` | `battery_powerbank` |
| HiXray | `Portable_Charger_2` | `battery_powerbank` |
| HiXray | `Tablet` | `tablet` |
| HiXray | `Water` | `liquid_container` |
| OPIXray | `Folding_Knife` | `knife_cutter` |
| OPIXray | `Multi-tool_Knife` | `knife_cutter` |
| OPIXray | `Scissor` | `scissors` |
| OPIXray | `Straight_Knife` | `knife_cutter` |
| OPIXray | `Utility_Knife` | `knife_cutter` |
| SIXray (Kaggle YOLO subset) | `Gun` | `firearm` |
| SIXray (Kaggle YOLO subset) | `Knife` | `knife_cutter` |
| SIXray (Kaggle YOLO subset) | `Pliers` | `pliers` |
| SIXray (Kaggle YOLO subset) | `Scissors` | `scissors` |
| SIXray (Kaggle YOLO subset) | `Wrench` | `wrench` |

</details>

## Licensing

There is no single licence: each image keeps its source's terms, recorded in the
`license` field of the COCO image and in `MANIFEST.csv`. [Source datasets](#source-datasets)
states the terms as each source publishes them. When in doubt, the upstream terms prevail, and
anything beyond non-commercial research needs the source authors' permission. The
annotation conversion and the label mapping added here are released under CC BY 4.0.

If you are an author of one of these datasets and want your data removed or its
terms described differently, open a discussion on this repository and it will be
handled promptly.

## Source datasets

### CLCXray (`clcxray`)

Cutters and liquid containers: subway-station scans plus scans of manually packed bags.

- Upstream: https://github.com/GreysonPhoenix/CLCXray
- Paper: https://ieeexplore.ieee.org/document/9722843
- Terms: Academic use only.
- Caveats: About half of the images are staged rather than stream-of-commerce.

### DvXray (`dvxray`)

Dual-view baggage scans: 16,000 pairs (two orthogonal views), 15 prohibited item classes, positives and negatives.

- Upstream: https://github.com/Mbwslib/DvXray
- Paper: https://ui.adsabs.harvard.edu/abs/2024ITIF...19.3866M/abstract
- Terms: Academic use only.
- Caveats: Both views of a pair are separate images here; keep pairs on the same side of any split you make. Most images are negatives.

### HiXray (`hixray`)

Real airport scans of passenger baggage with everyday items that must be declared (phones, laptops, power banks, cosmetics, liquids, lighters).

- Upstream: https://github.com/HiXray-author/HiXray
- Paper: https://openaccess.thecvf.com/content/ICCV2021/html/Tao_Towards_Real-World_X-Ray_Security_Inspection_A_High-Quality_Benchmark_and_Lateral_ICCV_2021_paper.html
- Terms: Academic use only; the authors ask for a signed commitment for formal use.
- Caveats: Heavily dominated by phones and power banks.

### OPIXray (`opixray`)

Airport baggage scans with 5 kinds of cutters, many occluded.

- Upstream: https://github.com/OPIXray-author/OPIXray
- Paper: https://arxiv.org/abs/2004.08656
- Terms: Academic use only (OPIXray terms).
- Caveats: Only knife and scissors classes; every image contains an item.

### SIXray (Kaggle YOLO subset) (`sixray_yolo_subset`)

A community YOLO-format subset of SIXray: subway baggage scans with guns, knives, wrenches, pliers and scissors.

- Upstream: https://www.kaggle.com/datasets/khanhbtq99/sixray
- Paper: https://openaccess.thecvf.com/content_CVPR_2019/html/Miao_SIXray_A_Large-Scale_Security_Inspection_X-Ray_Benchmark_for_Prohibited_Item_CVPR_2019_paper.html
- Terms: Academic use only (SIXray terms, https://github.com/MeioJane/SIXray).
- Caveats: Community re-annotation and re-split of SIXray, resized to at most 640 px; not the official SIXray release or split.

## Dataset creation

**Curation rationale.** Public X-ray security datasets are scattered across Google
Drive, Baidu, Kaggle, Roboflow and Hugging Face, in half a dozen annotation formats
and label vocabularies. Evaluating a model across them first needs one format, one
taxonomy and exact provenance for every image.

**Processing.** Each source was downloaded from its official or documented
distribution, converted to COCO (from VOC XML, YOLO txt, paired txt or COCO), and its
labels mapped to the unified taxonomy through a fixed table (`classes_mapping.json`).
Images are the downloaded files, byte for byte: this repository does not resize,
recompress or filter them (a distributor upstream may have, as the caveats note).
Large sources may be a class-balanced subset, as stated in their caveats. Splits are kept as distributed.

**Annotations.** All boxes come from the source datasets (see each paper for its
annotation protocol). This repository adds no new manual annotation; it renames
labels and records the original.

**Personal and sensitive information.** The images are X-ray transmission scans of
bags and cargo; they contain no faces, names or documents. Some sources are real
stream-of-commerce scans from airports or subway stations; none is known to include
personal data.

## Considerations for use

**Intended use.** Research on detection and understanding of objects in X-ray
security imagery: benchmarking detectors and vision-language models, studying
domain shift between scanners and between baggage and cargo, and building tools that
assist human inspectors.

**Out of scope.** Certifying or operating a screening system; any use that would
help conceal items from X-ray inspection; commercial use of sources whose terms
forbid it.

**Known biases and limitations.**

- Scanners, colour palettes and resolutions differ per source, and a model can learn
  the source instead of the object. Evaluate per source, or across sources on purpose.
- Class balance follows the sources: some classes come from a single source (and
  therefore a single scanner).
- Some sources are staged (items packed for the dataset) or synthetic (threats
  composited into real scans); these are not stream-of-commerce distributions.
- Near-duplicates exist in some sources (augmented copies, dual views); see caveats.
- Label granularity is reduced by the mapping (for example, every blade type becomes
  `knife_cutter`); use `attributes.original_label` for the finer label.

## Citation

Cite the original datasets you use, and this consolidation if it helped:

```bibtex
@misc{chastel2026miroxray_baggage,
  title        = {MIRO X-ray Security Scans (Baggage): a unified COCO consolidation of public X-ray datasets},
  author       = {Chastel, Fran\c{c}ois},
  year         = {2026},
  publisher    = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/FrenchCastle/xray-baggages-customs}}
}

@article{zhao2022clcxray,
  title   = {Detecting Overlapped Objects in X-Ray Security Imagery by a Label-Aware Mechanism},
  author  = {Zhao, Cairong and Zhu, Liang and Dou, Shuguang and Deng, Weihong and Wang, Liang},
  journal = {IEEE Transactions on Information Forensics and Security},
  volume  = {17},
  pages   = {998--1009},
  year    = {2022}
}

@article{ma2024dvxray,
  title   = {Toward Dual-View X-Ray Baggage Inspection: A Large-Scale Benchmark and Adaptive Hierarchical Cross Refinement for Prohibited Item Discovery},
  author  = {Ma, Bowen and Jia, Tong and Li, Mingyuan and Wu, Songsheng and Wang, Hao and Chen, Dongyue},
  journal = {IEEE Transactions on Information Forensics and Security},
  volume  = {19},
  year    = {2024}
}

@inproceedings{tao2021hixray,
  title     = {Towards Real-World X-ray Security Inspection: A High-Quality Benchmark and Lateral Inhibition Module for Prohibited Items Detection},
  author    = {Tao, Renshuai and Wei, Yanlu and Jiang, Xiangjian and Li, Hainan and Qin, Haotong and Wang, Jiakai and Ma, Yuqing and Zhang, Libo and Liu, Xianglong},
  booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
  pages     = {10923--10932},
  year      = {2021}
}

@inproceedings{wei2020opixray,
  title     = {Occluded Prohibited Items Detection: An X-ray Security Inspection Benchmark and De-occlusion Attention Module},
  author    = {Wei, Yanlu and Tao, Renshuai and Wu, Zhangjie and Ma, Yuqing and Zhang, Libo and Liu, Xianglong},
  booktitle = {Proceedings of the 28th ACM International Conference on Multimedia},
  pages     = {138--146},
  year      = {2020}
}

@inproceedings{miao2019sixray,
  title     = {{SIXray}: A Large-scale Security Inspection X-ray Benchmark for Prohibited Item Discovery in Overlapping Images},
  author    = {Miao, Caijing and Xie, Lingxi and Wan, Fang and Su, Chi and Liu, Hongye and Jiao, Jianbin and Ye, Qixiang},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  pages     = {2119--2128},
  year      = {2019}
}
```

## Maintenance

Maintained by François Chastel ([FrenchCastle](https://huggingface.co/FrenchCastle)).
Report problems, missing credits or licence questions in the Community tab of this
repository.