---
license: apache-2.0
pretty_name: XMR Industrial Foreign Object Detection — Lentils (Hyperspectral)
task_categories:
  - object-detection
  - image-segmentation
tags:
  - hyperspectral
  - hsi
  - food-quality
  - food-safety
  - industrial-inspection
  - anomaly-detection
  - foreign-object-detection
  - lentils
  - cuvis
  - cubert
size_categories:
  - 1K<n<10K
---

# XMR Industrial Foreign Object Detection — Lentils (Hyperspectral)

**1,136 hyperspectral frames, 696 with pixel-level COCO annotations, 7 foreign-object
classes, 15 merged `.cu3s` capture sessions across 3 acquisition days.**

Companion dataset to the Cubert whitepaper *“Spectral Foreign Object Detection in
Lentils Using a Compact Hyperspectral Channel Selector”* (Raj, May 2026). The
dataset is the larger counterpart to the small demo at
[`cubert-gmbh/XMR_Demo_Industrial_Foreign_Object_Detection_Lentils`](https://huggingface.co/datasets/cubert-gmbh/XMR_Demo_Industrial_Foreign_Object_Detection_Lentils),
which contained a single 69-frame capture for the lentils tutorial notebook.

- 📄 Whitepaper: see `whitepaper/lentils_hsi_whitepaper_draft.md` (linked from Cubert AI documentation)
- 📦 Demo / tutorial: `cubert-gmbh/XMR_Demo_Industrial_Foreign_Object_Detection_Lentils`
- 📬 Contact: Anish Raj, Cubert GmbH — `raj@cubert-gmbh.de`

## Summary

| | |
|---|---|
| Total frames | **1,136** |
| Annotated frames | **696** (61.3 %) |
| Annotated foreign-object regions | **1,536** |
| Hyperspectral cubes (merged `.cu3s` files) | **15** |
| Spectral resolution | **61 bands · 430–910 nm · 8 nm spacing** |
| Spatial resolution | **1080 × 1000** |
| Processing mode | **Reflectance** (55 % gray reference + dark reference) |
| Splits | **train 808 · val 148 · test 180** (71.1 / 13.0 / 15.8 %) |
| Total size on disk | **~57 GB** |
| License | **Apache-2.0** |

### Per-day breakdown

| Day | Capture date | Subfolders | Frames | Annotated | Foreign-object regions |
|---|---|---:|---:|---:|---:|
| day2 | 2026-03-03 | 6 | 384 | 188 |   368 |
| day3 | 2026-03-10 | 6 | 492 | 328 |   648 |
| day4 | 2026-03-17 | 3 | 260 | 180 |   520 |
| **Total** | | **15** | **1,136** | **696** | **1,536** |

## Foreign-object classes

| id | name        | object count |
|---:|---          |---:|
| 0 | `Unlabeled`   | (background / normal lentils + belt) |
| 1 | `stem_k`      |  288 |
| 2 | `stone`       |  516 |
| 3 | `alu_shard`   |  112 |
| 4 | `blue_paper`  |   80 |
| 5 | `white_paper` |   60 |
| 6 | `fly`         |  420 |
| 7 | `rubber`      |   60 |

Class id 0 (`Unlabeled`) is the background and is implicit — pixels not covered by
any other category. Five subfolders contain only normal/background captures (no
foreign objects); their frames appear in `splits.csv` with `has_annotation=0`.

## Acquisition setup

- Camera: **Cubert XMR 50 mm** hyperspectral, operated through Cuvis Next
- Illumination: 4 halogen lamps
- Background: blue FDA-compliant conveyor-belt material (belt stationary during capture)
- Field of view: ≈12.5 × 12 cm at 46.6 cm working distance
- Exposure: 15 ms
- White reference: 55 % gray target; dark reference acquired by covering the lens
- Lentils: **Emershofer Beluga** and **Emershofer dark green marbled**

For every scene arrangement, **four captures under different lighting conditions**
form a grouped unit (`group_id` in `splits.csv`). All four images of a group are
always kept in the same train / val / test split to prevent lighting-only
information leakage between evaluation sets.

The setup is a lab proof-of-concept with production-relevant design elements,
not a full production deployment study. See the whitepaper §Limitations for the
caveats.

## Repository layout

```
README.md
LICENSE                                 (Apache-2.0)
splits.csv                              # primary split file — 1 row per saved frame
splits_verification.md                  # proof that splits.csv mirrors the asai2 reference
annotations_canonical/                  # reference: per-day concatenated COCO (time-ordered global ids)
  day2_global_coco.json
  day3_global_coco.json
  day4_global_coco.json
data/
  day2/
    <subfolder>.cu3s                    # hyperspectral cube (merged capture session)
    <subfolder>.info                    # sensor sidecar (frame indexing)
    <subfolder>.json                    # per-cu3s COCO annotations (image_ids are local 0..N-1)
    …                                   # 6 subfolders for day2
  day3/                                 # 6 subfolders for day3
  day4/                                 # 3 subfolders for day4
```

`<subfolder>` is the capture-session timestamp `YYYY_MM_DD_HH-MM-SS` (with `_1`/`_2`
suffix when the camera was restarted at the same wall-clock second).

### Per-`<subfolder>.json` COCO schema

Standard COCO with extra per-image fields for hyperspectral and traceability:

```jsonc
{
  "info": { "subfolder": "…", "day": "…", "frame_count": N, "annotation_count": M },
  "categories": [ { "id": 0..7, "name": "Unlabeled|stem_k|…|rubber" } ],
  "images": [
    {
      "id": <local_image_id>,           // 0..N-1, matches index inside the .cu3s
      "file_name": "<subfolder>.cu3s",
      "width": 1080, "height": 1000,
      "global_frame_id": <int>,         // 0..(day_total-1) — keys to splits.csv & canonical day COCO
      "camera_frame_num": <int>,        // raw camera frame counter (matches `.info`)
      "camera_name": "Auto_000_<n>"
    }
  ],
  "annotations": [
    { "id": …, "image_id": <local_image_id>, "category_id": 1..7,
      "bbox": [x, y, w, h], "segmentation": [[…polygon…]],
      "iscrowd": 0, "area": 0.0, "mask": {"counts": [], "size": []}, "auxiliary": {} }
  ]
}
```

The annotations are **semantic masks**, not instance-level. Individual objects
of the same class in the same frame share a polygon contour, not separate
instance ids.

### `splits.csv` columns

| column | meaning |
|---|---|
| `day` | `day2` / `day3` / `day4` |
| `subfolder` | capture-session timestamp |
| `cu3s_path` | path inside this repo, e.g. `data/day2/2026_03_03_13-58-04_2.cu3s` |
| `json_path` | matching per-cu3s COCO path |
| `local_image_id` | 0..N-1 inside the merged `.cu3s` |
| `global_image_id` | 0..(day_total-1), in time order across the whole day — join key to `annotations_canonical/day*_global_coco.json` |
| `camera_frame_num` | raw camera frame counter (matches `.info`) |
| `camera_name` | `Auto_000_<n>` — single-cu3s identifier used by the original asai2 split |
| `split` | `train` / `val` / `test` |
| `group_id` | 4-frame lighting-quad group; all 4 frames of a group share one split |
| `group_index` | 0..3, position inside the lighting quad |
| `has_annotation` | 1 if the frame contains any foreign-object annotation, else 0 |
| `category_labels` | semicolon-separated category ids present in the frame (empty for normal frames) |

## Example frames (loaded from this repo via `huggingface_hub`)

All three examples below were produced by downloading the `.cu3s` and matching
`.json` from this dataset on Hugging Face, reading the cube via `cuvis.SessionFile`
in `ProcessingMode.Reflectance`, and overlaying the COCO polygons directly. The
generating script is `make_examples_remote.py` in this repo's release notes.

### Train · no foreign object (normal/background)

`data/day2/2026_03_03_11-11-01.cu3s` · `image_id=0` · 0 annotations · split=`train`

| RGB composite (650 / 550 / 450 nm) | CIR composite (860 / 670 / 560 nm) |
|---|---|
| ![](assets/examples/2026_03_03_11-11-01_id0000_rgb.png) | ![](assets/examples/2026_03_03_11-11-01_id0000_cir.png) |

### Train · 1 foreign object (stone)

`data/day3/2026_03_10_10-58-55.cu3s` · `image_id=0` · 1 annotation (`stone`) · split=`train`

| RGB + annotation | CIR + annotation |
|---|---|
| ![](assets/examples/2026_03_10_10-58-55_id0000_rgb_annotated.png) | ![](assets/examples/2026_03_10_10-58-55_id0000_cir_annotated.png) |

### Train · 3 foreign objects (alu_shard + fly + stone)

`data/day4/2026_03_17_11-41-54.cu3s` · `image_id=40` · 3 annotations · split=`train`

| RGB + annotations | CIR + annotations |
|---|---|
| ![](assets/examples/2026_03_17_11-41-54_id0040_rgb_annotated.png) | ![](assets/examples/2026_03_17_11-41-54_id0040_cir_annotated.png) |

### Split-loader sanity check

The HF copy was verified to load the right frame for one example per split by
downloading the cu3s via `huggingface_hub` and asserting
`cuvis.SessionFile.get_measurement(splits.local_image_id).name` matches the
`camera_name` predicted by `splits.csv`:

| split | cu3s | `local_image_id` | expected `Auto_000_<n>` | got | ok |
|---|---|---:|---|---|---|
| train | `data/day2/2026_03_03_11-11-01.cu3s` |  0 | `Auto_000_4261` | `Auto_000_4261` | ✅ |
| val   | `data/day2/2026_03_03_11-31-31.cu3s` | 14 | `Auto_000_1339` | `Auto_000_1339` | ✅ |
| test  | `data/day3/2026_03_10_10-58-55.cu3s` | 12 | `Auto_000_1370` | `Auto_000_1370` | ✅ |

Polygon-bounds sanity: every annotation polygon vertex in the loaded frames
lies inside the image rectangle `(0..1080, 0..1000)`. No clipping needed.

## Splits

| split | frames | annotated frames | objects |
|---|---:|---:|---:|
| train      | 808 | 500 | — |
| validation | 148 |  84 | — |
| test       | 180 | 112 | — |

The split was originally generated on the single-cu3s form of the data using
stratified group-aware splitting (lighting quads kept intact, category balance
preserved across splits). The `splits.csv` file in this repo remaps each
single-cu3s row to its position inside the corresponding merged
`.cu3s` file. See `splits_verification.md` for the seven-check proof that this
remapping is bit-faithful (coverage, per-day counts, per-subfolder counts,
annotation equivalence, split distribution, no-group-leakage, and a physical
round-trip through `cuvis.SessionFile`).

## How to load

### List the test set

```python
import csv
from huggingface_hub import hf_hub_download

splits_csv = hf_hub_download(
    repo_id="cubert-gmbh/XMR_Industrial_Foreign_Object_Detection_Lentils",
    repo_type="dataset",
    filename="splits.csv",
)
with open(splits_csv) as f:
    rows = [r for r in csv.DictReader(f) if r["split"] == "test"]
print(len(rows), "test frames")
```

### Stream one cu3s + annotations

```python
from huggingface_hub import hf_hub_download

repo = "cubert-gmbh/XMR_Industrial_Foreign_Object_Detection_Lentils"
sub  = "data/day4/2026_03_17_11-11-50"
cu3s = hf_hub_download(repo_id=repo, repo_type="dataset", filename=f"{sub}.cu3s")
info = hf_hub_download(repo_id=repo, repo_type="dataset", filename=f"{sub}.info")
js   = hf_hub_download(repo_id=repo, repo_type="dataset", filename=f"{sub}.json")

import cuvis, json
cuvis.init()
sess = cuvis.SessionFile(cu3s)
print("frames in cube:", sess.get_size())
mesu = sess.get_measurement(0)
print("cube shape:", mesu.cube.array.shape)  # (1000, 1080, 61)

anns = json.load(open(js))
print("annotated frames:", sum(any(a['image_id']==im['id'] for a in anns['annotations'])
                                for im in anns['images']))
```

### Mirror everything to a local directory

```bash
huggingface-cli download \
  cubert-gmbh/XMR_Industrial_Foreign_Object_Detection_Lentils \
  --repo-type=dataset \
  --local-dir=./lentils_full \
  --local-dir-use-symlinks=False
```

Or programmatically with `snapshot_download(...)` and `allow_patterns=` to fetch
only specific days / files.

## Citation

```bibtex
@techreport{raj2026lentilshsi,
  title  = {Spectral Foreign Object Detection in Lentils Using a Compact Hyperspectral Channel Selector},
  author = {Raj, Anish},
  institution = {Cubert GmbH},
  year   = {2026},
  note   = {Whitepaper, draft v0.1, May 2026}
}
```

## License

This dataset is released under the [**Apache License 2.0**](https://www.apache.org/licenses/LICENSE-2.0).

You are free to use, modify, distribute, and redistribute this dataset for any
purpose, including commercial use, subject to the terms of the Apache 2.0
license — primarily, retention of the copyright/license notice and a
NOTICE-style attribution if you publish derivative works. Citing the whitepaper
above is appreciated.

This matches the licensing used across other Cubert public datasets on Hugging
Face.