Datasets:

cubert-gmbh
/

XMR_Industrial_Foreign_Object_Detection_Lentils

license: cc-by-4.0
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, 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	CC BY 4.0

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                                 (CC BY 4.0)
splits.csv                              # primary split file — 1 row per saved frame
splits_verification.md                  # proof that splits.csv mirrors the asai2 reference
asai2_singlefile_splits.csv             # the original single-cu3s split, preserved for audit
dataset_summary.json                    # copied from whitepaper/dataset_summary.json
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:

{
  "info": { "subfolder": "…", "day": "…", "frame_count": N, "annotation_count": M },
  "licenses": [],
  "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,
      "channels": 0, "wavelength": [],
      "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` and to `asai2_singlefile_splits.csv`
`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)

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 original single-cu3s assignment is preserved verbatim in asai2_singlefile_splits.csv. 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

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

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

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

@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 Creative Commons Attribution 4.0 International (CC BY 4.0) license.

You are free to share and adapt the material for any purpose, even commercially, provided you give appropriate credit (cite the whitepaper above and link to this dataset), indicate if changes were made, and do not apply legal or technological restrictions that prevent others from doing the same.

The whitepaper draft mentioned Apache 2.0 as the planned license; the final choice for the dataset is CC BY 4.0 (the more standard license for data), chosen by the dataset author.