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Datasets:
cubert-gmbh
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XMR_Industrial_Foreign_Object_Detection_Lentils

Tasks:
Object Detection
Image Segmentation
Size:
1K<n<10K
Tags:
hyperspectral
hyperspectral-imaging
anomaly-detection
foreign-object-detection
food-safety
food-quality
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metadata 
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, 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:

{
  "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)

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

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

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 

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 Apache 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.