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+---
+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`](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 | **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:
+
+```jsonc
+{
+  "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
+
+```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 [**Creative Commons Attribution 4.0 International (CC BY 4.0)**](https://creativecommons.org/licenses/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.