--- pretty_name: XMR Industrial Foreign-Object Detection — Lentils (Hyperspectral, Full) license: apache-2.0 task_categories: - object-detection - image-segmentation size_categories: - 1K Cubert Hyperspectral

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# Hyperspectral Foreign-Object Detection in Lentils — Full Dataset The larger counterpart to the small tutorial demo at [`cubert-gmbh/XMR_Demo_Industrial_Foreign_Object_Detection_Lentils`](https://huggingface.co/datasets/cubert-gmbh/XMR_Demo_Industrial_Foreign_Object_Detection_Lentils). Captured with a Cubert [**Ultris XMR**](https://cubert-hyperspectral.com/de/ultris-xmr/) camera — **61 bands per pixel, 430–910 nm, 1080 × 1000 pixels**. Three acquisition days, **15 merged `.cu3s` capture sessions**, **1,136 frames** total, **696 frames** with pixel-level COCO annotations across **7 foreign-object classes**. Foreign-object detection in food sorting is an industrial-inspection problem — the rejected target could be a stone, a stem, a piece of packaging, a metal shard, or an insect. In this dataset the bulk product is bag-grade lentils (Emershofer Beluga and dark green marbled). Contaminants span seven classes (`stem_k`, `stone`, `alu_shard`, `blue_paper`, `white_paper`, `fly`, `rubber`). The same hyperspectral pipeline carries over to any product whose foreign objects differ spectrally from the bulk — even when they look near-identical in visible RGB. ## 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 implicit background. Five subfolders are **normal/background captures** (no foreign objects); their frames appear in `splits.csv` with `has_annotation=0` and contribute to the splits as normal-class examples for SSL / unsupervised methods. ## Why hyperspectral An RGB sensor collapses incoming light into three bands; the human eye does the same. Hyperspectral video records **61 continuous bands per pixel, per frame** — a material fingerprint that separates dyes, fabrics, coatings, pigments, organic-vs-mineral matter, and surface chemistry. Foreign objects that are colour-matched to the bulk product (small stones in brown lentils, aluminium shards under warm lighting) are often near-isoluminant in visible RGB. They typically reveal themselves in the near-infrared (different surface scattering, different moisture content) or in narrow visible bands the eye can't resolve. The three views below show the same frame rendered through three 3-channel projections of the 61-band cube (per-channel min-max, uint8). Bands chosen with [`cuvis_ai.node.channel_selector`](https://github.com/cubert-hyperspectral/cuvis-ai/blob/main/cuvis_ai/node/channel_selector.py) classes `FixedWavelengthSelector` (defaults `650 / 550 / 450 nm`) and `CIRSelector` (defaults NIR=860, R=670, G=560 nm). ## Example frames All examples were rendered by downloading the `.cu3s` + `.json` from this dataset on Hugging Face, applying `cuvis.ProcessingContext(sf).processing_mode = ProcessingMode.Reflectance`, picking the canonical band indices via `cuvis_ai`'s `FixedWavelengthSelector` (RGB) and `CIRSelector` (CIR), min-max-normalising each channel to `[0, 255]` and saving as PNG. ### Train · 1 foreign object (`stone`) `data/day3/2026_03_10_10-58-55.cu3s` · `image_id=0` · `split=train` · 1 annotation (`stone`) | RGB composite | RGB + annotation | CIR composite | CIR + annotation | |:---:|:---:|:---:|:---:| | ![](assets/examples/2026_03_10_10-58-55_id0000_rgb_minmax_u8.png) | ![](assets/examples/2026_03_10_10-58-55_id0000_rgb_annotated.png) | ![](assets/examples/2026_03_10_10-58-55_id0000_cir_minmax_u8.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` · `split=train` · 3 annotations | RGB composite | RGB + annotations | CIR composite | CIR + annotations | |:---:|:---:|:---:|:---:| | ![](assets/examples/2026_03_17_11-41-54_id0040_rgb_minmax_u8.png) | ![](assets/examples/2026_03_17_11-41-54_id0040_rgb_annotated.png) | ![](assets/examples/2026_03_17_11-41-54_id0040_cir_minmax_u8.png) | ![](assets/examples/2026_03_17_11-41-54_id0040_cir_annotated.png) | ### Train · normal / background (no foreign objects) `data/day2/2026_03_03_11-11-01.cu3s` · `image_id=0` · `split=train` · 0 annotations | RGB composite | CIR composite | |:---:|:---:| | ![](assets/examples/2026_03_03_11-11-01_id0000_rgb_minmax_u8.png) | ![](assets/examples/2026_03_03_11-11-01_id0000_cir_minmax_u8.png) | ### Split-loader sanity check Verified by downloading the cu3s via `huggingface_hub`, opening with `cuvis.SessionFile`, and asserting `get_measurement(splits.local_image_id).name` matches the `camera_name` predicted by `splits.csv`: | split | cu3s | `local_image_id` | expected | 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 `(0..1080, 0..1000)`. See [`splits_verification.md`](splits_verification.md) for the full seven-check audit (coverage, per-day, per-subfolder, annotation equivalence, split distribution, no-group-leakage, physical round-trip). ## Acquisition setup - Camera: **Cubert Ultris XMR** hyperspectral, operated through Cuvis Next - Illumination: 4 halogen lamps in 4 configurations (`l0`–`l3`) per scene arrangement - 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 each 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. The setup is a lab proof-of-concept with production-relevant design elements, not a full production deployment study. See the [**whitepaper PDF**](whitepaper/lentils_hsi_whitepaper.pdf) for the full acquisition protocol, method comparison (RGB AdaCLIP / finetuned AdaCLIP / Dinomaly with custom selector), and limitations discussion. ## Repository layout ``` README.md LICENSE (Apache-2.0) .gitattributes (LFS for *.cu3s) 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) day{2,3,4}_global_coco.json assets/ examples/ # rendered example frames (see Example frames above) whitepaper/ lentils_hsi_whitepaper.pdf # full whitepaper PDF lentils_hsi_whitepaper.md # markdown source data/ day2/ .cu3s # merged hyperspectral cube (capture session) .info # sensor sidecar (frame indexing) .json # per-cu3s COCO annotations (image_ids are local 0..N-1) _README.md # data log for this capture session … # 6 subfolders for day2 day3/ # 6 subfolders for day3 day4/ # 3 subfolders for day4 ``` `` 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-`.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": , // 0..N-1, matches index inside the .cu3s "file_name": ".cu3s", "width": 1080, "height": 1000, "global_frame_id": , // 0..(day_total-1) — keys to splits.csv & canonical day COCO "camera_frame_num": , // raw camera frame counter (matches `.info`) "camera_name": "Auto_000_" } ], "annotations": [ { "id": …, "image_id": , "category_id": 1..7, "bbox": [x, y, w, h], "segmentation": [[…polygon…]], "iscrowd": 0, "area": 0.0, "mask": {"counts": [], "size": []}, "auxiliary": {} } ] } ``` 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_` — single-cu3s identifier used by the original stratified 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 | normal/background | |---|---:|---:|---:| | train | 808 | 500 | 308 | | val | 148 | 84 | 64 | | test | 180 | 112 | 68 | 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). `splits.csv` in this repo remaps each single-cu3s row to its position inside the corresponding merged `.cu3s`. See [`splits_verification.md`](splits_verification.md) for the seven-check audit proving this remapping is bit-faithful. ## 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: test_rows = [r for r in csv.DictReader(f) if r["split"] == "test"] print(len(test_rows), "test frames") ``` ### Stream one cu3s + annotations and render an RGB composite ```python from huggingface_hub import hf_hub_download import json, cuvis, numpy as np from PIL import Image 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") js = hf_hub_download(repo_id=repo, repo_type="dataset", filename=f"{sub}.json") cuvis.init() # or cuvis.init("/path/to/cuvis/user/settings") sf = cuvis.SessionFile(cu3s) m = sf.get_measurement(0) # Cubes are stored in Preview mode; convert to Reflectance for analysis: ctx = cuvis.ProcessingContext(sf) ctx.processing_mode = cuvis.ProcessingMode.Reflectance ctx.apply(m) cube = m.cube.array # shape (1000, 1080, 61), dtype uint16 wl = list(m.cube.wavelength) # 430..910 nm # RGB composite (FixedWavelengthSelector defaults — 650 / 550 / 450 nm) RGB = (650, 550, 450) idx = [int(np.argmin(np.abs(np.asarray(wl) - t))) for t in RGB] sel = cube[..., idx].astype(np.float32) u8 = np.zeros_like(sel, dtype=np.uint8) for c in range(3): lo, hi = np.percentile(sel[..., c], (0.5, 99.5)) u8[..., c] = (np.clip((sel[..., c] - lo) / max(hi - lo, 1e-6), 0, 1) * 255).astype(np.uint8) Image.fromarray(u8, "RGB").save("frame_rgb.png") anns = json.load(open(js)) print("frames:", len(anns["images"]), "annotations:", len(anns["annotations"])) ``` ### 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 ``` Or programmatically with `huggingface_hub.snapshot_download(...)` using `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, May 2026}, url = {https://huggingface.co/datasets/cubert-gmbh/XMR_Industrial_Foreign_Object_Detection_Lentils/resolve/main/whitepaper/lentils_hsi_whitepaper.pdf} } ``` ## License Released under the **Apache License 2.0** — see [`LICENSE`](LICENSE). Matches the licensing of other Cubert public datasets on Hugging Face. ## Contact Recorded and processed by the AI Team @ [Cubert](mailto:cuvis.ai@cubert-gmbh.de). Reach out for collaboration, evaluation pilots, or to discuss running this methodology on your own product line. - Author: **Anish Raj** — - Team: