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186-grounding
Detection-format defect localization on magnetic tiles — 1,340 items (952 good + 388 defective),
derived deterministically from the pixel saliency masks of
AI4Manufacturing/186. The model must output
boxes as text; defect-free tiles must output [] — detection rejection is part of the task.
Task
"Locate every defect." annot is a JSON list of {"type": ..., "bbox_xywh": [x, y, w, h]} in
native pixel coordinates (origin top-left), one entry per defect instance (connected
components after proximity grouping: dilation radius ~1% of min dimension merges fragments of one
physical defect; sub-15-px groups denoised but counted — 14; if all of a record's groups fall under
the floor its union box is emitted instead — 2 records), sorted (type, x, y). Good tiles have
annot = [] (952). The query states the coordinate convention, the closed class list
(Blowhole, Break, Crack, Fray, Uneven), and the empty-list rule. Query pool: 24 surface variants
(template x good/defective independence: worst z = 1.88).
Uneven disclosure. Uneven (grind-unevenness) has GRADUAL boundaries — its mask is a saliency
region, not a sharp contour. Every Uneven row carries metadata.coarse_boundary: true; grade Uneven
localization by containment / center-hit, never tight IoU. All other classes have sharp
boundaries and tight boxes.
| field | type | meaning |
|---|---|---|
query |
str | 24 surface variants; closed class list; JSON output spec |
image |
Image | the raw grayscale tile photo (no overlays) |
annot |
str | JSON box list (see above), [] when defect-free |
reasoning |
null | none — deterministic derivation |
cate / task |
str | B / T-B2 |
metadata |
str (JSON) | source, category, image_sha256, image_path, r186_record_id, defect_type, n_instances, coarse_boundary |
Verification: every published box list re-derived independently from the mask at assembly —
byte-identical on all 1,340 rows; goods all [].
Provenance
Built deterministically (no LLM/teacher; reasoning is null) from
AI4Manufacturing/186 (revision 2117f8e) —
Magnetic-Tile-Defect, Huang et al., "Surface defect saliency of magnetic tile", The Visual Computer 2020:
1,344 grayscale magnetic-tile images, 5 defect classes (Blowhole, Break, Crack, Fray, Uneven) + good,
each defective image with a paired pixel saliency mask (binarized here at gray>40, which matches the
source defect_area_fraction). Generator: annotate/186/build_186_derived.py in forge_model;
machine gates: annotate/186/verify_186.py (all green at build time).
Source-data exclusion (counted): 4 MT_Uneven rows ship ALL-ZERO masks in the source dataset
(defect_area_fraction = 0.0) — an anomalous label with no localizable GT. They are excluded from
every derived set.
Query diversity. The query field is drawn from a fixed pool of surface variants for this task
(paraphrases preserving the task and answer format), selected by an independent per-record hash.
A machine gate checks that no template correlates with the gold (worst z-scores reported above).
The repository name is an internal task code (the source dataset's code is
186).
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