tags:
- smart-manufacturing
- sft
- industrial
- vision
license: other
pretty_name: 186-mcq
extra_gated_fields:
Name: text
Affiliation: text
Intended use: text
extra_gated_prompt: >-
This dataset is released for **research use**. Access is reviewed and granted
**manually** by the maintainers. Please state your name, affiliation, and
intended use.
186-mcq
Mask-grounded multiple choice (Set-of-Mark style) for magnetic-tile defect localization —
244 items, derived deterministically from the pixel saliency masks of
AI4Manufacturing/186. Exact-match gradable
→ SFT and RLVR-ready.
Task
One item per eligible defective record. The image is a 2×2 grid of views A–D of the same
tile photo; each view overlays one red candidate region mask (SoM style: translucent fill +
outline + corner letter). Exactly one view overlays the true defect mask — in both location and
extent (the query says so). annot is the correct letter. The three negatives per item are hard by
construction, drawn from: shift (translated by ~0.7–1.6× bbox extents), fliplr/flipud/rot180
(mirrored), dilate (over-grown ≥2.5×, bounded), erode (shrunk <60%). This dataset has ONE defect
type per image, so there is no othertype negative. Every negative is guaranteed wrong (IoU vs truth
< 0.35 except dilate, wrong by extent) and panels are mutually distinct (pairwise IoU < 0.7).
Negative kinds are assigned to slots by an independent salted hash (arrangement encodes nothing).
Gold letters: A 53 / B 61 / C 58 / D 72 (chi² = 3.18, at chance). Query pool: 16 variants.
Exclusions (counted, confidence-over-coverage): 148 of 392 defective records were skipped —
gold overlay under ~30 visible px after panel downscale (37, mostly the smallest Blowholes), fewer
than 3 sound visible negatives constructible (77), mask covering >35% of the frame (30, large
Uneven/Fray — no sane negatives), source empty-mask Uneven rows (4). Per-type coverage of the 244
shipped items: Blowhole 100, Break 55, Crack 56, Uneven 20, Fray 13. The skipped defects remain
fully covered by the companion 186-grounding / 186-region sets.
| field | type | meaning |
|---|---|---|
query |
str | 16 variants; names the defect type to locate; "answer with the letter only" |
image |
Image | 2×2 composite, panels A–D |
annot |
str | A / B / C / D |
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, gold_letter, panel_tags, area_pct |
Roles
Roles: this is an answer-only tier — there is no reasoning column; annot is both the machine-parseable gold AND the direct-answer SFT target ('SFT-ready' here means direct imitation of annot in the query-specified format); it is also the exact-match/IoU reward key for RLVR.
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).