You need to agree to share your contact information to access this dataset
This repository is publicly accessible, but you have to accept the conditions to access its files and content.
This dataset is released for research use. Access is reviewed and granted manually by the maintainers. Please state your name, affiliation, and intended use.
Log in or Sign Up to review the conditions and access this dataset content.
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).
- Downloads last month
- 18