186-mcq / README.md
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---
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`](https://huggingface.co/datasets/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`](https://huggingface.co/datasets/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`).