Datasets:

AI4Manufacturing
/

D15-mcq

tags:
  - smart-manufacturing
  - sft
  - industrial
  - vision
license: other
pretty_name: D15-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.

D15-mcq

Mask-grounded multiple choice (Set-of-Mark style) for industrial defect localization — 2,828 items, derived deterministically (no LLM/teacher involved) from the semantic masks of AI4Manufacturing/D15 (DefectSpectrum). Exact-match gradable → directly usable for SFT and RLVR-style training.

The repository name is an internal task code. See Provenance below.

Query diversity (2026-07-11). The query field is drawn from a pool of 36 surface variants for this task (paraphrases that preserve the task and answer-format exactly; the answer-format directive is held verbatim), each selected by an independent per-record hash. This replaces the earlier 4-template design to prevent instruction-format overfitting; answers, images, ids, and all provenance are unchanged. A machine gate in verify_*.py checks that no template correlates with the gold (binomial z < 4.5).

2026-07-17 re-cut: queries re-rendered from the 36-variant pool (2,512 changed, 316 index-coincident); answers, images, letters (A 681 / B 669 / C 740 / D 738) and metadata byte-identical to the previous revision. Counts auto-generated by annotate/D15/verify_d15_family.py --card-numbers (2026-07-17).

Task

One item per (defective D15 record, defect type). The image is a 2×2 grid of views A–D of the same product photo; each view overlays one red candidate region mask (SoM style: translucent fill + outline + corner letter). Exactly one view overlays the true mask for the named defect type — in both location and extent (the query says so explicitly). annot is the correct letter.

The three negatives per item are hard by construction, drawn in preference order from:

tag	construction
`othertype`	the true mask of a different defect type in the same image, presented as a candidate for this type (hardest)
`shift`	true mask translated by ~0.7–1.6× its bbox extents (border-clipped, ≥60% of pixels kept)
`fliplr` / `flipud` / `rot180`	true mask mirrored across the image axes
`dilate`	true mask grossly over-grown (bounded at 3% of the image dimension; must be ≥2.5× the true area) — a right-location, wrong-extent decoy
`erode`	true mask shrunk to <60% of its area

Every negative is guaranteed wrong (IoU vs. truth < 0.35, except dilate which is wrong by extent) and panels are mutually distinct (pairwise IoU < 0.7). Gold letters are balanced via id-hash: A 681 / B 669 / C 740 / D 738. Every independent choice — query surface variant, the other-type decoy draw, and the assignment of negatives to slots — is drawn from its own salted hash, so neither the query text nor the visible arrangement of negative kinds correlates with the gold position (verified: template×letter at chance; negatives appear in canonical construction order at the 1/6 shuffled expectation, vs 1.0 in v1/v2; worst single kind-at-letter rule at the 1/3 conditional chance). Both the gold and every negative overlay must render ≥30 visible pixels outside the letter label after downscale. Items whose mask covers

35% of the frame, whose gold overlay would be smaller than ~~30 rendered pixels after panel downscale (unanswerable), or where 3 sound negatives could not be built were **skipped (~~255 type-instances)** rather than shipped degenerate.

DS-DAGM panels use a histogram-equalized base image (identically in all four views): several DAGM defect classes are near-invisible in the raw render, which would otherwise make the item unanswerable. metadata.equalized_base records this.

Records

2,828 items (single train split): DS-MVTec 1,422 · DS-VISION 1,097 · DS-DAGM 268 · DS-Cotton-Fabric 41.

Revision notes (2026-07-09): two adversarial review rounds. v1→v2: query template shared the gold-position hash (query text revealed the answer) + 18 invisibly-small golds. v2→v3: negatives filled slots in fixed preference order (the visible arrangement of negative kinds decoded the gold with 100% accuracy), the other-type decoy draw shared the gold-position seed (parity leak on 3-type records), and the letter label could paint over a tiny gold overlay. v3 shuffles slot assignment independently, draws every choice from its own hash, and enforces label-aware visibility for gold AND negatives. Do not use v1/v2.

field	type	meaning
`query`	str	the MCQ instruction (36 surface variants; names the product and the defect type; asks for the letter only)
`image`	Image	the 2×2 composite (~1560px, JPEG)
`annot`	str	gold letter `A`–`D` (exact match)
`reasoning`	null	none — items are deterministic; no teacher was used
`cate` / `task`	str	`B` / `T-B2` (unified schema)
`metadata`	str (JSON)	source, category, `image_sha256`, `d15_record_id`, `defect_type`, `bbox_xywh` (native px of the source image), `area_pct`, `gold_letter`, `panel_tags` (letter → construction tag), `equalized_base`

Provenance

Built from AI4Manufacturing/D15 (DefectSpectrum — EnVision-Research, ECCV 2024, arXiv:2310.17316; fine-grained multi-class semantic re-annotation of MVTec-AD / VISION / DAGM / Cotton-Fabric), after D15's 2026-07-08 correction (26 upstream-misfiled records removed). Generator: annotate/D15/build_d15_mcq.py in AI4Manufacturing/forge_model — fully deterministic (seeded by md5(record_id|type)), zero API cost. Upstream license: MIT (respect the underlying datasets' terms; this card is license: other).

Training-mixture notes (2026-07-17 certification)

One photo appears in up to 5 family artifacts (family mean ≈6.3 items/photo). Carve any train/eval split PHOTO-WISE on metadata.image_sha256 across ALL D15-family repos simultaneously.
Global label prior: 85.6% of photos are defective — a blind guesser scores ≈90% on good-vs-defective labels. Counterweight with good-heavy sources in your mixture; this is a structural property of DefectSpectrum, not a leak (text-only blind probe on shipped queries: MCQ at exact chance 25%; region at prior level).
641 VISION photos in this family are byte-shared with AI4Manufacturing/D23 under a MATERIALLY DIFFERENT label policy (DS masks confirm only 88.5% of VISION boxes; worst subset coverage 0.03). Take each shared image from exactly ONE side — machine-readable keys: overlap_with_D23.json on the D15 repo. Full sibling-overlap table: D15 base card §8.

Overlap / de-duplication (§8)

The base photos are the SAME images as D15 and therefore inherit all of D15's overlaps (sha-verified): DS-MVTec ⊂ D20 test (and appears in D05); DS-DAGM ⊂ 181 (120 of them in 181's test); DS-VISION ⊂ D23 (incl. its val split). Do not evaluate on those repos' held-out splits if you train on D15-mcq. Reconstruct exact overlap sets via metadata.image_sha256.

Training notes

Composite images are self-contained — no extra rendering needed at train time.
annot is a single letter → put loss on completions; exact-match reward for RLVR.
Companion sets: D15-annotated (CoT defect typing on the raw images, L1–L3 of the same ladder), and the source D15 for pixel-mask GT.