README.md

metadata

tags:
  - smart-manufacturing
  - sft
  - industrial
  - vision
license: other
pretty_name: 181-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.

181-mcq

Region-MCQ (Set-of-Mark) for anomaly localization — 1,943 items, deterministic (no LLM), one per anomalous DAGM record of 9 classes. 2×2 grid A–D; exactly one panel's red region contains the anomaly. Negatives are location-only (the true mask translated to positions drawn from the SAME class's gold-centroid pool, mirror fallbacks) — under a coarse containing-region GT, extent-based negatives (dilate/erode) are an annotation convention rather than a visual fact, so they were removed after adversarial review. Placing negatives at class-typical positions makes them positionally indistinguishable from golds by construction: a geometry-only attacker scores 0.225 pooled (below 25% chance) with worst class 0.300 — machine-gated in verify_181.py. Gold letters A 468 / B 480 / C 499 / D 496; every independent choice from its own salted hash; template×letter at chance. Both gold and negatives must render ≥30 visible px outside the letter tag. Raw base images. Exclusions (counted, confidence-over-coverage): Class6 entirely (150 — huge border-flush masks make position-fair negatives impossible; a 51% per-class position exploit was measured and eliminated by exclusion) + 7 Class8 golds hidden under the letter tag. Class6/Class8 anomalies remain fully covered by the companion region/grounding/L1 sets.

Weak-GT disclosure. DAGM's official labels are deliberately COARSE ellipses ("roughly indicating" the defect) — every localization here is a containing region, not a tight extent (metadata.coarse_gt: true). Grade localization by containment/center-hit, never tight IoU.

Query diversity (2026-07-11). The query field is drawn from a pool of 25 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).

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.

Overlap / de-duplication (§8)

270 of these images (all anomalous, DAGM Classes covered by DefectSpectrum) also appear byte-identical in the D15 family (D15-annotated / D15-mcq / D15-region / D15-grounding) with FINE masks and defect-type labels. Reconstruct the exact overlap via metadata.image_sha256. Both official DAGM splits are processed identically here (project policy): metadata.split preserves the original Train/Test membership — carve your own held-out set downstream and keep it out of training.

Provenance

Built from AI4Manufacturing/181 by annotate/181/build_181_derived.py (forge_model), verified by verify_181.py. Exact-match / RLVR-ready. Companion sets: 181-annotated, 181-region, 181-grounding.