| --- |
| 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`](https://huggingface.co/datasets/AI4Manufacturing/D15) family |
| ([`D15-annotated`](https://huggingface.co/datasets/AI4Manufacturing/D15-annotated) / |
| [`D15-mcq`](https://huggingface.co/datasets/AI4Manufacturing/D15-mcq) / |
| [`D15-region`](https://huggingface.co/datasets/AI4Manufacturing/D15-region) / |
| [`D15-grounding`](https://huggingface.co/datasets/AI4Manufacturing/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`](https://huggingface.co/datasets/AI4Manufacturing/181) by |
| `annotate/181/build_181_derived.py` (forge_model), verified by `verify_181.py`. Exact-match / RLVR-ready. |
| Companion sets: [`181-annotated`](https://huggingface.co/datasets/AI4Manufacturing/181-annotated), |
| [`181-region`](https://huggingface.co/datasets/AI4Manufacturing/181-region), |
| [`181-grounding`](https://huggingface.co/datasets/AI4Manufacturing/181-grounding). |
|
|
