181-region / README.md
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card: state training-target vs machine-gold roles (2026-07-17 convention pass)
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---
tags:
- smart-manufacturing
- sft
- industrial
- vision
license: other
pretty_name: 181-region
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-region
Region-conditioned binary anomaly QA — **7,548 items**, deterministic (no LLM): 2,100 positives (the
ellipse region of every anomalous record; overlay ring or native-px bbox-in-query, ~50/50) + 5,448
clean regions ("no anomaly"; on-image cleans for anomalous records — DAGM has exactly one anomaly per
defective image, so off-ellipse regions are genuinely clean — plus sampled good-image regions). Clean
boxes sample size AND position from the emitted-positive population; large boxes (≥10% of image)
place cleans at MIRRORED pool positions so border-flushness is reproduced — an adversarial review
found Class6's border-flush defects vs interior cleans were separable at AUC 0.94 before this fix;
after it, per-(class,mode) GBM gates (0.75) show worst cell 0.625 and pooled AUC 0.52/0.53.
Machine-verified: zero structural out-of-range rules; every template occurs with both golds; weak
residual single-feature signals in the Class6 large-box stratum (~0.58, mechanisms understood) are
disclosed. **Clean-majority ratio (72%) is disclosed** — reweight at training time if you want balance.
**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 **40 per mode (80) 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 golds
(`anomaly` / `no anomaly`). Companions: [`181-annotated`](https://huggingface.co/datasets/AI4Manufacturing/181-annotated),
[`181-mcq`](https://huggingface.co/datasets/AI4Manufacturing/181-mcq),
[`181-grounding`](https://huggingface.co/datasets/AI4Manufacturing/181-grounding).