You need to agree to share your contact information to access this dataset
This repository is publicly accessible, but you have to accept the conditions to access its files and content.
This dataset is released for research use. Access is reviewed and granted manually by the maintainers. Please state your name, affiliation, and intended use.
Log in or Sign Up to review the conditions and access this dataset content.
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
queryfield 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 inverify_*.pychecks that no template correlates with the gold (binomial z < 4.5).
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 golds
(anomaly / no anomaly). Companions: 181-annotated,
181-mcq,
181-grounding.
- Downloads last month
- -