Defect Spectrum: A Granular Look of Large-Scale Defect Datasets with Rich Semantics
Paper • 2310.17316 • Published • 1
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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
queryfield is drawn from a pool of 19 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).
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 (4 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).
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.
annotis 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 sourceD15for pixel-mask GT.
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