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D15-grounding

Detection-format defect localization (L6-lite of the D15 task ladder) — 2,684 items (every record of the corrected AI4Manufacturing/D15, minus one excised contradiction — see the 2026-07-17 note below), derived deterministically (no LLM/teacher). The model must output boxes as text, the mainstream VLM grounding pattern (Qwen-VL-style absolute-coordinate JSON).

The repository name is an internal task code. See Provenance below.

Query diversity (2026-07-11). The query field is drawn from a pool of 40 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).

Task

"Locate every defect." annot is a JSON list of {"type": ..., "bbox_xywh": [x, y, w, h]} in native pixel coordinates (origin top-left), one entry per defect instance (per-class connected components after proximity grouping; 107 over-fragmented records fall back to per-class union boxes), sorted (type, x, y) for a deterministic target. Type-complete by construction: every defect type present in the record's mask contributes at least one box — instance fragments under 15 px are denoised, but if all of a type's instances fall under the floor, its union box is emitted instead (counted at build). Defect-free parts (386) have annot = [] — detection rejection is part of the task. The query states the coordinate convention, the closed class list, and the empty-list rule.

Records

2,684 items (single train split): 2,298 defective (≥1 box) + 386 defect-free ([]). Counts auto-generated by annotate/D15/verify_d15_family.py --card-numbers (2026-07-17).

Excision (2026-07-17): one contradiction record removed (image sha256 4be393b8..., DS-VISION/Capacitor/512.jpg — source labels it anomalous with an empty mask; the derived gold here would have been [], teaching "good"). The base D15 repo retains it as a disclosed source contradiction.

Legibility (rendered defect-instance size at reference inputs; method: per-instance mask pixels after long-side resize; n=3,086 instances):

reference input instances <16px median instance px

448 5.5% 605

768 1.5% 1,626

1024 0.8% 2,490

Train this artifact at ≥768–1024px effective input or with a crop curriculum; sub-floor instances are dominated by DS-VISION.

Revision note (2026-07-09): v1's sub-15px denoise could silently delete a defect type entirely (2 records contradicted the sibling D15 gold; 21 more lost instance boxes). This revision guarantees type-completeness (union-box rescue) and counts every dropped fragment. Do not use v1.

field	type	meaning
`query`	str	4 surface variants; product wording; closed class list; JSON output spec
`image`	Image	the raw product photo (no overlays)
`annot`	str	JSON box list (see above), `[]` when defect-free
`reasoning`	null	none — deterministic derivation
`cate` / `task`	str	`B` / `T-B2`
`metadata`	str (JSON)	source, category, `image_sha256`, `d15_record_id`, `n_instances`

Provenance

Built from AI4Manufacturing/D15 (DefectSpectrum, ECCV 2024, arXiv:2310.17316) after its 2026-07-08 correction. Generator: annotate/D15/build_d15_l5_grounding.py in AI4Manufacturing/forge_model — deterministic, zero API cost. Upstream license: MIT (respect upstream terms; card is license: other).

Training-mixture notes (2026-07-17 certification)

One photo appears in up to 5 family artifacts (family mean ≈6.3 items/photo). Carve any train/eval split PHOTO-WISE on metadata.image_sha256 across ALL D15-family repos simultaneously.
Global label prior: 85.6% of photos are defective — a blind guesser scores ≈90% on good-vs-defective labels. Counterweight with good-heavy sources in your mixture; this is a structural property of DefectSpectrum, not a leak (text-only blind probe on shipped queries: MCQ at exact chance 25%; region at prior level).
641 VISION photos in this family are byte-shared with AI4Manufacturing/D23 under a MATERIALLY DIFFERENT label policy (DS masks confirm only 88.5% of VISION boxes; worst subset coverage 0.03). Take each shared image from exactly ONE side — machine-readable keys: overlap_with_D23.json on the D15 repo. Full sibling-overlap table: D15 base card §8.

Overlap / de-duplication (§8)

Same base photos as D15 → inherits all of D15's sha-verified overlaps: DS-MVTec ⊂ D20 test (also in D05); DS-DAGM ⊂ 181 (120 in its test); DS-VISION ⊂ D23 (incl. val). Do not evaluate on those repos' held-out splits if you train on this set. Reconstruct overlaps via metadata.image_sha256.

Training notes

Boxes are canonical native-px COCO xywh. Convert to your model's grounding convention at train time (normalized 0–1000, corner pairs, special tokens, …) — regenerate, don't regex; see common/box_convert.py in forge_model.
Gradable by class-aware box matching (e.g. greedy IoU≥0.5) → usable as an RLVR reward or eval metric; [] records score rejection.
For pixel-accurate masks at inference, box-prompt SAM/SAM2 with the predicted boxes; the pixel GT lives in D15 / D15-annotated (mask column).
Companions: D15-annotated (L1–L3), D15-mcq (L4), D15-region (L5).

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Total file size:

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Paper for AI4Manufacturing/D15-grounding

Defect Spectrum: A Granular Look of Large-Scale Defect Datasets with Rich Semantics

Paper • 2310.17316 • Published Oct 26, 2023 • 1