Defect Spectrum: A Granular Look of Large-Scale Defect Datasets with Rich Semantics
Paper • 2310.17316 • Published • 1
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D15-region
Region-conditioned defect typing (L5 of the D15 task ladder) — 6,041 items, derived
deterministically (no LLM/teacher) from the semantic masks of
AI4Manufacturing/D15 (DefectSpectrum).
Exact-match gradable (closed type list + no defect) → SFT and RLVR-ready.
The repository name is an internal task code. See Provenance below.
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).
Task
"An operator points at a region — what defect, if any, is there?" One item per defect instance
(per-class connected components after proximity grouping, so fragmented defects like dashed scratches
form one instance; 107 over-fragmented records fall back to per-class union boxes —
metadata.union_fallback marks union-box items) plus 1,491 clean-region negatives (~25%) teaching
rejection. The region is conveyed in one of two modes (~50/50, metadata.region_mode):
overlay— a red rectangular ring drawn on the image around the region (ring thickness scales with image size; regions are padded to ≥6% of the min dimension so they stay visible; positives and clean regions use the identical rectangular ring language, so ring shape leaks nothing).bbox_text— the raw image plus the region as a native-pixel box[x, y, w, h](origin top-left) in the query text — pairs with the coordinate vocabulary ofD15-annotated'sreasoning_grounded.
Golds are guaranteed unambiguous: instances whose query box contains another defect class's pixels are
skipped (422), and clean boxes contain zero defect pixels by construction. Gold = the type name
exactly as listed in the query's closed class list, or no defect.
Records
6,041 items (single train split): 4,550 positives + 1,491 clean.
Top golds: no defect 1,491 · scratch 1,220 · crush 643 · color 228 · bump 213 · crack 202 · …
Revision notes (2026-07-09): two adversarial review rounds. v1→v2: template-parity tell (two templates only occurred on defect items) + two defect types lost entirely to the sub-15px denoise (union-box rescue added,
metadata.union_fallback). v2→v3: clean boxes were size-clamped to [6%, 30%] while defect boxes weren't — printed coordinates separated defect from clean with 100% precision on 77% of bbox-text items. v3 samples clean-box sizes from the record's own defect boxes (no clamp; overlay minimum applies to both kinds), jitters good-record sizes, and guards the drawn ring's full visual extent for purity. v3→v4 (round-3 review): clean sizes previously sampled per-CLASS union boxes while positives used per-INSTANCE boxes — a within-category size tell; v4 samples clean size AND position from the exact per-instance population positives emit. v4→v5 (round-4 review): v4's clean sampler had an 8-px size floor and off-by-one edge clamps — coordinates only positives could print (336 bbox-text items decodable at precision 1.0). v5 removes every clean-only constraint (no floor, flush-to-edge allowed), draws clean sizes/centers from the exact emitted-positive box population (instances + union boxes), and adds a uniform placement fallback so the size/position tail stays reachable. Measured on v5: bbox_text geometry GBM AUC 0.51 (chance); per-category size-only probes at/below majority everywhere; out-of-range rule scan within bounds below. Known residuals (inherent, disclosed, regression-gated inverify_leaks.py— round-5 adjudication; no precision-1.0 rule exists in v5, verified): (1) an overlay position prior (global GBM AUC ~0.64): clean regions physically cannot occupy defect pixels — e.g. zipper defects lie on the zipper line; (2) extreme-tail marginals in bbox-text (54 area + 26 x items, ~2.5%): the SMALL-area tail is ~13× defect-heavy because every defect instance emits a positive while each record draws at most one clean (pool weighting); the far-edge/giant tail is purity-constrained (several sources, e.g. VISION, contain no defect-free stock); (3) a large-box position×size interaction (boxes ≥10% of the image, ~7% of items: position-only AUC ~0.74–0.80 within that stratum): large clean boxes place uniformly while large defects sit at object-structured positions — the irreducible trade for tail coverage (the alternative, no large cleans at all, is a strictly worse precision-1.0 rule). All are soft priors from purity-constrained negatives — none bypasses looking at the image, and the region CONTENT remains the intended signal. Do not use v1–v4.
| field | type | meaning |
|---|---|---|
query |
str | 4 surface variants per mode; names the product; closed class list; instructs "type only, or 'no defect'" |
image |
Image | product photo, with the red ring burned in for overlay mode |
annot |
str | gold type string, or no defect (exact match) |
reasoning |
null | none — deterministic derivation |
cate / task |
str | B / T-B2 |
metadata |
str (JSON) | source, category, image_sha256, d15_record_id, region_mode, bbox_xywh (native px; padded box for overlay), instance_index (−1 = clean), gold |
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
(seeded by record id), zero API cost. Upstream license: MIT (respect upstream terms; card is license: other).
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
- Loss on completions; answers are short by instruction (the query demands "type only"), so no verbosity prior is learned. Exact-match reward for RLVR.
- Companions:
D15-annotated(CoT, L1–L3),D15-mcq(mask MCQ, L4),D15-grounding(detection format, L6-lite).
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