| --- |
| tags: |
| - smart-manufacturing |
| - sft |
| - industrial |
| - vision |
| license: other |
| pretty_name: D15-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. |
| --- |
| |
| # D15-region |
|
|
| Region-conditioned defect typing (L5 of the D15 task ladder) — **6,040 items**, derived |
| **deterministically** (no LLM/teacher) from the semantic masks of |
| [`AI4Manufacturing/D15`](https://huggingface.co/datasets/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 `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). |
| |
| ## 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 **1,490 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 of |
| [`D15-annotated`](https://huggingface.co/datasets/AI4Manufacturing/D15-annotated)'s `reasoning_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 |
|
|
| **6,040** items (single `train` split): 4,550 positives + 1,490 clean. |
| Top golds: no defect 1,490 · scratch 1,220 · crush 643 · color 228 · bump 213 · crack 202 · … |
| 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), which contributed |
| > one `overlay`/`no defect` item here. The base D15 repo retains it as a disclosed source contradiction. |
|
|
| > **Per-category prior warning:** per-category modal-answer shares reach 70.8% (Screw→crush) — see |
| > the Training-mixture notes below; the disclosed soft geometry priors remain as documented. |
|
|
| > **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 in `verify_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` | |
| |
| ## 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. |
| |
| ## 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`](https://github.com/AI4Manufacturing/forge_model) — deterministic |
| (seeded by record id), 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`](https://huggingface.co/datasets/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`](https://huggingface.co/datasets/AI4Manufacturing/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`](https://huggingface.co/datasets/AI4Manufacturing/D20) **test** (also in |
| [`D05`](https://huggingface.co/datasets/AI4Manufacturing/D05)); DS-DAGM ⊂ |
| [`181`](https://huggingface.co/datasets/AI4Manufacturing/181) (120 in its test); DS-VISION ⊂ |
| [`D23`](https://huggingface.co/datasets/AI4Manufacturing/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`](https://huggingface.co/datasets/AI4Manufacturing/D15-annotated) (CoT, |
| L1–L3), [`D15-mcq`](https://huggingface.co/datasets/AI4Manufacturing/D15-mcq) (mask MCQ, L4), |
| [`D15-grounding`](https://huggingface.co/datasets/AI4Manufacturing/D15-grounding) (detection format, L6-lite). |
|
|
