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193-grounding

Detection-format surface-defect localization on Severstal cold-rolled steel-strip imagery — 12,420 records (all train), derived deterministically (no LLM/teacher) from the base AI4Manufacturing/193 masks. The model outputs boxes as text: annot is a native-pixel JSON list [{"bbox_xywh": [x, y, w, h]}] (origin top-left, sorted by x then y), one box per surviving defect region — or [] when the strip is good (rejection is in-task). Anonymous class (box-only, no type token), matching 181-grounding.

Task

Locate every defect region on the strip -> boxes. Composition:

6,518 positive images carrying 15,646 boxes total (transitive-merge components that survive the legibility + fill floors).
5,902 good images shipped as empty-answer [] negatives (~1:1) — the goods carve-out.
Empty-answer prior: 47.5% (5,902 of 12,420).

Boxes-per-positive-image (frozen battery): mean 2.4709, median 2, p90 5, max 15.

Schema (7 columns, answer-only)

field	type	meaning
`query`	str	student question — domain-conditioned steel-strip surface QC; 34 pooled variants; ends with a verbatim box-output directive
`image`	Image	the raw steel-strip surface photo (bytes; never cropped)
`annot`	str	machine-parseable gold: JSON box list `[{"bbox_xywh":[x,y,w,h]}]` (native px), or `[]` on goods
`reasoning`	null	none — deterministic derivation, answer-only
`cate` / `task`	str	`B` / `T-B1` (inherited from parent 193)
`metadata`	str (JSON)	`image_sha256`, `negative`, `n_boxes`, `gold_boxes_xywh` (== `annot`), `derivation`, `legibility_floor`, `fill_floor`, `gold_scope`, `goods_carveout`, `anonymous_class`, `disclosures`, family + gate provenance

Roles (regime 3)

Roles: this is an answer-only tier — there is no reasoning content (reasoning is null on every record). annot is both the machine-parseable gold AND the direct-answer SFT target in the exact format the query specifies (regime 3: the final format the query requests == the annot format); it is also the exact-match / IoU reward key for RLVR.

Gold scope — legible components only (disclosed)

Golds derive from the class-indexed mask by a transitive union-find merge @12px of connected components. A component is kept only if it clears the legibility floor (16px short side @ native/2.36MP (scale 1.0); counting all-components-legible margin @1568 long-side) and a fill-ratio floor (mask-pixels / bbox-area >= 0.05). Sub-floor components are unlearnable-by-render and are excluded: 5.0% of components fall below the floor at the native/2.36MP production reference. The gold is therefore legible-components-only (render-relative) — an answer never silently omits a legible box. Goods [] is an absence VERDICT ("no defect region under this standard"), not an exhaustiveness claim.

Grounding is not a saliency task (round-2 self-test)

Because goods ship as [], the model must perform real defect-vs-benign discrimination, not generic saliency. The frozen round-2 CONVERGED self-test confirms this decisively: a saliency detector actually prefers to box goods — std AUC 0.415; 89.3% of goods false-fire at 80% defect recall; the MCQ-killing low-level texture shortcut compounds to 67% goods-FP. Empty-on-goods forces the discrimination a pure saliency model cannot fake. (An MCQ rung was measured NOT-VIABLE — a saliency task — and dropped; grounding survives because coverage + empty-on-goods make it real.)

Anonymous defect classes

Severstal's four surface-defect classes are anonymous: the maker never released what they physically mean (the labels are only the numeric ids 1-4; typology not given — see Carvalho et al., arXiv:2305.13261). With no released semantics, this rung is defect-agnostic — no defect type token appears anywhere in query or annot (matching the sibling 181-grounding, also an anonymous-class, box-only rung with no type field). The build-gate asserts 0 query/annot cells carry a phenomenon-noun/type token. (The "pitted / crazing / scratches / patches" names that circulate online are NEU-DET's and are mis-attributed to Severstal; they are not used here.)

Query design (build-gate 9)

The query is drawn from a fixed 34-variant domain-conditioned pool (selected by an independent salted per-image hash). Each variant names the domain (cold-rolled steel-strip surface inspection) and defers to "this line's / this setting's defect standard" without enumerating which phenomena count as defects — the model learns the good/anomalous boundary from the data, not the prompt. The pool reuses the parent query_pool._FORBIDDEN guard: a machine gate confirms 0 variants leak a phenomenon noun and every variant ends with the verbatim answer-format directive Output a JSON list [{"bbox_xywh": [x, y, w, h]}] in native pixels, origin top-left, one box per defect region, sorted by x then y; output [] if none.

Provenance / reproduction

Derived read-only from the class-indexed segmentation masks of base AI4Manufacturing/193 (Severstal Steel Defect Detection, Kaggle 2019). Answers are a pure function of the mask — $0 teacher, no LLM anywhere in this rung: each box list / count is recomputed by a transitive union-find merge @12px of the mask's connected components to a growing-extent fixpoint. Built by forge_model annotate/193/rungs/ (build_rungs.py, query_pool_rungs.py, prebuild_gates_v2.py; cards by gen_cards.py, all numbers read from frozen reports). Every choice is salted-hash seeded; a rerun reproduces the artifact exactly.

Derivation script build_rungs.py sha256 bf42ab24790ce37bc2d791023880c853a1b23bcbbb343a8c94d825797f76185e.
11 deterministic checks (re-derived from the written parquet's mask) re-derived every gold from the written parquet's mask: grounding boxes — 0 mismatches vs mask re-derivation; 15646 boxes total; counting FINAL count — 0 mismatches vs mask re-derivation (FINAL == component count).
Boxes are canonical native-px COCO xywh. Convert to your model's grounding convention at train time — regenerate, don't regex; see common/box_convert.py in forge_model.

Upstream license other — respect the upstream terms. Public, manual access review (gated=manual).

Split & family carve manifest

Train-only — every record is split=train (no val split; uniform-split policy). Eval carving is fully downstream, keyed on metadata.image_sha256 across the whole 193 family (parent 193 + grounding + counting share images), so a machine-checkable carve manifest is shipped alongside the build (outputs/193/rungs/family_carve_manifest.json, keyed on image_sha256):

member	records
parent `193`	12,568
`193-grounding`	12,420
`193-counting`	10,870
in both rungs	10,870

Family size (distinct images) = 12,568. The two rungs pose different questions (boxes vs count) over overlapping images — same evidence: carve them jointly on image_sha256, and never place the same image on both sides of a train/eval split.

Overlap / de-duplication (§8)

Inherits base 193's image relationships (Kaggle test GT withheld and the mirror's derived YOLO labels are excluded upstream). Every record carries metadata.image_sha256; the grounding and counting rungs share images with each other and the parent — reconstruct any overlap and carve jointly via the family manifest above.

Companions

193 (base binary good/anomalous), 193-counting (separated-region count).

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Total file size:

1.25 GB

Paper for AI4Manufacturing/193-grounding

A Review of Benchmarks for Visual Defect Detection in the Manufacturing Industry

Paper • 2305.13261 • Published May 5, 2023