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193-counting

Counting of separated surface-defect regions on Severstal cold-rolled steel-strip imagery — 10,870 records (all train), derived deterministically (no LLM/teacher) from the base AI4Manufacturing/193 masks. annot is a single integer as a string ("0" on goods). Anonymous class (defect-agnostic), matching the 193-grounding sibling.

Task — an HONEST referent

Count the separated defect regions on the strip — the number of >=12px-separated transitive-merge components, NOT a physical defect count. A single fragmented extended defect counts as its separated regions; the query asks "how many separated regions of surface defect are present" throughout (never "distinct defects"). Composition:

4,968 anomalous images (merge-stable + fully legible; see pool below).
5,902 good images shipped as count 0 — the goods carve-out.
Answer prior (with goods): 54.3% are count-0 (5,902 of 10,870).
Among the anomalous-only pool the counts are diverse: majority-1 is 46.6% (< 60%), mean 2.0087, max 11, 11 distinct values.

Schema (7 columns, answer-only)

field	type	meaning
`query`	str	student question — domain-conditioned; 34 pooled variants; ends with the verbatim directive `Answer with a single integer.`
`image`	Image	the raw steel-strip surface photo (bytes; never cropped)
`annot`	str	machine-parseable gold: a single integer (`"0"` 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`, `count` (== `annot`), `separation_threshold_px`, `referent`, `sensitivity_note`, `pool`, `derivation`, `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.

Pool — deliberately stricter than grounding

The count is a pure function of the separation threshold, so the pool ships only images whose count is stable across the 8-12-20px band AND whose every canonical component is legible at the 1568 long-side render — a deliberately stricter margin than grounding's per-box floor, because one illegible component flips the count. Floor: 16px short side @ native/2.36MP (scale 1.0); counting all-components-legible margin @1568 long-side. Goods contribute count 0 under the absence carve-out.

Count-vs-threshold sensitivity (frozen battery)

The count depends on the >=12px separation definition. The shipped golds use 12px; the frozen pre-build battery reports the full anomalous-image count distribution at four thresholds:

separation threshold	majority-1 share	share >=2	mean	max	distinct counts
8px	37.1%	62.9%	2.5596	16	16
12px (shipped)	38.0%	62.0%	2.4709	15	15
20px	39.8%	60.2%	2.3249	14	14
30px	42.4%	57.6%	2.1812	12	12

Anonymous, area-metric-free

No defect type token and no area-fraction / percentage appears in any field (build-gate asserts 0 leaks) — the referent is purely the count of separated regions.

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 Answer with a single integer.

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-grounding (defect-region boxes).

Downloads last month: 10

Total file size:

1.1 GB

Paper for AI4Manufacturing/193-counting

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

Paper • 2305.13261 • Published May 5, 2023