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181-grounding

Detection-format anomaly localization — all 16,100 records of AI4Manufacturing/181, deterministic (no LLM). annot = [] (14,000 anomaly-free — rejection is in-task) or [{"bbox_xywh": [x,y,w,h]}], the official ellipse's containing box in native pixels, floored to a 24-px minimum side (118 degenerate thin ellipse rasterizations — mostly Class8 — under-cover their visible mark; metadata.degenerate_gt flags them and the floored box provably still contains the original, verified on all 2,100). The query states the containment semantics. Grade by containment / center-hit — the GT is deliberately coarse; tight-IoU rewards would punish correct answers.

Weak-GT disclosure. DAGM's official labels are deliberately COARSE ellipses ("roughly indicating" the defect) — every localization here is a containing region, not a tight extent (metadata.coarse_gt: true). Grade localization by containment/center-hit, never tight IoU.

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).

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.

Overlap / de-duplication (§8)

270 of these images (all anomalous, DAGM Classes covered by DefectSpectrum) also appear byte-identical in the D15 family (D15-annotated / D15-mcq / D15-region / D15-grounding) with FINE masks and defect-type labels. Reconstruct the exact overlap via metadata.image_sha256. Both official DAGM splits are processed identically here (project policy): metadata.split preserves the original Train/Test membership — carve your own held-out set downstream and keep it out of training.

Provenance

Built from AI4Manufacturing/181 by annotate/181/build_181_derived.py (forge_model), verified by verify_181.py (box == mask-derived ellipse bbox on all 2,100; [] exactly for goods; template independence). Convert box conventions at train time via common/box_convert.py — regenerate, don't regex. Companions: 181-annotated, 181-mcq, 181-region.

Geometry (`metadata.geometry`)

Every record carries a geometry block inside the existing metadata JSON string, so that its gold can be re-derived at any render size. No schema column changed; existing loaders are unaffected.

Coordinates are native pixels of the image in that record. scale is 1.0 throughout — this repo publishes at source resolution, nothing was downscaled at publish time.

"geometry": {
  "image_wh":  [W, H],        // dims of the image in THIS record
  "source_wh": [W, H],        // dims of the original source image
  "scale": 1.0,               // image_wh / source_wh; < 1.0 would disclose a publish-time downscale
  "n_instances": 2,
  "instances": [
    { "instance_id": 1, "bbox_xywh": [x, y, w, h], "min_side_px": 65, "class": null }
  ],
  "n_dropped_subminimum": 0,  // components removed by the filters below
  "union_box_fallback": false,// true => boxes are per-class unions, NOT real instances
  "conventions": { ... }      // see table
}

instances is present even when empty. [] means the record genuinely has no defects; an absent block would mean geometry could not be recovered. Those are different states and are never conflated.

Conventions used to derive it

There is no universal definition of "one defect instance" — it depends on the mask the source shipped. This repo's is stated, not implied:

field	value
`algorithm`	`containing_box`
`binarisation`	`gt:0`
`connectivity`	`4`
`merge`	`none`
`min_area_px`	`0`
`max_instances`	`1`
`artifact`	`coarse`
`fill_floor`	`None`
`legibility_floor_px`	`None`
`min_side_floor_px`	`24`
`spec_sha`	`ccc479c9706b8f85`

Provenance and verification


records	16,100
carrying a geometry block	16,100 / 16,100
instances per record	`0`: 14,000, `1`: 2,100
total instances	2,100
image dimensions	512×512 (16,100)
`scale` values present	[1.0]

Derived from the AI4Manufacturing/193 masks and verified against this repo's own published answers before it was written — a recomputation that disagreed with the shipped gold would have aborted the update rather than overwritten it.

Using it

Coordinates only stay correct if they are rescaled with the image. A patch-based VLM does not render at native size: Qwen2-VL's processor snaps both dimensions to a multiple of 28, so a 1600×256 strip is rendered 1596×252 and native-pixel boxes are then wrong by a few pixels. forge_model/193/adapt.py regenerates coordinates for a target render size, re-derives counts, and drops records whose gold no longer holds there.

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