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186-grounding

Detection-format defect localization on magnetic tiles — 1,340 items (952 good + 388 defective), derived deterministically from the pixel saliency masks of AI4Manufacturing/186. The model must output boxes as text; defect-free tiles must output [] — detection rejection is part of the task.

Task

"Locate every defect." annot is a JSON list of {"type": ..., "bbox_xywh": [x, y, w, h]} in native pixel coordinates (origin top-left), one entry per defect instance (connected components after proximity grouping: dilation radius ~1% of min dimension merges fragments of one physical defect; sub-15-px groups denoised but counted — 14; if all of a record's groups fall under the floor its union box is emitted instead — 2 records), sorted (type, x, y). Good tiles have annot = [] (952). The query states the coordinate convention, the closed class list (Blowhole, Break, Crack, Fray, Uneven), and the empty-list rule. Query pool: 24 surface variants (template x good/defective independence: worst z = 1.88).

Uneven disclosure. Uneven (grind-unevenness) has GRADUAL boundaries — its mask is a saliency region, not a sharp contour. Every Uneven row carries metadata.coarse_boundary: true; grade Uneven localization by containment / center-hit, never tight IoU. All other classes have sharp boundaries and tight boxes.

field	type	meaning
`query`	str	24 surface variants; closed class list; JSON output spec
`image`	Image	the raw grayscale tile photo (no overlays)
`annot`	str	JSON box list (see above), `[]` when defect-free
`reasoning`	null	none — deterministic derivation
`cate` / `task`	str	`B` / `T-B2`
`metadata`	str (JSON)	source, category, `image_sha256`, `image_path`, `r186_record_id`, `defect_type`, `n_instances`, `coarse_boundary`

Verification: every published box list re-derived independently from the mask at assembly — byte-identical on all 1,340 rows; goods all [].

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 deterministically (no LLM/teacher; reasoning is null) from AI4Manufacturing/186 (revision 2117f8e) — Magnetic-Tile-Defect, Huang et al., "Surface defect saliency of magnetic tile", The Visual Computer 2020: 1,344 grayscale magnetic-tile images, 5 defect classes (Blowhole, Break, Crack, Fray, Uneven) + good, each defective image with a paired pixel saliency mask (binarized here at gray>40, which matches the source defect_area_fraction). Generator: annotate/186/build_186_derived.py in forge_model; machine gates: annotate/186/verify_186.py (all green at build time).

Source-data exclusion (counted): 4 MT_Uneven rows ship ALL-ZERO masks in the source dataset (defect_area_fraction = 0.0) — an anomalous label with no localizable GT. They are excluded from every derived set.

Query diversity. The query field is drawn from a fixed pool of surface variants for this task (paraphrases preserving the task and answer format), selected by an independent per-record hash. A machine gate checks that no template correlates with the gold (worst z-scores reported above).

The repository name is an internal task code (the source dataset's code is 186).

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`	`dilate_cc`
`binarisation`	`gt:40`
`connectivity`	`4`
`merge`	`mask_dilate:1pct`
`min_area_px`	`15`
`max_instances`	`8`
`artifact`	`coarse`
`fill_floor`	`None`
`legibility_floor_px`	`None`
`min_side_floor_px`	`None`
`spec_sha`	`22cd9e70b8008b05`

Provenance and verification


records	1,340
carrying a geometry block	1,340 / 1,340
instances per record	`0`: 952, `1`: 352, `2`: 29, `3`: 1, `4`: 3, `5+`: 3
total instances	440
image dimensions	265×375 (6), 123×286 (5), 122×285 (4)
`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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