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186-region

Region-conditioned defect typing on magnetic tiles — 1,583 items, derived deterministically from the pixel saliency masks of AI4Manufacturing/186. Exact-match gradable (closed type list + no defect) → SFT and RLVR-ready.

Task

"An operator points at a region — what defect, if any, is there?" One item per defect instance (438 positives: Blowhole 115, Break 115, Uneven 101, Crack 70, Fray 37) plus 1,145 clean-region negatives teaching rejection (every good record + ~~half of defective records; clean-majority 72% is disclosed — reweight at training time if you want balance). The region is conveyed in one of two modes (~~50/50, metadata.region_mode; overlay 801 / bbox_text 782):

overlay — a red rectangular ring drawn on the image around the region (ring thickness scales with image size; regions padded to >=6% of the min dimension so they stay visible).
bbox_text — the raw image plus the region as a native-pixel box [x, y, w, h] (origin top-left) in the query text.

Clean boxes sample size AND position from the emitted positive population (median clean/positive box area ratio 0.94), so box geometry separates nothing. Golds are unambiguous: instance boxes containing another instance's pixels are skipped in bbox-text mode (2). Gold = the type name exactly as in the query's closed list, or no defect. Query pools: 16 variants per mode (template x clean/defect independence: worst z = 2.61). Verified: zero defect pixels inside any clean box.

Uneven disclosure. Uneven boundaries are gradual (saliency GT); positive Uneven regions carry metadata.coarse_boundary: true.

field	type	meaning
`query`	str	16 variants per mode; closed class list
`image`	Image	raw tile photo, or tile with ONE red rectangular ring (overlay mode)
`annot`	str	`Blowhole` / `Break` / `Crack` / `Fray` / `Uneven` / `no defect`
`reasoning`	null	none — deterministic derivation
`cate` / `task`	str	`B` / `T-B2`
`metadata`	str (JSON)	source, category, `image_sha256`, `image_path`, `r186_record_id`, `region_mode`, `bbox_xywh`, `instance_index`, `gold`, `coarse_boundary`

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	1,583
carrying a geometry block	1,583 / 1,583
instances per record	`0`: 952, `1`: 527, `2`: 71, `3`: 4, `4`: 13, `5+`: 16
total instances	813
image dimensions	265×375 (7), 421×296 (6), 122×285 (5)
`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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