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Roles

Roles: canon repo — annot is the source label, kept machine-parseable as the gold for verification and reward parsing; there is no reasoning column and this repo is not itself a training view. Derived repos (-annotated, -grounding, -region, -mcq) each state their own regime on their own card. Geometry for every record lives in metadata.geometry (below).

186

Magnetic-tile defect classification (5 defects + good; saliency mask GT). Category B, task T-B2, in the unified Smart-Manufacturing SFT schema.

The repository name is an internal task code. See Provenance below for the underlying dataset.

Records

1,344 records (train=1344). Pixel masks are embedded as a mask image column.

Unified SFT schema

field	type	meaning
`query`	str	the question / instruction (model input)
`image`	Image	the input image (bytes embedded); for multi-image rows, a preview of the first view
`images`	list[Image]	(multi-image rows) all input views / modalities for the row, bytes embedded
`annot`	str	the answer — for this dataset: plain-text `{label, defect_type}` — `{good, null}` or `{anomalous, <defect>}` (one of Blowhole/Break/Crack/Fray/Uneven). The paper's task is pixel saliency segmentation; that mask is deferred GT in the `mask` column, with segmentation info (`mask_path`, `defect_area_fraction`) in `metadata` — see Task, mask & split below
`reasoning`	null	no native CoT in these datasets
`cate`	"B"	SFT category
`task`	"T-xx"	unified task id
`metadata`	str (JSON)	split, provenance, `image_path`, `image_sha256` (dedup key)
`mask`	Image \| null	(T-B1/T-B2 only) the pixel ground-truth mask, bytes embedded
`masks`	list[Image]	(multi-image T-B1 / D21) per-view masks aligned with `images` (None where a view has no defect), or multi-region masks

Task, mask & split

What this is. Magnetic-Tile-Defect (Huang et al., "Surface defect saliency of magnetic tile", The Visual Computer 2020) — 1,344 grayscale magnetic-tile images across 6 subsets: 5 defect types (Blowhole, Break, Crack, Fray, Uneven) + MT_Free (defect-free / good). Each image ships a paired pixel-level ground-truth mask.

The paper's own task is saliency SEGMENTATION (segmenting the defect region); the pixel masks are that ground truth. This release instead frames the image-level task as defect classification (the dataset is organized by defect class): query (our template) asks whether the tile is good or anomalous and, if anomalous, to name the defect type from the 5 classes; annot is {label, defect_type} ({good, null} / {anomalous, <defect>}). The query does not ask for a mask.

Segmentation (the paper's task — kept as deferred GT). The pixel saliency mask is kept in the mask column as localization ground truth (anomalous images only; good images have mask=null). Per-image segmentation info is in metadata: mask_path (source mask) and defect_area_fraction (fraction of pixels labelled defect; 0 for good). A text-output model cannot emit a pixel mask, so segmentation is deferred.

Split. No upstream train/val/test split -> single train. Class counts: Free (good) 952, Blowhole 115, Uneven 103, Break 85, Crack 57, Fray 32 (total 1,344).

License. No formal license in the source; released for research use — please cite Huang et al. 2020.

Query text — pooled paraphrases (v2)

Every record's query is drawn from common/vision_query_pools.json[F2a/label_type], a pool of 39 gate-verified paraphrases of the shipped wording, assigned by a stable hash of the source image path and recorded as metadata.query_template (39 templates in use, top share 3.3%).

The opening role sentence is drawn separately (metadata.query_role, a 10-way hand-written pool _role/sentence; index 0 is this repository's own sentence, index 1 is none); the subject sentence is this repository's own, verbatim, on every record. Role and ask are hashed independently.

Template 1 is v1's wording byte for byte (3 records keep it); the pass asserted that on every record before rewriting anything.

Template ↔ gold independence on this build: 1,344 records, 39 templates, worst template p = 0.026, alpha 2.6e-04, 0 flagged; 10 roles, worst role p = 0.0767, 0 flagged → PASS.

Frame-size floor (common/lazy_floors.py, the standing (width, height)-only row): balanced accuracy 0.562 vs 0.500 chance (plain 0.579 vs 0.708 majority; permutation p = 0.005, 200 shuffles), 1225 distinct frame sizes — a shortcut of +6.2 pp balanced, report against it (5-fold cross-validation: the repository ships one split).

Answers, images, masks, split and every other field are byte-identical to v1: this revision was issued from the published parquet itself (tools/requery_published.py), not rebuilt from source, and the pixel-identity guard ran on the embedded images (§8 below).

Provenance

Underlying dataset: Magnetic-Tile-Defect. Upstream license: other (research use; cite Huang et al. 2020) (this card is license: other; respect the upstream terms). Converted read-only from the raw source into the unified schema; conversion code under 186/ (with publish/push_to_hf.py) in AI4Manufacturing/forge_model.

Overlap / de-duplication (§8)

None notable.

Two identities, and they answer different questions. metadata.image_sha256 hashes the file bytes: it finds byte-identical copies and is blind to a re-encode. metadata.pixel_sha256 hashes the decoded image (mode | size | pixels): it finds the same photograph saved twice. Only the second one settles whether an image is duplicated.

Measured at build time, not asserted afterwards — a violation aborts the build and names the offending records:


images checked	1,344
distinct by decoded pixels	1,344
images carrying more than one record	0
images on both sides of the split	0

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 (coords_frame: "record_image"). 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	1,344
carrying a geometry block	1,344 / 1,344
instances per record	`0`: 956, `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]

Computed from this repo's own 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.

⚠ The 16px floor applies at the RENDER, not at native

min_side_px is in native pixels. The model does not see native: Qwen2-VL caps by megapixels AND snaps each dimension to a multiple of 28. So min_side_px >= 16 is the floor tested in the wrong frame. Measured on this repo:


native → rendered (qwen2_vl @ 2.36MP)	103×289 → 112×280, 105×283 → 112×280, 109×291 → 112×280
shipped boxes	440
legible at that render (>=16px there)	256 (58.2%)

⚠ An earlier version of this section reported the inverse — boxes clearing 16px at native and failing at the render — and that number was misleading. It is frame-relative: publishing at a larger native size lets more boxes clear 16 in the published frame, so more can "fail", which penalises exactly the choice that helps. Measured on 179: publishing native (3024) means a box needs >=32px native to be legible at the render and 86.7% qualify; the previous 1024 publish needed >=47px native and only 69.5% qualified. The native republish improved rendered legibility by 17 points while the old metric scored it as 12.5% "broken". The figure above is the comparable one.

Nothing in the data is frame-dependent — geometry is native and complete. Use forge_model/common/adapt_engine.py, which applies the floor at whatever size the consumer renders.

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 this repo's 103×289 is rendered 112×280 and native-pixel boxes are then wrong by a few pixels. forge_model/common/adapt_engine.py regenerates coordinates for a target render size, re-derives counts, and drops records whose gold no longer holds there.

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