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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)
`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]	(D21 only) 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.

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 script: 186/convert_d86.py, published with publish/push_to_hf.py, both in AI4Manufacturing/forge_model.

Overlap / de-duplication (§8)

None notable. Each record carries metadata.image_sha256 so overlapping images can be kept entirely on one side of a train/eval split.

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	1,344
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]

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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