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179

Aero-engine blade anomaly detection under domain shift (4 defects; segmentation GT). Category B, task T-B1, in the unified Smart-Manufacturing SFT schema.

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

Records

2,160 records (test=1639 · train=521). 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 ablation/breakdown/fracture/groove). Each image's domain-shift condition (background/illumination/same/view) is in `metadata.domain_condition`; the pixel `mask` is deferred localization GT — 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. AeBAD (Zhang et al., arXiv 2304.02216, Industrial Anomaly Detection with Domain Shift) — a real-world Aero-engine Blade Anomaly Detection dataset. This repo converts the single-blade sub-dataset AeBAD-S (the video sub-dataset AeBAD-V is not included). Its defining feature is a domain shift between train (normal) and test, driven by changes in illumination and viewpoint; targets are also unaligned and at varying scales.

Query & answer (this repo's SFT task). query is our own instruction template (the dataset ships no question); it names the 4 defect types and asks for the label + defect type. annot = plain-text {good, null} or {anomalous, <defect>}, one of ablation / breakdown / fracture / groove.

Domain condition (in metadata). Every image is captured under one of 4 conditions — background, illumination, same (aligned/in-distribution), view — recorded in metadata.domain_condition. This is the axis the dataset was built to stress; it is provenance, not part of the answer.

Mask (deferred localization GT). Each anomalous image ships a pixel ground-truth mask (mask column), matched by basename under ground_truth/<defect>/<condition>/, with defect_area_fraction in metadata; good images have mask=null. Localization is deferred.

Split. train = 521 normal images (defect-free, across conditions); test = 490 good + 1,149 anomalous (4 defect types × 4 conditions) = 1,639. Standard one-class AD protocol with a domain-shifted test set.

Provenance

Underlying dataset: AeBAD-S. Upstream license: other (research use; Zhang et al., arXiv 2304.02216) (this card is license: other; respect the upstream terms). Converted read-only from the raw source into the unified schema; conversion script: 179/convert_d79.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`	`fine`
`fill_floor`	`None`
`legibility_floor_px`	`None`
`min_side_floor_px`	`None`
`spec_sha`	`ff6b39fac2294808`

Provenance and verification


records	2,160
carrying a geometry block	2,160 / 2,160
instances per record	`0`: 1,011, `1`: 710, `2`: 171, `3`: 75, `4`: 189, `5+`: 4
total instances	2,053
image dimensions	3024×3024 (2,160)
`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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