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192-object

Multi-view industrial anomaly detection — OBJECT (sample-level) version (5 views per row; object-level binary; per-view masks kept as 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

30,210 records (test=22917 · train=7293).

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: the plain-text object-level label `good` or `anomalous`. One row = one object = five synchronised views in `images` (C1 top + C2-C5 side); the object is `anomalous` iff it is an NG sample even if some views look `good` (invisible-view effect; per-view labels in `metadata.views`). The per-view masks are aligned in `masks` (None where a view has no defect) but NOT referenced in the query — see Versions, task & 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

Versions, task & split

What this is. Real-IAD (Wang et al., Real-IAD: A Real-World Multi-View Dataset for Benchmarking Versatile Industrial Anomaly Detection, CVPR 2024) — 30 manufactured objects, five synchronised camera views per object (C1 top-down + C2-C5 at 45°), with pixel defect masks. 256-px release (realiad_256).

Two published versions (same images, different unit).

192-object (this repo) — one object (its 5 views) per row, sample-level binary AD (Real-IAD's headline S-AUROC): the five views are aggregated into one object decision.
192-single — one camera view per row, image-level binary AD (I-AUROC). They are the same photos re-grouped — keep the two on the same side of any train/eval split.

One row = one object. images holds the object's five views in order C1..C5 (image scalar = the C1 top-down view, a preview); masks holds the per-view defect masks aligned with images (None where that view has no defect), attached but not referenced in the query.

Query & answer. query (our own template, per category) asks whether the object is good or anomalous; annot is the plain-text label. The object is anomalous iff it is an NG sample — even if some of its views individually look good (the invisible-view effect: a defect may be visible from only one or two angles). The per-view labels (which view is good/anomalous, whether it has a mask) are in metadata.views; the object-level defect type is metadata.defect_code / defect_name (Real-IAD's 8 types AK/BX/CH/HS/PS/QS/YW/ZW).

Split. The main split is published: train = normal-only objects (unsupervised-AD protocol), test = mixed. ~30,210 objects total (151,050 images / 5 views). (Real-IAD also defines FUIAD noisy-train splits; only the main split is published.)

Provenance

Underlying dataset: Real-IAD. Upstream license: CC BY-NC-SA 4.0 (this card is license: other; respect the upstream terms). Converted read-only from the raw source into the unified schema; conversion code under 192/ (with publish/push_to_hf.py) in AI4Manufacturing/forge_model.

Overlap / de-duplication (§8)

Same underlying images as 192-single (this is the multi-view grouping) — keep the two on the same side of any split. Published main split only. Each record carries metadata.image_sha256 so overlapping images can be kept entirely on one side of a train/eval split.

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Total file size:

5.05 GB