README.md

metadata

tags:
  - smart-manufacturing
  - sft
  - industrial
  - vision
license: other
pretty_name: 192-single
extra_gated_fields:
  Name: text
  Affiliation: text
  Intended use: text
extra_gated_prompt: >-
  This dataset is released for **research use**. Access is reviewed and granted
  **manually** by the maintainers. Please state your name, affiliation, and
  intended use.

192-single

Multi-view industrial anomaly detection — SINGLE-IMAGE version (one camera view per row; image-level binary; per-image mask 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

151,050 records (test=114585 · train=36465). 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: the plain-text image-level label good or anomalous. Real-IAD labels each view separately (a view of an NG object may itself be good — the invisible-view effect), so this is faithful per image. The per-image defect mask (None for good) is attached but NOT referenced in the query; the defect code/name are in metadata.defect_code/defect_name — 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 imaged from five synchronised camera views (C1 top-down + C2-C5 at 45°) on a real production line, with pixel defect masks. This is the 256-px release (realiad_256).

Two published versions (same images, different unit). Real-IAD supports an image-level and a sample-level protocol, so we publish two datasets from the same underlying images:

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

Query & answer. query (our own template, per category) asks only good vs anomalous; annot is the plain-text label. Real-IAD labels each view separately, so a view of a defective object on which the defect is not visible is itself labelled good (the invisible-view effect) — the per-image label is faithful. annot = anomalous iff that image carries a defect annotation.

Mask (deferred localization GT). The per-image defect mask (None for good) is attached in the mask column but not referenced in the query. The defect type is metadata.defect_code (one of Real-IAD's 8 types AK/BX/CH/HS/PS/QS/YW/ZW) with a plain-English metadata.defect_name; the camera view and sample id are metadata.camera_view / metadata.sample_id.

Split. The dataset's main split is published: train = normal-only (36,465 images; the unsupervised-AD protocol trains only on good images), test = mixed (114,585 images). Total 151,050 images. (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 the object-level companion 192-object (this is the per-image flattening) — 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.