| --- |
| 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`](https://github.com/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. |
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|
