README.md

---
tags:
- smart-manufacturing
- sft
- industrial
- vision
license: other
pretty_name: "192-object"
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-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`](https://github.com/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.