README.md

---
tags:
- smart-manufacturing
- sft
- industrial
- vision
license: other
pretty_name: "182"
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.
---

# 182

Multi-modal synthetic candy AD (8 categories; multi-label; all 8 modalities per record). Category **B**, task **T-B2**, in the unified Smart-Manufacturing SFT schema.

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

## Records

**9,200** records (test=400 · train=8000 · validation=800). 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 multi-label `{label, [defect_types]}` — `{good, null}` or `{anomalous, [<type>, ...]}` over bumps/colors/dents/normals (a single object can carry several types, so the types are a bracketed list), from each sample's metadata flags. One record = **one query over all 8 modality images of the same candy**: the `images` sequence column holds them in a fixed order — 6 RGB lightings (image_0..5), depth, normals (`metadata.modalities` gives the per-position kind); the `image` scalar is image_0. The combined anomaly `mask` is deferred GT — see **Task, modalities, 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, modalities, mask & split

**What this is.** Eyecandies (Bonfiglioli et al., *"The Eyecandies Dataset for Unsupervised Multimodal Anomaly
Detection and Localization"*, ACCV 2022) — a synthetic (Blender-rendered) candy anomaly-detection dataset. 8 candy
categories; each object is rendered under **6 lighting conditions** (`image_0..5`) plus a **depth** map and a
**normals** map, and defects carry pixel masks. Four defect types: **bumps, colors, dents, normals** (multi-label).

**Task & answer.** Multi-label defect classification + localization. The dataset ships **no natural-language
query** (only per-sample anomaly flags in `metadata.yaml`), so `query` is our own template: it names the candy
category and asks whether it is **good** or **anomalous** and, if anomalous, to list every defect type present. `annot`
is `{label, [defect_types]}` — a single image can carry **several** defect types, so the types are a bracketed list:
`{good, null}` / `{anomalous, [bumps]}` / `{anomalous, [bumps, colors, dents, normals]}`. Labels come from each
sample's `metadata.yaml` flags. **The query does not ask for a mask.**

**All modalities are embedded — one query over 8 images.** Each record bundles **all 8 modality images of the same
candy** so the model judges from the full multi-modal view. The **`images`** sequence column holds them in a fixed
order: **6 RGB lightings (`image_0..5`), then `depth`, then `normals`** — `metadata.modalities` lists the kind at each
position and `metadata.n_images` the count (8). The **`image`** scalar column is `image_0` (the primary RGB, for
dataset viewers; its bytes are deduplicated against `images[0]` in the parquet). Depth is a 16-bit map and normals is a
surface-normal map; both are stored as images. (Earlier revisions shipped the extra modalities as loose `assets/`
files — they are now embedded in `images` instead.)

**Mask (deferred GT).** The combined anomaly mask is the deferred localization ground truth in the `mask` column
(anomalous images only; good = null); `metadata.defect_area_fraction` gives its area. A text model cannot emit a pixel
mask, so segmentation is deferred.

**Split (why anomalies are only in `test`).** Eyecandies is an **unsupervised** anomaly-detection benchmark, so by
design the training data is **normal-only**: `train` and `validation` contain **100% good** samples and anomalies
appear **only** in `test`. This is the source's own composition (verified from every sample's `metadata.yaml` flags),
not a conversion artefact. The source's `test_private` split ships no masks/labels (GT withheld) and is **dropped**.
Counts: train 8,000 good, validation 800 good, test 400 (210 good / 190 anomalous) = **9,200**; the private test
(3,200) is excluded.

## Provenance

Underlying dataset: **Eyecandies**. Upstream license: **other (research use; Eyecandies, Bonfiglioli et al. ACCV 2022)** (this card is `license: other`; respect the upstream terms). Converted read-only from the raw source into the unified schema; conversion script: `182/convert_d82.py`, published with `publish/push_to_hf.py`, both in [`AI4Manufacturing/forge_model`](https://github.com/AI4Manufacturing/forge_model).

## Overlap / de-duplication (§8)

Synthetic (Blender-rendered) — no image overlap with the real-image AD sets. Each record carries `metadata.image_sha256` so overlapping images can be kept entirely on one side of a train/eval split.