README.md

metadata

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.

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.