You need to agree to share your contact information to access this dataset
This repository is publicly accessible, but you have to accept the conditions to access its files and content.
This dataset is released for research use. Access is reviewed and granted manually by the maintainers. Please state your name, affiliation, and intended use.
Log in or Sign Up to review the conditions and access this dataset content.
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.
- Downloads last month
- 47