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D21
Logical & structural anomaly detection (multi-region localization 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
3,651 records (test=1568 · train=1778 · validation=305). Pixel masks are embedded as a mask image column (multi-region masks).
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 {label, defect_type} matching the query form — {good, null} / {anomalous, logical} / {anomalous, structural} (not a JSON object). The pixel masks (mask/masks) are localization ground truth for a separate, deferred task — see Task, 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, mask & split
What this is. MVTec-LOCO-AD (Bergmann et al., IJCV 2022, "Beyond Dents and Scratches") — unsupervised anomaly detection over 5 product categories, distinctive for splitting anomalies into logical (violations of logical constraints: missing / extra / misplaced components, wrong count or arrangement) and structural (local defects: scratches, dents, contamination).
Query & answer (this repo's SFT task). query is our own instruction template (the raw dataset ships no
natural-language question). It asks the model to decide good vs anomalous and, if anomalous, classify the
defect type as logical or structural, answering in the form {label, defect_type} — exactly what
annot holds. The query does not ask for a pixel mask.
Masks (localization ground truth for a separate, deferred task). Every anomalous image ships pixel-precise
ground-truth masks: mask = the first anomalous region, masks = the list of all regions. One image can have
several disjoint anomalous regions — most have 1, but some logical anomalies have up to 15 (e.g. each wrong
compartment is its own region). Normal images have mask=null, masks=[]. Producing a pixel mask is not
something a text-output model emits directly, so this release keeps the masks as ground truth but does not
frame localization as the query task (it is deferred; the masks remain available for pixel-level evaluation or
segmentation models).
Split. train = normal only; validation = normal only; test = normal + anomalous (logical + structural).
See Records for exact counts. Standard unsupervised protocol with a real held-out validation split.
Provenance
Underlying dataset: MVTec-LOCO-AD. 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 script: D21/convert_d21.py, published with publish/push_to_hf.py, both in AI4Manufacturing/forge_model.
Overlap / de-duplication (§8)
Subset of MMAD's image pool. 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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