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D20
One-class anomaly detection with per-category defect naming. 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
5,354 records (test=1725 · train=3629). 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 {label, defect_type} matching the query form — {good, null} or {anomalous, <defect>} where <defect> is the specific per-category defect name (e.g. {anomalous, broken_large}). The mask column is 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-AD (Bergmann et al., CVPR 2019) — the standard unsupervised industrial anomaly detection & localization benchmark: 15 categories (10 objects + 5 textures), ~48 fine-grained, per-category defect types, pixel-precise ground-truth masks.
Query & answer (this repo's SFT task). query is our own instruction template (the raw dataset ships no
natural-language question — only folder labels + masks). It names the object category, asks the model to decide
good vs anomalous, and, if anomalous, name the defect type from that category's own closed set — the
valid defect names are enumerated in the query. The answer form is {label, defect_type} — exactly what
annot holds ({good, null} / {anomalous, <defect>}). The query does not ask for a pixel mask.
Mask (localization ground truth for a separate, deferred task). Each anomalous image ships a single binary
ground-truth mask (mask column; 1 = defect, 0 = background); normal images have mask=null. A text-output
model cannot emit a pixel mask directly, so this release keeps the masks as ground truth but does not frame
localization as the query task (deferred; masks remain for pixel-level evaluation or segmentation models).
Split. train = normal images only; test = normal + anomalous (all defect types). MVTec-AD ships no
separate validation split. Standard unsupervised one-class protocol.
Provenance
Underlying dataset: MVTec-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: D20/convert_d20.py, published with publish/push_to_hf.py, both in AI4Manufacturing/forge_model.
Overlap / de-duplication (§8)
Subset of MMAD's image pool; do not put both in train+eval. 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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