The MVTec AD 2 Dataset: Advanced Scenarios for Unsupervised Anomaly Detection
Paper • 2503.21622 • Published
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D26
Binary anomaly detection (public test; private GT withheld -> dropped). 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
3,914 records (test=1084 · train=2528 · validation=302). 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: the plain-text image-level label good or anomalous (public MVTec-AD-2 is binary — no fine-grained defect types). Pixel-level localization is a separate, deferred task whose target is the mask column — 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-2 (Heckler-Kram et al., arXiv:2503.21622, 2025) — advanced unsupervised industrial anomaly detection & localization over 8 scenarios, emphasizing hard cases (transparent / overlapping objects, dark-field & back-light illumination, extremely small defects). The public data is binary at the image level — good vs anomalous — with no fine-grained defect types.
Query & answer (this repo's SFT task). query is our own instruction template (the raw dataset ships no
natural-language question). It asks only whether the object is good or anomalous; annot is the
plain-text answer good or anomalous. 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 / segmentation models).
Lighting conditions. Each scene is captured under several lighting conditions — the bad filename suffixes
regular / overexposed / underexposed / shift_* are lighting variants, not defect types — so MVTec-AD-2
can test robustness to real-world illumination shifts.
Split & withheld data. train = normal only; validation = normal only; test = the public test set
(test_public: good + anomalous, masks on the anomalous ones). The private test sets (test_private,
test_private_mixed) keep their ground truth on the official evaluation server and are not included here
(not fabricated). See Records for exact counts.
Provenance
Underlying dataset: MVTec-AD-2. 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: D26/convert_d26.py, published with publish/push_to_hf.py, both in AI4Manufacturing/forge_model.
Overlap / de-duplication (§8)
None notable. 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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