tags:
- smart-manufacturing
- sft
- industrial
- vision
license: other
pretty_name: '187'
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.
187
Metal-parts binary anomaly detection & localization. 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
1,346 records (test=458 · train=888). 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 (MPDD's benchmark task is binary AD). The source per-category defect-type sub-label is kept in metadata.defect_type; 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. MPDD (Jezek et al., ICUMT 2021) — Metal Parts Defect Detection: MVTec-AD-style unsupervised anomaly detection & localization over 6 metal-part categories (bracket_black / bracket_brown / bracket_white, connector, metal_plate, tubes), trained on defect-free images only. The benchmark task is binary (normal vs anomaly) at the image level plus pixel-level localization.
Query & answer (this repo's SFT task). query is our own instruction template (the dataset ships no
natural-language question). It names the metal part and asks only whether it is good or anomalous;
annot is the plain-text answer good or anomalous. The query does not ask for a pixel mask.
Defect types (secondary, in metadata — not the answer). MPDD organizes 5 of its 6 categories' test anomalies
into named defect folders (e.g. hole, scratches, major_rust, parts_mismatch); the tubes category is not
sub-typed (a single anomalous folder). These are the authors' organization, not the benchmark target, so the
source label is kept in metadata.defect_type (null for normal, the folder name for anomalies) rather than in annot.
Mask (deferred localization GT). 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, so
localization is deferred (mask kept as GT).
Split. train = normal images only (888); test = normal + anomalous (176 normal + 282 anomalous = 458).
No separate validation split. Standard unsupervised one-class protocol.
Provenance
Underlying dataset: MPDD. 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: 187/convert_d87.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.