| --- |
| 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`](https://github.com/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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|
