| --- |
| tags: |
| - smart-manufacturing |
| - sft |
| - industrial |
| - vision |
| license: other |
| pretty_name: "186" |
| 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. |
| --- |
| |
| # 186 |
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| Magnetic-tile defect classification (5 defects + good; saliency mask GT). Category **B**, task **T-B2**, in the unified Smart-Manufacturing SFT schema. |
|
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| > The repository name is an internal task code. See **Provenance** below for the underlying dataset. |
|
|
| ## Records |
|
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| **1,344** records (train=1344). Pixel masks are embedded as a `mask` image column. |
|
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| ## 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}` — `{good, null}` or `{anomalous, <defect>}` (one of Blowhole/Break/Crack/Fray/Uneven). The paper's task is pixel saliency segmentation; that mask is deferred GT in the `mask` column, with segmentation info (`mask_path`, `defect_area_fraction`) in `metadata` — 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 |
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|
| **What this is.** Magnetic-Tile-Defect (Huang et al., *"Surface defect saliency of magnetic tile"*, The Visual |
| Computer 2020) — 1,344 grayscale magnetic-tile images across 6 subsets: 5 defect types (Blowhole, Break, Crack, |
| Fray, Uneven) + `MT_Free` (defect-free / good). Each image ships a paired pixel-level ground-truth mask. |
|
|
| **The paper's own task is saliency SEGMENTATION** (segmenting the defect region); the pixel masks are that ground |
| truth. This release instead frames the image-level task as defect **classification** (the dataset is organized by |
| defect class): `query` (our template) asks whether the tile is **good** or **anomalous** and, if anomalous, to name |
| the defect type from the 5 classes; `annot` is `{label, defect_type}` (`{good, null}` / `{anomalous, <defect>}`). |
| **The query does not ask for a mask.** |
|
|
| **Segmentation (the paper's task — kept as deferred GT).** The pixel saliency mask is kept in the `mask` column as |
| localization ground truth (anomalous images only; good images have `mask`=null). Per-image segmentation info is in |
| `metadata`: `mask_path` (source mask) and `defect_area_fraction` (fraction of pixels labelled defect; 0 for good). |
| A text-output model cannot emit a pixel mask, so segmentation is deferred. |
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| **Split.** No upstream train/val/test split -> single `train`. Class counts: Free (good) 952, Blowhole 115, |
| Uneven 103, Break 85, Crack 57, Fray 32 (total 1,344). |
|
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| **License.** No formal license in the source; released for research use — please cite Huang et al. 2020. |
|
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| ## Provenance |
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| Underlying dataset: **Magnetic-Tile-Defect**. Upstream license: **other (research use; cite Huang et al. 2020)** (this card is `license: other`; respect the upstream terms). Converted read-only from the raw source into the unified schema; conversion script: `186/convert_d86.py`, published with `publish/push_to_hf.py`, both in [`AI4Manufacturing/forge_model`](https://github.com/AI4Manufacturing/forge_model). |
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| ## Overlap / de-duplication (§8) |
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| 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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|
