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
Magnetic-tile defect classification (5 defects + good; saliency mask GT). Category B, task T-B2, in the unified Smart-Manufacturing SFT schema.
The repository name is an internal task code. See Provenance below for the underlying dataset.
Records
1,344 records (train=1344). 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} — {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
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.
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).
License. No formal license in the source; released for research use — please cite Huang et al. 2020.
Provenance
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.
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.