tags:
- smart-manufacturing
- sft
- industrial
- vision
license: other
pretty_name: 186-region
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-region
Region-conditioned defect typing on magnetic tiles — 1,583 items, derived deterministically
from the pixel saliency masks of
AI4Manufacturing/186. Exact-match gradable
(closed type list + no defect) → SFT and RLVR-ready.
Task
"An operator points at a region — what defect, if any, is there?" One item per defect instance
(438 positives: Blowhole 115, Break 115, Uneven 101, Crack 70, Fray 37) plus 1,145 clean-region
negatives teaching rejection (every good record + half of defective records; clean-majority 72%
is disclosed — reweight at training time if you want balance). The region is conveyed in one of two
modes (50/50, metadata.region_mode; overlay 801 / bbox_text 782):
overlay— a red rectangular ring drawn on the image around the region (ring thickness scales with image size; regions padded to >=6% of the min dimension so they stay visible).bbox_text— the raw image plus the region as a native-pixel box[x, y, w, h](origin top-left) in the query text.
Clean boxes sample size AND position from the emitted positive population (median clean/positive
box area ratio 0.94), so box geometry separates nothing. Golds are unambiguous: instance boxes
containing another instance's pixels are skipped in bbox-text mode (2). Gold = the type name exactly
as in the query's closed list, or no defect. Query pools: 16 variants per mode (template x
clean/defect independence: worst z = 2.61). Verified: zero defect pixels inside any clean box.
Uneven disclosure. Uneven boundaries are gradual (saliency GT); positive Uneven regions carry
metadata.coarse_boundary: true.
| field | type | meaning |
|---|---|---|
query |
str | 16 variants per mode; closed class list |
image |
Image | raw tile photo, or tile with ONE red rectangular ring (overlay mode) |
annot |
str | Blowhole / Break / Crack / Fray / Uneven / no defect |
reasoning |
null | none — deterministic derivation |
cate / task |
str | B / T-B2 |
metadata |
str (JSON) | source, category, image_sha256, image_path, r186_record_id, region_mode, bbox_xywh, instance_index, gold, coarse_boundary |
Roles
Roles: this is an answer-only tier — there is no reasoning column; annot is both the machine-parseable gold AND the direct-answer SFT target ('SFT-ready' here means direct imitation of annot in the query-specified format); it is also the exact-match/IoU reward key for RLVR.
Provenance
Built deterministically (no LLM/teacher; reasoning is null) from
AI4Manufacturing/186 (revision 2117f8e) —
Magnetic-Tile-Defect, Huang et al., "Surface defect saliency of magnetic tile", The Visual Computer 2020:
1,344 grayscale magnetic-tile images, 5 defect classes (Blowhole, Break, Crack, Fray, Uneven) + good,
each defective image with a paired pixel saliency mask (binarized here at gray>40, which matches the
source defect_area_fraction). Generator: annotate/186/build_186_derived.py in forge_model;
machine gates: annotate/186/verify_186.py (all green at build time).
Source-data exclusion (counted): 4 MT_Uneven rows ship ALL-ZERO masks in the source dataset
(defect_area_fraction = 0.0) — an anomalous label with no localizable GT. They are excluded from
every derived set.
Query diversity. The query field is drawn from a fixed pool of surface variants for this task
(paraphrases preserving the task and answer format), selected by an independent per-record hash.
A machine gate checks that no template correlates with the gold (worst z-scores reported above).
The repository name is an internal task code (the source dataset's code is
186).