tags:
- smart-manufacturing
- sft
- industrial
- vision
- anomaly-detection
license: other
pretty_name: 179-mcq
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.
179-mcq
Mask-grounded multiple choice (Set-of-Mark style) for aero-engine-blade defect localization —
1137 items, derived deterministically from the binary segmentation masks of
AI4Manufacturing/179. Exact-match gradable
→ SFT and RLVR-ready.
Task
One item per eligible defective record. The image is a 2×2 grid of views A–D of the same
blade photo; each view overlays one red candidate region mask. Exactly one view overlays the true
defect mask — in both location and extent (the query says so, naming the defect type to locate).
annot is the correct letter. The three negatives per item are hard by construction: shift
(translated), fliplr/flipud/rot180 (mirrored), dilate (over-grown ≥2.5×), erode (shrunk).
Every negative is guaranteed wrong (IoU vs truth < 0.35 except dilate, wrong by extent); panels are
mutually distinct (pairwise IoU < 0.7); negative kinds are assigned to slots by an independent salted
hash. Gold letters: A 294 / B 262 / C 311 / D 270. Per-type coverage: ablation 169, breakdown 329, fracture 389, groove 250.
Records are skipped (confidence over coverage) when the gold mask is under ~30 visible px after
panel downscale, fewer than 3 sound negatives are constructible, or the mask covers >35% of the
frame. Skipped defects remain fully covered by 179-grounding / 179-region.
| field | type | meaning |
|---|---|---|
query |
str | 16 variants; names the defect type to locate; "answer with the letter only" |
image |
Image | 2×2 composite, panels A–D |
annot |
str | A / B / C / D |
reasoning |
null | none — deterministic derivation |
cate / task |
str | B / T-B2 |
metadata |
str (JSON) | source, image_sha256, defect_type, gold_letter, panel_tags, area_pct |
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/179 — AeBAD (Aero-engine
Blade Anomaly Detection, AeBAD_S subset; Zhang et al., "Industrial Anomaly Detection with Domain
Shift"): 2,160 aero-engine-blade surface photos, 4 defect types (ablation, breakdown, fracture,
groove) + good, each anomalous image with a paired binary pixel segmentation mask (binarized
here at gray>40, which reproduces the source defect_area_fraction exactly). Generator:
annotate/179/build_179_derived.py in forge_model; machine gates:
annotate/179/verify_179.py (all green at build time).
Resolution. Source photos are 3024×3024. Every image here is downscaled to a 1024 long side
(LANCZOS; masks NEAREST) and all coordinates are in that pixel space — see metadata.image_wh. This
matches common VLM input sizes and keeps the repo compact; a native-resolution rebuild is a
deterministic option (DOWNSCALE=None).
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
179).