You need to agree to share your contact information to access this dataset
This repository is publicly accessible, but you have to accept the conditions to access its files and content.
This dataset is released for research use. Access is reviewed and granted manually by the maintainers. Please state your name, affiliation, and intended use.
Log in or Sign Up to review the conditions and access this dataset content.
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).
- Downloads last month
- 9