| --- |
| 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`](https://huggingface.co/datasets/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`](https://huggingface.co/datasets/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`). |
|
|
