| --- |
| tags: |
| - smart-manufacturing |
| - sft |
| - industrial |
| - vision |
| - anomaly-detection |
| license: other |
| pretty_name: "179-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. |
| --- |
| |
| # 179-region |
|
|
| Region-conditioned defect typing on aero-engine blades — **3634 items**, derived **deterministically** |
| from the binary segmentation masks of |
| [`AI4Manufacturing/179`](https://huggingface.co/datasets/AI4Manufacturing/179). 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** |
| (2048 positives: breakdown 953, groove 466, fracture 389, ablation 240) plus **1586 clean-region negatives** teaching rejection |
| (every good record + ~half of defective records). The region is conveyed in one of two modes |
| (`metadata.region_mode`; overlay 1806 / bbox_text 1828): |
| |
| - **`overlay`** — a red rectangular ring drawn on the image around the region. |
| - **`bbox_text`** — the raw image plus the region as a pixel box `[x, y, w, h]` in the query text. |
| |
| Clean boxes sample **size AND position from the emitted positive population**, so box geometry |
| separates nothing. Instance boxes containing another instance's pixels are skipped in bbox-text mode. |
| Gold = the type name exactly as in the query's closed list, or `no defect`. Verified: zero defect |
| pixels inside any clean box. |
| |
| | field | type | meaning | |
| |---|---|---| |
| | `query` | str | 16 variants per mode; closed class list | |
| | `image` | Image | blade photo, or blade with ONE red rectangular ring (overlay mode) | |
| | `annot` | str | `ablation` / `breakdown` / `fracture` / `groove` / `no defect` | |
| | `reasoning` | null | none — deterministic derivation | |
| | `cate` / `task` | str | `B` / `T-B2` | |
| | `metadata` | str (JSON) | source, `image_sha256`, `image_wh`, `region_mode`, `bbox_xywh`, `instance_index`, `gold` | |
|
|
| ## 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`). |
|
|
