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. 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 — 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).