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179-grounding
Detection-format defect localization on aero-engine blades — 2160 items (1011 good + 1149
defective), derived deterministically from the binary segmentation masks of
AI4Manufacturing/179. The model outputs
boxes as text; defect-free blades must output [] — detection rejection is part of the task.
Task
"Locate every defect." annot is a JSON list of {"type": ..., "bbox_xywh": [x, y, w, h]} in the
image's pixel coordinates (origin top-left; the image is 1024-long-side, see metadata.image_wh),
one entry per defect instance (connected components after proximity grouping: dilation radius
~1% of the min dimension merges fragments of one physical defect; sub-15-px groups denoised), sorted
(type, x, y). Good blades have annot = [] (1011). 438 defective images are multi-instance
(≥2 boxes; breakdown in particular is scattered spots). The query states the coordinate convention,
the closed class list (ablation, breakdown, fracture, groove), and the empty-list rule.
| field | type | meaning |
|---|---|---|
query |
str | 24 surface variants; closed class list; JSON output spec |
image |
Image | the blade photo (JPEG, 1024 long side; no overlays) |
annot |
str | JSON box list (see above), [] when defect-free |
reasoning |
null | none — deterministic derivation |
cate / task |
str | B / T-B2 |
metadata |
str (JSON) | source, image_sha256, image_path, image_wh, domain_condition, r179_record_id, defect_type, n_instances |
Verification: every published box list re-derived independently from the mask at build — byte-identical
on all 2160 rows; goods all []; every box within image bounds.
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).
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