README.md

---
tags:
- smart-manufacturing
- sft
- industrial
- vision
- anomaly-detection
license: other
pretty_name: "179-grounding"
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-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`](https://huggingface.co/datasets/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`](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`).