README.md

---
tags:
- smart-manufacturing
- sft
- industrial
- vision
license: other
pretty_name: "186-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.
---

# 186-grounding

Detection-format defect localization on magnetic tiles — **1,340 items** (952 good + 388 defective),
derived **deterministically** from the pixel saliency masks of
[`AI4Manufacturing/186`](https://huggingface.co/datasets/AI4Manufacturing/186). The model must output
**boxes as text**; defect-free tiles 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
**native pixel coordinates** (origin top-left), one entry per defect **instance** (connected
components after proximity grouping: dilation radius ~1% of min dimension merges fragments of one
physical defect; sub-15-px groups denoised but counted — 14; if all of a record's groups fall under
the floor its union box is emitted instead — 2 records), sorted `(type, x, y)`. Good tiles have
`annot = []` (952). The query states the coordinate convention, the closed class list
(Blowhole, Break, Crack, Fray, Uneven), and the empty-list rule. Query pool: 24 surface variants
(template x good/defective independence: worst z = 1.88).

**Uneven disclosure.** `Uneven` (grind-unevenness) has GRADUAL boundaries — its mask is a saliency
region, not a sharp contour. Every Uneven row carries `metadata.coarse_boundary: true`; grade Uneven
localization by **containment / center-hit**, never tight IoU. All other classes have sharp
boundaries and tight boxes.

| field | type | meaning |
|---|---|---|
| `query` | str | 24 surface variants; closed class list; JSON output spec |
| `image` | Image | the raw grayscale tile photo (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, category, `image_sha256`, `image_path`, `r186_record_id`, `defect_type`, `n_instances`, `coarse_boundary` |

Verification: every published box list re-derived independently from the mask at assembly —
byte-identical on all 1,340 rows; goods all `[]`.

## 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/186`](https://huggingface.co/datasets/AI4Manufacturing/186) (revision `2117f8e`) —
Magnetic-Tile-Defect, Huang et al., *"Surface defect saliency of magnetic tile"*, The Visual Computer 2020:
1,344 grayscale magnetic-tile images, 5 defect classes (Blowhole, Break, Crack, Fray, Uneven) + good,
each defective image with a paired pixel saliency mask (binarized here at gray>40, which matches the
source `defect_area_fraction`). Generator: `annotate/186/build_186_derived.py` in `forge_model`;
machine gates: `annotate/186/verify_186.py` (all green at build time).

**Source-data exclusion (counted):** 4 `MT_Uneven` rows ship ALL-ZERO masks in the source dataset
(`defect_area_fraction = 0.0`) — an anomalous label with no localizable GT. They are excluded from
every derived set.

**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 `186`).