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191-qa

5,267 questions in three forms, all derived from the canon's geometry.

The task

Three question forms, each with its own gold rule; metadata.qtype says which:

qtype	question	answer
`presence`	is this defect type on the surface?	`Yes` / `No`
`count_class`	how many regions of this type?	a single integer
`leftright`	is one region left or right of another?	`Left` / `Right`

Class names appear in the query as display phrases; where an answer is a class it is the source token. A scorer maps phrase to token through common/display_names.

Roles: this is an answer-only tier — annot is the machine-parseable gold AND the training target, in the format the query specifies; reasoning is null by design, because no teacher was called for this repository.

Split, in words

This repository is a MIXED container: it holds records from the canon's train split and from its validation split, so the HF split is named train and the truth for every record is its own metadata.split and metadata.eval_lock.

where the record comes from	records
canon `train`	5,182
canon `validation`	85
total	5,267

load_dataset(...)["train"] therefore hands back train 5,182 + validation 85. A record whose metadata.eval_lock.locked is true is evaluation material wherever it is read; common/overlap.py classifies every record in this repository with 0 unknown, so the container is checkable by the reader rather than promised here.

Overlap / de-duplication (§8)

Cross-family evaluation lock — metadata.eval_lock (manifest revision fe6e286912b0, generated 2026-09-08). Every record of this repository, locked or not, carries metadata.eval_lock, computed by forge_model/common/overlap.py::Overlap.stamp_for against common/overlap_manifest.json at that revision — so within this repository the absence of the key cannot occur. Shape: {"locked": bool, "against": [{"repo": …, "split": …}, …], "own_split": …, "manifest_revision": …, "manifest_generated": …}. locked is true when the image is evaluation material anywhere in the corpus; own_split marks a record locked by its own split. The per-record field is the authority — the count here is quoted once, at this revision, and a later manifest may change it: 85 of 5,267 records (30 distinct images) are locked — by column: 0 by the cross-family manifest, 85 by their own split. In words: this repository is a MIXED container published under the HF split name train; the 85 locked records are the canon's validation records, and a training pool built from AI4Manufacturing/191-qa must exclude them — Overlap.partition / assert_train_pool_clean read the field and will. A stamp whose manifest_revision differs from the current manifest is stale, not wrong — recompute it (Overlap.stamp_is_current); a record with no stamp has not been checked against the corpus as it now is.

The 0.5 priors are BY CONSTRUCTION, not luck

form	records	majority share
`presence`	3,716	0.5
`count_class`	1,361	0.3894
`leftright`	190	0.5

presence is balanced per class: for every class, as many No questions are asked as Yes ones, so a model answering "Yes" to everything scores 0.5, not 1.0.

class	Yes eligible	No eligible	asked (Yes + No)
`crazing`	300	1,498	300 + 300
`inclusion`	366	1,416	366 + 366
`patches`	340	1,458	340 + 340
`pitted_surface`	301	1,497	301 + 301
`rolled-in_scale`	300	1,498	300 + 300
`scratches`	251	1,498	251 + 251

leftright asks BOTH orderings of every eligible pair — if "is A left of B?" is asked, so is "is B left of A?" — so the answer prior is exactly 0.5 by construction. The pair funnel: 127 pairs → 116 pass the separation rule → 95 also fully legible → 190 questions. The two conditions are counted independently on purpose: counted only in sequence they would look like a disagreement with the Stage 4 screen, which measured separation alone.

Legibility floor

The corpus floor is 16 px rendered min side, measured at the resolution the model sees: 200x200 native renders to 196x196 (scale 0.98), so the 2.36 MP cap never binds here and a 16.3 px native box is the boundary. The unit is per-INSTANCE, not per-record-largest.

Per form: a presence Yes needs the asked class's largest instance legible (65 dropped), a count_class question needs every instance of that class legible (257 dropped), and a leftright pair needs both anchors legible (22 pairs dropped).

This illegibility is intrinsic to the source: 200 px images cannot be re-rendered larger, so no resolution policy changes it. Sub-floor shares by class are on the canon's own card.

How this repository was checked

A verifier battery reads the canon and the committed query pools and knows nothing about how the builder works, so it and the builder can only agree by both agreeing with the canon. On this rung:

check	result
records	5,267
problems	0
template coverage	191/qa_count_class 37/37, 191/qa_leftright 37/37, 191/qa_presence 37/37
planted faults caught	10/10

The gold of every record was re-derived from the canon and required to equal what shipped; every query was required to be its pool's template at its recorded index; every record's eval_lock was recomputed; and no query lets a class phrase govern an inflecting verb (common/agreement_probe). Evidence: reports/191/stage5_rungs.html, reports/191/stage5_catalog.html.

Provenance

Derived from AI4Manufacturing/191 at revision eff72aff931d — the canon's own images and human-drawn boxes, unchanged. No model produced any part of this repository: every answer is a function of the canon's geometry.

what	value
canon revision	`eff72aff931d675718c02635110e8b0d8fb3f1d2`
canon shard sha256 (train)	`5db03a541430a3d4f1fd2342c8461e9a0e2f990085ac5bff31f4fcd52af347f4`
canon shard sha256 (validation)	`e8c2aed498a59e71b48457528044aa31ad8dca1a365a3c3820403b9dc154e62b`
builder	`forge_model/annotate/191/build_qa.py` (`b3b1bb8e1ff9035f`)
salt	`191-rungs-v1/qa`
cost	$0 · 0 hosted-model calls

query pool	templates	`pool_sha16`
`191/qa_presence`	37	`c7f4002a6db7315a`
`191/qa_count_class`	37	`749a054aa5da0179`
`191/qa_leftright`	37	`be42b1a3b6fb035a`

Source: NEU-DET (Song & Yan, Applied Surface Science 2013; the NEU surface defect database), packaged by the third-party GitHub project siddhartamukherjee/NEU-DET-Steel-Surface-Defect-Detection (id 361085234, license null). The source tree ships no LICENSE file and the README calls the images NEU's "open surface defect database", so license: other (research use) is the strongest claim this data supports.

Loading

from datasets import load_dataset
ds = load_dataset("AI4Manufacturing/191-qa")["train"]          # a MIXED container — see "Split, in words"
import json
md = json.loads(ds[0]["metadata"])
md["split"], md["eval_lock"]["locked"]              # the truth for THIS record

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