This dataset is released for research use. Access is reviewed and granted manually by the maintainers. Please state your name, affiliation, and intended use.

Roles

Roles: canon repo — annot is the source label, kept machine-parseable as the gold for verification and reward parsing; there is no filled reasoning column and this repo is not itself a training view. Derived repos each state their own regime on their own card.

218

Image-level aesthetic-defect classification of laser weld seams on EV battery cells (61,670 narrow strips; binary). Category B, task T-B1, in the unified Smart-Manufacturing SFT schema.

The repository name is an internal task code. See Provenance below for the underlying dataset.

Records

61,670 records (test=9251 · train=43169 · val=9250).

Unified SFT schema

field	type	meaning
`query`	str	the question / instruction (model input)
`image`	Image	the input image (bytes embedded); for multi-image rows, a preview of the first view
`images`	list[Image]	(multi-image rows) all input views / modalities for the row, bytes embedded
`annot`	str	the answer — for this dataset: one word, `ok` or `defective`. The deposit's own folder token (`OK` / `KO`) is preserved in `metadata.source_label`; `KO` is not self-explanatory so it is not the answer string. ⚠ Report balanced accuracy or defect recall, never plain accuracy — a single threshold on the image's WIDTH reaches 81.3% balanced accuracy with no pixels read, and its plain accuracy (75.4%) sits below the majority floor, which hides it. See the card
`reasoning`	null	no native CoT in these datasets
`cate`	"B"	SFT category
`task`	"T-xx"	unified task id
`metadata`	str (JSON)	split, provenance, `image_path`, `image_sha256` (dedup key)
`mask`	Image \| null	(T-B1/T-B2 only) the pixel ground-truth mask, bytes embedded
`masks`	list[Image]	(multi-image T-B1 / D21) per-view masks aligned with `images` (None where a view has no defect), or multi-region masks

Licence

CC BY 4.0. Read from the Zenodo record's own metadata.license field at fetch time, not inferred from a paper. Redistribution is permitted with attribution and no permission request was needed.

Cite the deposit: "2D image dataset for the presence or absence of aesthetical defects on EV battery welds", OPENZDM project, Zenodo, DOI 10.5281/zenodo.14025630. The archive (Weld Aesthetical Defects.zip, 1,153,183,844 B) was verified against Zenodo's published md5, recomputed locally, before anything was built from it.

⚠⚠ Read this before reporting any number: the image's WIDTH carries most of the label

KO strips are almost all ~400 px wide (p10 388); ok strips span 48–651 px (p10 92). So a single threshold on the image's width — reading no pixels at all — recovers most of the defect class:

pixel-blind predictor	accuracy	defect recall	specificity	balanced accuracy
always the majority class (`ok`)	80.9%	0.0%	100.0%	50.0%
`defective` iff width ≥ 350 — one threshold	75.4%	90.8%	71.8%	81.3%
size-bin lookup (width/10, height)	83.0%	36.1%	94.0%	65.1%
intensity-bin lookup (mean, std)	81.1%	3.6%	99.4%	51.5%
the image's width and height alone — the standing corpus floor (class-balanced linear probe, no pixels read; `common/lazy_floors.py`)	75.4%	90.8%	71.8%	81.3%

The last row is the corpus's standing frame-size floor, computed on every image dataset from the build: fitted on train, scored on test, 3,355 distinct frame sizes. It rediscovers the one-threshold rule exactly (its weight sits almost entirely on the width), which is the check that the two instruments agree.

Report results as balanced accuracy or defect recall. Plain accuracy hides this shortcut completely. The width rule's plain accuracy is 75.4%, which is below the 80.9% majority floor — read that column alone and you conclude the shortcut is worthless. It is not: on balanced accuracy it is 81.3% against a 50% chance line and it recovers 90.8% of every defective strip.

This is not a hypothetical caution. The first pass of this dataset's own audit scored the probes on plain accuracy, saw "+2.1 pp over the floor", and concluded the shortcut was minor. That conclusion was wrong, and it was wrong because of the metric, not the measurement.

The intensity probe reaches only 51.5% balanced, so the artefact is geometric, not photometric — not exposure or brightness. Defect rate by width:

width	images	defective
50–99	9,603	0.2%
100–149	1,307	0.8%
300–349	25,660	4.2%
350–399	4,011	36.8%
400–449	20,648	44.4%
width < 350	36,997 (60.0%)	3.1%
width ≥ 350	24,673 (40.0%)	43.2%

A 14× shift in the class prior from one number that needs no pixels. The deposit records nothing that would explain it; the likeliest cause is that short crops come from a different product, station or seam length whose failure rate is simply lower.

Nothing is filtered or re-cropped to hide this. Canon measures; the mixture decides the dose. Every record carries metadata.image_wh, so the artefact is auditable per record rather than only in aggregate.

Adapt-layer instruction

Two consequences, stated so the choice is made deliberately rather than by a default:

Any resize to a fixed size — padding included — changes or destroys the width shortcut. That may be what you want, but it is a change to what the task measures, not a formatting detail.
Any view that preserves native width hands the shortcut to the model.

Strip geometry: width median 323 (p10 94, p90 408), height median 37 (min 22, max 70), median aspect ratio 9.4:1. A square-ish resize either pads the strip — spending most of the token budget on blank space — or squashes it, destroying the along-seam texture the classes differ in. A view should render at ≥3× height (37 → 111 px) and preserve the aspect ratio. The choice belongs to layer 3; this card states the consequences only.

Records and split

split	`ok`	`defective`	total	defective share
train	34,916	8,253	43,169	19.1%
val	7,482	1,768	9,250	19.1%
test	7,482	1,769	9,251	19.1%
total	49,880	11,790	61,670	19.1%

The authors' own 3-way split is adopted unchanged and recorded in metadata.official_split. It is stratified to a 19.1% defect share in all three parts, to one decimal.

⚠ The middle split is named val, not validation — that is the deposit's own folder name and it is kept verbatim, so load_dataset(...)["val"] is the key that works.

Leakage: no evidence, which is not the same as proof

The deposit has no capture id and filenames restart at image_1000.png inside every split/class, so nothing states which strips came from the same weld. Image similarity is the only available evidence, and it was measured against a control:

comparison	exact match	within Hamming 4
test → train (the question)	0.00%	0.18%
trainA → trainB (an arbitrary boundary inside train, control)	0.00%	0.02%
		excess +0.17 pp

The control is a boundary that by construction does not separate welds, so whatever rate it produces is the hash's background collision rate on this data. An excess of a sixth of a percentage point is nothing.

⚠ A first measurement, using a standard square 8×8 difference hash, reported 9.0% and looked like serious leakage. That was the instrument: squashing a 400×35 strip into a 9×8 thumbnail throws away the along-seam detail that distinguishes one weld from another, so unrelated strips collided. A strip-shaped 16×4 hash gives the 0.18% above.

⚠ Stated precisely: this is no evidence of leakage, not proof of a clean split. A weld imaged twice from different angles would not look similar and would not be caught by any image-similarity test. With no capture id in the deposit, that possibility cannot be excluded by anything.

Duplicates: clean

61,670 distinct sha256 over 61,670 images — zero duplicate copies, zero groups spanning more than one split, zero label conflicts. Worth stating because it is not the norm: 207 shipped 120 duplicate hashes with 79 label conflicts, and 096 was unusable partly for that reason.

What this dataset does not contain

⚠ No boxes and no masks. A defect-size distribution cannot be computed for this dataset, and anything quoting one is inventing it. The label is image-level only, so the corpus's 16 px legibility floor — which gates localization — does not bind here: nothing has to be resolved to a box.

There is no detection rung to build from this source.

Version history

v1 — published 2026-09-09. 61,670 records (43,169 train / 9,250 val / 9,251 test), one query template.

v2 — this revision. Query text only. Every record's query is drawn from a pool of 37 gate-verified paraphrases of the v1 wording, assigned by a stable hash of the image path and recorded as metadata.query_template; index 0 is v1's wording, byte for byte (1,677 records keep it). Answers, images, split and every other field are unchanged. The pool passed the deterministic gates (names the target, does not misdescribe the strip, keeps the two-word answer form, introduces no class name the source does not state) and a template ↔ gold independence test on this build (37 templates, 0 flagged). Two more things land with it: the build-time pixel-identity check (§8 now carries measured numbers: 61,670 images, 61,670 distinct, 0 on both sides of any split — the same result the sha256 count above reached, now on decoded pixels) and the frame-size floor row in the pixel-blind table, which reproduces the width rule.

Provenance

Underlying dataset: OPENZDM EV battery welds. Upstream license: CC BY 4.0 (Zenodo record metadata.license; OPENZDM project) — redistribution permitted with attribution, no permission request needed (this card is license: other; respect the upstream terms). Converted read-only from the raw source into the unified schema; conversion code under 218/ (with publish/push_to_hf.py) in AI4Manufacturing/forge_model.

Converter: forge_model@cae2e84 (PR #98). That is the last commit to touch this dataset's converter, which is what produced the data; this card's own text lives in publish/push_to_hf.py and moves independently.

Overlap / de-duplication (§8)

No overlap with any other dataset in this corpus — laser welding of battery interconnects, a different process from 216 (MAG bead) and 102/214 (radiography), and 61,670 distinct sha256 with zero duplicates. ⚠ The deposit carries no capture id, so nothing states which strips share a weld; the official 3-way split is adopted and the leakage evidence is on the card.

Two identities, and they answer different questions. metadata.image_sha256 hashes the file bytes: it finds byte-identical copies and is blind to a re-encode. metadata.pixel_sha256 hashes the decoded image (mode | size | pixels): it finds the same photograph saved twice. Only the second one settles whether an image is duplicated.

Measured at build time, not asserted afterwards — a violation aborts the build and names the offending records:


images checked	61,670
distinct by decoded pixels	61,670
images carrying more than one record	0
images on both sides of the split	0

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