VISION Datasets: A Benchmark for Vision-based InduStrial InspectiON
Paper • 2306.07890 • Published
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D23-region
Region-conditioned defect typing on VISION industrial imagery — 1,048 items
(993 gold instances + 55 clean decoys), derived deterministically
(no LLM/teacher) from the human gold boxes of
AI4Manufacturing/D23-annotated.
Exact-match gradable (closed per-subset menus) → SFT and RLVR-ready.
The repository name is an internal task code. See Provenance below.
Query diversity.
queryis drawn from a pool of 32 surface variants (paraphrases preserving task and answer format; the options clause and answer-format directive are held verbatim), selected by an independent salted per-item hash — the corpus query-template-diversity standard.
Task
"An operator points at a region — what defect, if any, is there?" One item per
eligible gold instance: the instance is marked on the full-frame image by a
red rectangular outline (D15-region ring convention: thickness scales with image
size), and the query asks which menu option the outlined region contains. Gold =
the class token verbatim (annot exact match).
Padded outlines (disclosed deviation from D15-region). Every outline is padded
per side by an independent seeded margin in [10%, 40%]
of the box dimension (clamped to the frame) — the outline never coincides with the
gold box, so box-tightness cannot carry class information; the geometry-decoder
battery below is computed on exactly these padded outlines. D15-region instead pads
to a fixed minimum size. There is no bbox_text mode here (overlay only).
Clean decoys (PCB_1 only). 55 regions (28
train / 27 val; seeded fixed ratio
1:3 decoy:gold, realized 0.3313) with size AND
position sampled from PCB_1's own per-instance positive population (D15-region v5
sampler semantics), placed so the padded outline + ring reach has zero overlap with
every gold box (IoU 0, no containment). Their gold is no defect.
PCB_1 clean-region decoys rest on the same audit: stage-3 defensibility (parent audit artifact reports/D23/03_stage3_defensibility.html, §4) found PCB_1's golds complete — its defects are deliberately synthesized onto the boards — which is the trust basis for sampling defect-free regions. (The parent card still hardens PCB_1 prose against completeness claims; decoys assert absence only inside the sampled region, which additionally avoids every gold box.)
Menus. Menus are per-subset and every option is gold at least once
(never-correct options are excised corpus-wide): Hemisphere's menu is ternary
(Defect-A fails the discriminability bar below and is excised as gold AND option);
no defect appears only in the PCB_1 menu — the only place it can be correct —
and only in this rung (in MCQ it would be never-correct).
Records
1,048 items (train=432 · validation=616), split preserved verbatim from the parent.
| subset | gold items (train/val) | menu (every option is gold ≥1×) | majority answer (post-decoy) |
|---|---|---|---|
| Hemisphere | 625 (230/395) | Defect-B, Defect-C, Defect-D |
Defect-D 53.0% |
| Lens | 202 (90/112) | Fiber, Flash Particle, Hole, Surface Damage, Tear |
Fiber 59.9% |
| PCB_1 | 166 (84/82) + 55 decoys | missing_hole, mouse_bite, open_circuit, short, spur, spurious_copper, no defect |
missing_hole 43.0% |
- Hemisphere carries 62.9% of the gold records (post-kill concentration — see the kills below); its ternary menu prior is the majority share above.
- Lens is Fiber-trimmed (seeded) to hold Fiber at 59.9%.
- Within PCB_1,
no defectis 24.9% of items; global majority (Defect-D) is 31.6% — all answer priors are asserted ≤60% per category and ≤75% globally after decoy injection, so blind-guess ceilings equal the shares above. - Template collisions (measured on the shipped artifact): 61 item pairs share their source image AND byte-identical query text (38 same-gold / 23 different-gold, over 49 records) — benign by construction: paired items always differ in the drawn outline, so the image disambiguates; expected under independent per-item template draws on multi-instance images.
| field | type | meaning |
|---|---|---|
query |
str | names the product, mentions the red outline, lists the closed menu; "answer with the option exactly as written" |
image |
Image | full-frame photo (never cropped) with the padded red outline burned in |
annot |
str | gold class token, or no defect (exact match) |
reasoning |
null | none — deterministic derivation |
cate / task |
str | B / T-B2 (unified schema; rungs keep the parent token) |
metadata |
str (JSON) | subset, split, image_sha256, source_record_id, instance_index (deduplicated objects list; −1 = decoy), gold + raw bbox_xywh (native px), padded_box_xywh, drawn_box_xywh, pad_margins, menu, gates provenance |
Eligibility & kills (the decision trail)
Pools were frozen by a pre-build gate battery under an adversarial convergence review; the numbers below are the battery's own measurements.
Legibility floor. An instance is eligible only if min(w,h) ≥ 16px at the 2.36MP-equivalent training resolution. Unlearnable fraction of each shipped subset's parent instances by reference input (long side / area cap):
| subset | 448 | 768 | 1024 | 1568 | 2.36MP |
|---|---|---|---|---|---|
| Hemisphere | 88.1% | 71.9% | 54.2% | 22.4% | 17.4% |
| Lens | 93.4% | 87.8% | 83.5% | 68.1% | 68.8% |
| PCB_1 | 100.0% | 97.5% | 85.6% | 69.7% | 58.1% |
Geometry-decoder kills (rule: hard decoder iff probe >= majority + 25pts AND >= 75% absolute, computed on padded outline geometry — no retained subset's outline geometry decodes its answer):
- PCB_2 killed — full-geometry GBM 98.1% vs 30.5% majority (repeated-panel layout).
- Cable killed (region/MCQ) — size-only GBM 82.4% vs 50.4% majority: box size alone decodes the class.
- Casting killed — full-geometry GBM 83.8% vs 56.6% majority; the kill is position-driven (position channel adds +10.0pts over size-only 73.8%).
- Cylinder killed (round-3) — cross-split geometry twins: 21.7% of val instances have a train instance within 10px (raw geometry) and 51.3% within 30px; train→val padded 1-NN 80.3% vs 33.6% val-majority — repeated rig positions leak labels across the split.
Retained-subset certifications (same probes, below the bar):
- Hemisphere (BCD) — cross-split padded 1-NN 71.9% (+16.7pts over val-majority) with twin fractions 0.0%/8.1% — the signal is class-geometry, not rig repetition; in-pool padded LOO 1-NN on the shipped pool = 0.773 vs kill bar 0.780 (passes by 0.7pts; both digits disclosed — this is the highest retained value in the corpus).
- Lens (post-Fiber-trim) — cross-split padded 1-NN 50.0% (-8.0pts), twins 0.9%/3.6%.
- PCB_1 — cross-split padded 1-NN 37.8% (-26.8pts), twins 3.7%/7.3%.
Opaque-code discriminability (size-normalized audit). Hemisphere's anonymized
Defect-A..D codes were additionally required to show appearance-borne signal on a
fixed 224px canvas (removes
absolute size by construction; judge-side instrument, upscaling allowed): Defect-B
3.2× chance, Defect-C 2.0×,
Defect-D 1.73× pass the ≥1.5× bar; Defect-A
0.8× fails and is excluded as a gold everywhere (menu
excised too — never-correct options are removed corpus-wide).
The verdicts are judge-robust (judge-sensitivity rerun, three control-passing
judges: claude-sonnet-4-5, claude-sonnet-4-6, gpt-5.6): Defect-A stays under the ≥1.5× bar with every judge
(accuracy 0.20/0.37/0.30 vs the 0.375 bar), while Defect-B/C/D and every PCB_2
class pass under all three.
Instrument-ceiling caveat: the positive control (Cylinder, semantically named
classes) passes the control gate under every judge (macro 0.65/0.74/0.75), but its
Chip class scores 0.15 under the original judge and rises to
≈0.45 under the stronger judges — per-class collapses are partly
judge-limited, not a hard pixel ceiling; treat per-class passes as a lower bound
on discriminability. Residual cue disclosed by the protocol: aspect ratio
survives the canvas normalization.
No D23-counting rung exists: counting was dropped at plan review (degenerate ~all-one count priors on this parent + the counting shape lacks PoC validation; the shape is kept corpus-wide via other artifacts).
Roles
Roles: this is an answer-only tier — there is no reasoning content (reasoning is null on every record); 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
Derived read-only from AI4Manufacturing/D23-annotated (revision pinned: fd728cc34406cd87512bd22fd8c31df78dace149), itself derived from raw VISION (Bai et al., arXiv:2306.07890; upstream CC BY-NC 4.0 — respect upstream terms; this card is license: other). Answers are pure functions of the human gold annotations — no LLM/teacher anywhere in this rung. Generator: annotate/D23/rungs/build_rungs.py in AI4Manufacturing/forge_model (builder sha256 8652395b…), built against the pre-build gate battery (gates v5 report, script sha256 0b9d0e43…, build source of truth = its section 16). Every independent choice is salted-hash seeded; a rerun reproduces the artifact exactly (tuple-hash verified).
Parent golds were deduplicated first: 5 records carried 8 exact-duplicate (class,bbox) phantom instances, collapsed before any pool math. metadata.instance_index refers to this deduplicated objects list, not the raw parent array. The parent's official train/validation split is preserved verbatim on every item (uniform-split policy; carve train/eval downstream).
Training-mixture notes
- One-lineage rule: the parent excludes the 641 VISION images byte-shared with the DefectSpectrum/D15 family (materially different label policies); the 8 subsets here have zero image overlap with the D15 family. Details + machine-readable keys: base
AI4Manufacturing/D23card §8. - Same-evidence rungs: 137 of 140 D23-mcq marked instances also ship as D23-region records with identical padded outlines — region and mcq are format variants over the same evidence. Treat them jointly in any mixture/eval carve; never split the two rungs across train/eval.
- Image-wise carving: one photo can appear in the parent and in up to three rungs; carve on
metadata.image_sha256across the whole D23 family simultaneously. - All-defective world prior: every parent record is defective; rung items never assert a defect-free image (clean regions exist only as PCB_1 decoys here).
Overlap / de-duplication (§8)
Base photos are the SAME images as AI4Manufacturing/D23-annotated and base AI4Manufacturing/D23 — this rung inherits their overlap situation: the shipped 8-subset lineage shares no imagery with the D15 family (sha-verified sidecar on the base card), and D23-validation imagery appears in the other D23 rungs and the parent. Do not evaluate on any D23-family repo's validation split if you train on this set, and reconstruct exact overlaps via metadata.image_sha256.
Training notes
- Loss on completions; answers are short by instruction. Exact-match reward for RLVR.
- 137/140 of
AI4Manufacturing/D23-mcq's marked instances are also region records here with identical outlines — same-evidence format variants (see Training-mixture notes). - Companions:
AI4Manufacturing/D23-annotated(CoT channels),AI4Manufacturing/D23-mcq,AI4Manufacturing/D23-grounding.
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