VISION Datasets: A Benchmark for Vision-based InduStrial InspectiON
Paper • 2306.07890 • Published
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D23-grounding
Single-class detection-format defect localization on VISION industrial imagery —
283 items (263 positive (record, class) pairs + 20 Cable
negatives), derived deterministically (no LLM/teacher) from the human gold
boxes of AI4Manufacturing/D23-annotated. The model outputs
boxes as text (absolute-coordinate JSON, D15-grounding format parity).
The repository name is an internal task code. See Provenance below.
Smallness is deliberate. 283 items is what survives the anti-duplication and floor rules below. The rung ships as its own repo because the corpus PoC leave-one-out found grounding rungs load-bearing (removing them: grounding F1 -8.2, in-domain exact -2.8), and per-shape format parity with the D15/181 grounding rungs is what makes those findings transfer.
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
"Locate every {class}." Each query names ONE class; annot is a JSON list of
{"type": ..., "bbox_xywh": [x, y, w, h]} in native pixel coordinates (origin
top-left, sorted by x then y), one entry per gold instance of that class — or []
when the class is absent (Cable negatives). No overlay — the image is the raw
full frame.
- Anti-duplication rule: a positive (record, class) pair is eligible only if the record contains ≥2 distinct classes — on single-class records the answer would equal the parent detection task's full answer verbatim. Consequence: the queried class is always present except on the 20 Cable negatives — a disclosed limitation: within Hemisphere/Lens this rung teaches per-class localization, not per-class absence.
- Pair-level floor: every instance of the queried class must pass the legibility floor (an answer may not silently omit an illegible box).
- Cable negatives (20:
break11 /thunderbolt9; 1:1 with Cable positives per split, salted-hash selected): queries about the class absent from a single-class Cable record, gold[]. - Cable absence golds rest on the exhaustiveness audit, not on assertion: the stage-3 defensibility review (parent audit artifact
reports/D23/03_stage3_defensibility.html, §4 exhaustiveness spot-check) found Cable's gold sets complete-looking with no unboxed defects, and the parent card's exhaustiveness-tier note places Cable in the completeness-claiming tier. - Negatives disclosure: answering the Cable negatives requires the break/thunderbolt distinction whose padded box geometry killed Cable's region items; here it is perception-mediated (no overlay) — a split-respecting geometry decoder reaches 62.7% on the padded boxes, under the 75.4% kill bar.
Records
283 items (train=121 · validation=162), split preserved verbatim from the parent. Global empty-answer share 7.07%.
| subset | items | queried classes (each ≥1×) | max query-class share | empty-answer share |
|---|---|---|---|---|
| Cable | 40 | break, thunderbolt |
52.5% | 50.0% |
| Hemisphere | 194 | Defect-B, Defect-C, Defect-D |
51.5% | 0.0% |
| Lens | 49 | Fiber, Flash Particle, Hole, Surface Damage, Tear |
38.8% | 0.0% |
Exits: Casting (4 multi-class pairs) and Cylinder
(2) fall below de-minimis once single-class records are
excluded; PCB_1 has 0 multi-class pairs; Electronics is
single-class (anti-duplication); PCB_2 is geometry-killed (see the parent-family
kill table on the region card); Hemisphere Defect-A is excluded as a gold
(discriminability audit).
| field | type | meaning |
|---|---|---|
query |
str | names the product and ONE class; JSON output spec + empty-list rule held verbatim |
image |
Image | raw full-frame photo (no overlays, never cropped) |
annot |
str | JSON box list (see above), [] on negatives |
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, query_class, n_instances, negative, gold_boxes_xywh (native px), 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 |
|---|---|---|---|---|---|
| Cable | 31.2% | 7.3% | 4.0% | 1.6% | 1.2% |
| Hemisphere | 88.1% | 71.9% | 54.2% | 22.4% | 17.4% |
| Lens | 93.4% | 87.8% | 83.5% | 68.1% | 68.8% |
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 (empty answers here assert only that a named class is absent).
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
- Boxes are canonical native-px COCO xywh. Convert to your model's grounding
convention at train time (normalized, corner pairs, special tokens, …) —
regenerate, don't regex; see
common/box_convert.pyin forge_model. - Gradable by class-aware box matching (e.g. greedy IoU≥0.5) → RLVR reward or eval
metric;
[]records score rejection of the named class. - Companions:
AI4Manufacturing/D23-annotated(CoT channels incl. the coordinate-citingreasoning_grounded),AI4Manufacturing/D23-region,AI4Manufacturing/D23-mcq.
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