Datasets:

AI4Manufacturing
/

D23-grounding

tags:
  - smart-manufacturing
  - sft
  - industrial
  - vision
license: other
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.
pretty_name: D23-grounding
dataset_info:
  features:
    - name: query
      dtype: string
    - name: image
      dtype: image
    - name: annot
      dtype: string
    - name: reasoning
      dtype: 'null'
    - name: cate
      dtype: string
    - name: task
      dtype: string
    - name: metadata
      dtype: string
  splits:
    - name: train
      num_examples: 121
    - name: validation
      num_examples: 162

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. query is 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: break 11 / thunderbolt 9; 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/D23 card §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_sha256 across 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.py in 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-citing reasoning_grounded), AI4Manufacturing/D23-region, AI4Manufacturing/D23-mcq.