VISION Datasets: A Benchmark for Vision-based InduStrial InspectiON
Paper • 2306.07890 • Published
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D23
Defect detection & classification (bbox; polygon GT kept in metadata). Category B, task T-B2, in the unified Smart-Manufacturing SFT schema.
The repository name is an internal task code. See Provenance below for the underlying dataset.
Records
1,894 records (train=880 · validation=1014).
Unified SFT schema
| field | type | meaning |
|---|---|---|
query |
str | the question / instruction (model input) |
image |
Image | the input image (bytes embedded) |
annot |
str | the answer — for this dataset: one line per defect, class,[x, y, width, height] (plain text). The COCO polygon segmentation is preserved as ground truth in metadata.objects (not asked in the query) — see Task, mask & split below |
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] | (D21 only) multi-region masks |
Task, segmentation & split
What this is. VISION (Bai et al., arXiv:2306.07890, 2023) — a vision-based industrial inspection benchmark: 14 subsets, 44 defect types, with COCO instance-segmentation annotations (bounding boxes + polygons). Every image is defective; the underlying goal is to find, classify, and outline each defect instance.
Query & answer (this repo's SFT task). query is our own instruction template (the raw dataset ships no
natural-language question — only COCO json). It names the subset, lists that subset's defect classes, and asks the
model to detect each defect and give its class and bounding box, answering one line per defect as
class,[x, y, width, height] — exactly what annot holds.
Why bbox, not the polygon. VISION's segmentation is a real COCO polygon (text coordinates), but the polygons are
often very detailed (median ~40 vertices, up to ~2680), which a text-output model cannot realistically reproduce. So
the SFT task here is detection + classification (class + bbox). The full COCO instances — including the polygon
segmentation — are preserved as ground truth in metadata.objects (each with category, bbox, segmentation,
area, iscrowd) for pixel-precise / segmentation-model evaluation; the polygon is simply not asked of the model.
Class names. 10 of the 14 subsets have meaningful defect names (e.g. break, Scratch, Porosity, mouse_bite,
open_circuit); 4 subsets ship generic placeholder names (Capacitor = 0, Hemisphere = Defect-A..D,
PCB_2 = defect1..7, Screw = defect) — kept as-is (faithful to the source).
Split. train + validation (per-subset COCO annotations). The inference split ships images without ground
truth (official eval only) and is not included. See Records for counts.
Provenance
Underlying dataset: VISION. Upstream license: CC BY-NC 4.0 (this card is license: other; respect the upstream terms). Converted read-only from the raw source into the unified schema; conversion script: D23/convert_d23.py, published with publish/push_to_hf.py, both in AI4Manufacturing/forge_model.
Overlap / de-duplication (§8)
D15 (DefectSpectrum) shares 641 of this repo's 1,894 images (33.8%), byte-identical (sha-verified 2026-07-14): DefectSpectrum re-annotated 6 of VISION's 14 subsets by copying the image files verbatim.
| subset | shared / D23 records | subset | shared / D23 records | |
|---|---|---|---|---|
| Capacitor | 77 / 77 | Groove | 98 / 99 | |
| Screw | 120 / 120 | Console | 180 / 192 | |
| Wood | 102 / 102 | Ring | 64 / 87 |
The other 8 subsets (Cable, Casting, Cylinder, Electronics, Hemisphere, Lens, PCB_1, PCB_2) share nothing. All 641 sit in D15's train split, while here 315 are train and 326 are validation — so D23-validation is contaminated as an eval set for any model trained on the D15 family. (D15 also holds 76 VISION images from the unlabeled inference split, which are not in this repo at all. MMAD/D05 shares no VISION imagery — the previous vague note here was wrong.)
The two repos' labels materially disagree on shared images — different upstream annotation policies, not noise: VISION deliberately preserves the original sparse crowd-sourced defect decisions, while DefectSpectrum re-annotated exhaustively with fine masks and its own class names. Measured on all 641: DS masks confirm 88.5% of VISION boxes (718/811); a median 68% of DS defect pixels fall inside the VISION-box union, and on 259/641 images it is under half (worst: Groove — e.g. the same photo carries 1 gold defect here and 8 in D15-grounding).
Rule: build mixtures with each shared image from exactly one repo — prefer the D15 family for detection-shaped supervision (finer, exhaustive lineage); this repo's copies are the VISION-policy alternative, not additional supervision. The machine-readable key list is overlap_with_D15.json (641 sha256 keys + stats; generated by annotate/D23/overlap_sidecar.py in forge_model), joinable to every record's metadata.image_sha256:
overlap = set(json.load(open("overlap_with_D15.json"))["sha256"])
keep = [r for r in d23 if json.loads(r["metadata"])["image_sha256"] not in overlap]
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