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)
Partial overlap with MMAD / DefectSpectrum; dedup by image hash. Each record carries metadata.image_sha256 so overlapping images can be kept entirely on one side of a train/eval split.
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