Defect Spectrum: A Granular Look of Large-Scale Defect Datasets with Rich Semantics
Paper • 2310.17316 • Published • 1
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D15
Multi-label defect detection & classification (semantic mask GT deferred). 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
2,711 records (train=2711). Pixel masks are embedded as a mask image column.
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: plain-text {label, [defect_types]} — {good, null} or {anomalous, [type1, type2, ...]}, the defect classes present (multi-label), derived from the semantic mask's class indices via the per-category legend. The class-indexed semantic mask is localization ground truth for a separate, deferred task; the legend is in metadata.defect_legend — 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, mask & split
What this is. Defect Spectrum (EnVision-Research, ECCV 2024, arXiv:2310.17316) — fine-grained, multi-class semantic re-annotation of 4 defect datasets (DS-MVTec, DS-VISION, DS-DAGM, DS-Cotton-Fabric). One image can contain several defect types; a class-indexed semantic mask marks each, and a per-category legend maps a mask index to a defect name.
Query & answer (this repo's SFT task). query is our own instruction template (the raw dataset ships no
natural-language question). It names the source/category, lists that category's possible defect classes, and asks
the model to decide good vs defective and, if defective, list all defect types present — answering in the
form {label, [defect_types]} (multi-label), exactly what annot holds ({good, null} /
{anomalous, [type1, type2, ...]}). The defect types are derived from the semantic mask + the legend, so
DS-VISION / DS-DAGM (which have no per-defect folders) get real types too. The query does not ask for a mask.
Mask & legend (deferred localization ground truth). The mask column is the class-indexed semantic
segmentation mask (pixel value = defect-class index; 0 = background); normal images have mask=null. The
per-category legend (index -> defect name) is in metadata.defect_legend, and the derived classes in
metadata.defect_types. A text-output model cannot emit a pixel mask, so localization is deferred (mask kept as GT).
Defect classes. The legend (defects_dict from each source's DS-*.md) defines 98 per-category defect
classes (58 distinct names): DS-MVTec 15 categories / 71, DS-VISION 6 / 20, DS-DAGM 1 / 5, DS-Cotton-Fabric 1 / 2.
Split & notes. No upstream train/val split -> single train. Re-annotates MVTec/VISION/DAGM/Cotton (keep on
one side of any split vs the raw sets). synthetic_* (generated augmentation) is excluded, and the paper's
per-sample captions are not included here. A few images are labeled anomalous but have an all-zero mask (source
contradiction) -> {anomalous, []}.
Provenance
Underlying dataset: DefectSpectrum. Upstream license: MIT (upstream MVTec/VISION/DAGM/Cotton) (this card is license: other; respect the upstream terms). Converted read-only from the raw source into the unified schema; conversion script: D15/convert_d15.py, published with publish/push_to_hf.py, both in AI4Manufacturing/forge_model.
Overlap / de-duplication (§8)
Re-annotates MVTec/VISION/DAGM/Cotton; keep on one side of a split vs the raw sets. 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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