README.md

metadata

tags:
  - smart-manufacturing
  - sft
  - industrial
  - vision
license: other
pretty_name: D15
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.

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.