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D15
Fine-grained defect classification & typed semantic segmentation. 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 (JSON) | the answer: {label, defect_type} (T-B1), {objects:[bbox/polygon]} (T-B2/B5b), or {answer, answer_text, question_type} (T-B3) |
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 |
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 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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