| --- |
| 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 |
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|
| **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`](https://github.com/AI4Manufacturing/forge_model). |
|
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| ## Overlap / de-duplication (§8) |
|
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| 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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|
