| --- |
| tags: |
| - smart-manufacturing |
| - sft |
| - industrial |
| - vision |
| license: other |
| pretty_name: "190" |
| 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. |
| --- |
| |
| # 190 |
|
|
| SEM nanofibrous-material unsupervised anomaly detection (segmentation GT). Category **B**, task **T-B1**, in the unified Smart-Manufacturing SFT schema. |
|
|
| > The repository name is an internal task code. See **Provenance** below for the underlying dataset. |
|
|
| ## Records |
|
|
| **45** records (test=40 · train=5). 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: the plain-text image-level label `good` or `anomalous` (binary; the label is derived from the pixel mask). The binary segmentation `mask` is deferred localization GT, with seg info (`mask_path`, `defect_area_fraction`) in `metadata`; the grayscale `.tif` sources are re-encoded to `.png` for the image column — 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.** NanoTWICE (Carrera et al., IEEE TII 2017) — SEM (scanning-electron-microscope) images of |
| **nanofibrous filter material** for unsupervised surface **anomaly detection & localization**. A handful of |
| defect-free images are used for training; defective images carry pixel-level ground-truth masks. |
|
|
| **Query & answer (this repo's SFT task).** `query` is our own instruction template (the dataset ships no |
| question). It asks only whether the material surface is **good** or **anomalous**; `annot` is the plain-text |
| label `good` or `anomalous`, **derived from the mask** (any defect pixel → anomalous). The query does not ask |
| for a pixel mask. |
|
|
| **Mask (deferred localization GT).** Each anomalous image ships a binary ground-truth mask (`mask` column; |
| `1` = defect, `0` = background), with `mask_path` + `defect_area_fraction` in `metadata`; normal images have |
| `mask`=null. Localization is deferred (a text model cannot emit a mask). The source images are 8-bit grayscale |
| `.tif`; they are re-encoded to `.png` for the image column so the dataset viewer renders them. |
|
|
| **Split.** `train` = 5 defect-free images; `test` = 40 anomalous images (the standard NanoTWICE unsupervised |
| protocol — all normal images are used for training). |
|
|
| ## Provenance |
|
|
| Underlying dataset: **NanoTWICE**. Upstream license: **other (research use; Carrera et al., IEEE TII 2017)** (this card is `license: other`; respect the upstream terms). Converted read-only from the raw source into the unified schema; conversion script: `190/convert_d90.py`, published with `publish/push_to_hf.py`, both in [`AI4Manufacturing/forge_model`](https://github.com/AI4Manufacturing/forge_model). |
|
|
| ## Overlap / de-duplication (§8) |
|
|
| None notable. Each record carries `metadata.image_sha256` so overlapping images can be kept entirely on one side of a train/eval split. |
|
|
