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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.
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.
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