tags:
- smart-manufacturing
- sft
- industrial
- vision
license: other
pretty_name: '184'
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.
184
Electrical-commutator surface-defect binary 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
399 records (train=399). 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; no defect types — 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 — 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. KolektorSDD (Tabernik et al., "Segmentation-Based Deep-Learning Approach for Surface-Defect Detection", J. Intelligent Manufacturing 2020) — 399 grayscale images of electrical-commutator surfaces from 50 items (8 sections each); 52 defective, 347 defect-free. Each image ships a pixel-level binary defect mask.
Task & label. Surface-defect detection: image-level binary (defect vs OK) + pixel-level segmentation. The
source has no good/defect folders — the image-level label is derived from the mask (any nonzero pixel ->
anomalous). query (our template) asks only whether the surface is good or anomalous; annot is the
plain-text answer good or anomalous. The query does not ask for a mask.
Segmentation (deferred GT). The binary segmentation mask is kept in the mask column as localization ground
truth (anomalous images only; good images have mask=null). Per-image seg info is in metadata: mask_path and
defect_area_fraction. A text-output model cannot emit a pixel mask, so segmentation is deferred.
Split. No train/test split in the source (the paper uses 3-fold cross-validation) -> single train (399
images: 52 anomalous + 347 good).
Provenance
Underlying dataset: KolektorSDD. Upstream license: CC BY-NC-SA 4.0 (this card is license: other; respect the upstream terms). Converted read-only from the raw source into the unified schema; conversion script: 184/convert_d84.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.