tags:
- smart-manufacturing
- sft
- industrial
- vision
license: other
pretty_name: '191'
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.
191
Steel-surface defect detection (6 classes; VOC bbox). 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
1,800 records (train=1770 · validation=30).
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: one class,[x, y, w, h] line per defect bounding box (COCO x/y/width/height in pixels; converted from the source Pascal-VOC corner boxes). The 6 steel-defect classes are a closed set given in the query; full boxes + image size are in metadata.objects. Detection task — no mask column — see Task & 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 & split
What this is. NEU-DET — the detection version of the NEU Surface Defect Database (Song & Yan, Northeastern University): 1,800 grayscale 200x200 steel-surface images across 6 defect classes (crazing, inclusion, patches, pitted_surface, rolled-in_scale, scratches), each annotated with Pascal-VOC bounding boxes (4,126 boxes total). Every image contains at least one defect — there are no defect-free images.
Task. Object detection: localize and classify every surface defect. query (our template) names the
closed set of 6 classes and asks for one class,[x, y, w, h] line per box (top-left x, y + width, height, in
pixels). annot is exactly that — the source VOC corner boxes [xmin,ymin,xmax,ymax] converted to COCO
[x,y,w,h]. There is no mask — localization is the bounding box. Full boxes + image size are preserved in
metadata.objects and metadata.width/height.
Split. The source ships a held-out Validation_Images folder (30 images, verified 0 overlap with the main
set) -> train (1,770) + validation (30) = 1,800.
Provenance
Underlying dataset: NEU-DET. Upstream license: other (research use; NEU Surface Defect Database) (this card is license: other; respect the upstream terms). Converted read-only from the raw source into the unified schema; conversion script: 191/convert_d91.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.