Datasets:
tags:
- smart-manufacturing
- sft
- industrial
- vision
license: other
pretty_name: '181'
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.
181
Textured-surface weakly-supervised defect detection (10 classes; weak masks). 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
16,100 records (test=8050 · train=8050). 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 per texture class; the defect is unnamed). The WEAK elliptical segmentation mask is deferred localization GT, with seg info (mask_path, defect_area_fraction) in metadata; metadata.category is the texture class (Class1-10) — 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. DAGM 2007 (Wieler & Hahn, "Weakly Supervised Learning for Industrial Optical Inspection", DAGM 2007 competition / Bosch) — 16,100 synthetic grayscale images across 10 statistically-textured surface classes (Class1-10). Each class carries a single (unnamed) defect type; most images are defect-free.
Task & label. Originally weakly-supervised image-level defect classification (defective vs defect-free), now
widely used for anomaly detection + weak localization. Per texture class the task is binary. An image is
labelled anomalous iff the source ships a Label/<id>_label.PNG mask for it (defect-free images have no
mask). query (our template) names the texture class and asks only whether it is good or anomalous;
annot is the plain-text answer good or anomalous. The query does not ask for a mask. metadata.category
records the texture class (Class1-10).
Mask (WEAK label — deferred GT). DAGM's masks are weak labels: a rough elliptical region around the defect
(0/255), not pixel-precise. Kept in the mask column as deferred localization GT (anomalous images only; good
= null); seg info (mask_path, defect_area_fraction) in metadata. Segmentation is deferred (a text model
can't emit a pixel mask).
Split. Official per-class Train/Test -> train (8,050) + test (8,050) = 16,100. Class1-6: 575 images
per split; Class7-10: 1,150 per split.
Provenance
Underlying dataset: DAGM2007. Upstream license: CC BY 4.0 (this card is license: other; respect the upstream terms). Converted read-only from the raw source into the unified schema; conversion script: 181/convert_d81.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.