tags:
- smart-manufacturing
- sft
- industrial
- vision
license: other
pretty_name: '179'
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.
179
Aero-engine blade anomaly detection under domain shift (4 defects; 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
2,160 records (test=1639 · train=521). 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: plain-text {label, defect_type} — {good, null} or {anomalous, <defect>} (one of ablation/breakdown/fracture/groove). Each image's domain-shift condition (background/illumination/same/view) is in metadata.domain_condition; the pixel mask is deferred localization GT — 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. AeBAD (Zhang et al., arXiv 2304.02216, Industrial Anomaly Detection with Domain Shift) — a real-world Aero-engine Blade Anomaly Detection dataset. This repo converts the single-blade sub-dataset AeBAD-S (the video sub-dataset AeBAD-V is not included). Its defining feature is a domain shift between train (normal) and test, driven by changes in illumination and viewpoint; targets are also unaligned and at varying scales.
Query & answer (this repo's SFT task). query is our own instruction template (the dataset ships no
question); it names the 4 defect types and asks for the label + defect type. annot = plain-text
{good, null} or {anomalous, <defect>}, one of ablation / breakdown / fracture / groove.
Domain condition (in metadata). Every image is captured under one of 4 conditions — background,
illumination, same (aligned/in-distribution), view — recorded in metadata.domain_condition. This is the
axis the dataset was built to stress; it is provenance, not part of the answer.
Mask (deferred localization GT). Each anomalous image ships a pixel ground-truth mask (mask column),
matched by basename under ground_truth/<defect>/<condition>/, with defect_area_fraction in metadata; good
images have mask=null. Localization is deferred.
Split. train = 521 normal images (defect-free, across conditions); test = 490 good + 1,149 anomalous
(4 defect types × 4 conditions) = 1,639. Standard one-class AD protocol with a domain-shifted test set.
Provenance
Underlying dataset: AeBAD-S. Upstream license: other (research use; Zhang et al., arXiv 2304.02216) (this card is license: other; respect the upstream terms). Converted read-only from the raw source into the unified schema; conversion script: 179/convert_d79.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.