| --- |
| 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`](https://github.com/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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|
