README.md

metadata

tags:
  - smart-manufacturing
  - sft
  - industrial
  - vision
license: other
pretty_name: '210'
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.

210

Outdoor maintenance-inspection anomaly detection over 7 asset scenarios (SYNTHETIC, 3D-rendered; binary 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

105,000 records (test=35000 · train=70000). 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); for multi-image rows, a preview of the first view
images	list[Image]	(multi-image rows) all input views / modalities for the row, bytes embedded
annot	str	the answer — for this dataset: plain-text {label, defect_type} — {good, null} or {anomalous, <defect>}, the defect name from THAT scenario's own closed set (enumerated in the query), following D20/D22. The binary mask column is deferred localization GT
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]	(multi-image T-B1 / D21) per-view masks aligned with images (None where a view has no defect), or multi-region masks

Task, mask & split

What this is. MIAD — Maintenance Inspection Anomaly Detection (Bao, Chen, Li, Wang, Fei, Wu, Zhao, Zheng, arXiv:2211.13968; ICCV 2023 Workshop) — 105,000 512x512 images across 7 outdoor maintenance-inspection scenarios: catenary_dropper, electrical_insulator, metal_welding, nut_and_bolt, photovoltaic_module, wind_turbine, witness_mark. The design is perfectly regular: every scenario ships 10,000 good training images and a 5,000-image test split (2,500 good + 2,500 defective) with 2,500 pixel masks.

These are outdoor assets in service — overhead lines, turbine blades, PV modules — not factory production-line parts, which is what most of this corpus holds.

⚠ THE IMAGERY IS SYNTHETIC. MIAD is generated with 3D graphics software, not photographed. That is the point of the dataset — it buys free variation in viewpoint, weather and lighting together with exact pixel ground truth — but a model trained on it learns rendered appearance, and any claim about real-world transfer needs a real-image test set. Every record carries metadata.synthetic = true so a training mixture can weight or exclude it. The only other synthetic member of this corpus is 182 (Eyecandies); everything else is photographic.

Task & answer. Anomaly detection with defect naming. query is our own template (the source ships no natural-language question): it names the asset and asks whether it is good or anomalous and, if anomalous, for the defect type from that scenario's closed set. annot is plain text {good, null} / {anomalous, <defect>}.

Defect vocabularies differ per scenario, and three are effectively binary. electrical_insulator (broken), wind_turbine (crack) and witness_mark (looseness) have exactly one defect type, so naming it adds nothing beyond the label there; metadata.single_defect_type marks those records. The four richer scenarios are catenary_dropper (broken / looseness / miss), nut_and_bolt (looseness / missbolt / missnut), photovoltaic_module (broken / foreign_body / miss) and metal_welding (weld_beading / weld_pit).

Mask (deferred GT). Binary {0, 255} masks are embedded in the mask column for all 17,500 defective test images; good images carry mask = null.

Lazy-baseline floor. The test split is exactly balanced — 17,500 good vs 17,500 anomalous — so the binary majority floor is 50.0%, and the full {label, defect_type} floor is also 50.0% (answering {good, null} every time). This is one of the cleanest floors in the corpus, a consequence of the synthetic design.

Provenance

Underlying dataset: MIAD (Maintenance Inspection Anomaly Detection). Upstream license: CC BY-NC-SA 4.0 (non-commercial) (this card is license: other; respect the upstream terms). Converted read-only from the raw source into the unified schema; conversion code under 210/ (with publish/push_to_hf.py) in AI4Manufacturing/forge_model.

Overlap / de-duplication (§8)

No overlap with any other dataset in this corpus. ⚠ The imagery is 3D-rendered, not photographed — see the synthetic note below. Each record carries metadata.image_sha256 so overlapping images can be kept entirely on one side of a train/eval split.