Datasets:

AI4Manufacturing
/

D24

tags:
  - smart-manufacturing
  - sft
  - industrial
  - vision
license: other
pretty_name: D24
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.

D24

BeanTech anomaly detection & localization. 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

2,540 records (test=741 · train=1799). 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` (BTAD is binary `ok`/`ko` — no fine-grained defect types). Pixel-level localization is a separate task whose target is the `mask` column — 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

This dataset supports two levels of the anomaly task:

Image-level detection — query asks only whether the pictured product is good or anomalous, and annot is the plain-text answer good or anomalous.
Pixel-level localization / segmentation — for every anomalous image the mask column carries the ground-truth defect mask: a binary image (pixel 1 = defect, 0 = background) at the input resolution. Normal images have no defect and therefore no mask (null). A model addressing the localization task is expected to output a binary mask image of the same height×width (1 = defect pixel, 0 = background); this repo ships that mask as the localization target.

Split. train = normal images only (the ok folders; no anomalies, no masks); test = normal + anomalous (ok + ko), with a mask on each anomalous image (see the exact counts under Records). BTAD ships a single anomaly detection-and-localization protocol (no supervised / few-shot variants); the three products are 01 / 02 / 03.

Provenance

Underlying dataset: BTAD. Upstream license: CC-BY-SA (this card is license: other; respect the upstream terms). Converted read-only from the raw source into the unified schema; conversion script: D24/convert_d24.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.