| --- |
| tags: |
| - smart-manufacturing |
| - sft |
| - industrial |
| - vision |
| license: other |
| pretty_name: D25 |
| 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. |
| --- |
| |
| # D25 |
|
|
| Visual anomaly detection & localization (1-class protocol). 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 |
|
|
| **10,821** records (test=2162 · train=8659). 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` (VisA is binary — 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 object 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 — one-class (`1cls`) protocol.** `train` = **normal images only** (no anomalies, no masks); |
| `test` = **normal + anomalous** images, with a mask on each anomalous image (see the exact counts under |
| **Records**). This is the standard unsupervised one-class anomaly-detection protocol; VisA's supervised |
| `2cls` and few-shot protocols (the same images under a different train/test split) are **not** included here. |
|
|
| ## Provenance |
|
|
| Underlying dataset: **VisA**. 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: `D25/convert_d25.py`, published with `publish/push_to_hf.py`, both in [`AI4Manufacturing/forge_model`](https://github.com/AI4Manufacturing/forge_model). |
|
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| ## Overlap / de-duplication (§8) |
|
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| Subset of MMAD's image pool. 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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|
