| --- |
| tags: |
| - smart-manufacturing |
| - sft |
| - industrial |
| - vision |
| license: other |
| pretty_name: "180" |
| 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. |
| --- |
| |
| # 180 |
|
|
| Woven-fabric defect classification (12 defect types; segmentation GT). Category **B**, task **T-B2**, in the unified Smart-Manufacturing SFT schema. |
|
|
| > The repository name is an internal task code. See **Provenance** below for the underlying dataset. |
|
|
| ## Records |
|
|
| **247** records (train=247). 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 the 12 AITEX defect types (the authoritative AFID code->name map is applied). The binary `mask` is deferred localization GT with seg info in `metadata` — 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.** AITEX AFID (Silvestre-Blanes et al. 2019, *A Public Fabric Database for Defect Detection*) — |
| **woven-fabric** surface defect detection & segmentation: 106 defect images across **12 defect types** over 7 |
| fabrics, each with a binary mask, plus 141 defect-free images. |
|
|
| **Query & answer (this repo's SFT task).** `query` is our own instruction template (the dataset ships no |
| question); it names the closed set of 12 defect types and asks for the label + defect type. `annot` = |
| plain-text `{good, null}` or `{anomalous, <defect>}` (one defect type per image). Defect types are named via the |
| **authoritative AFID code→name map** (from the AITEX afid page — filename code `ddd` → name): `002` Broken end, |
| `006` Broken yarn, `010` Broken pick, `016` Weft curling, `019` Fuzzyball, `022` Cut selvage, `023` Crease, |
| `025` Warp ball, `027` Knots, `029` Contamination, `030` Nep, `036` Weft crack. The raw defect code and the |
| fabric code are kept in `metadata` (`defect_code`, `fabric_code`). |
|
|
| **Mask (deferred localization GT).** Each defect image ships a binary mask (`mask` column; white = defect area), |
| with `mask_path`(s) + `defect_area_fraction` in `metadata`; defect-free images have `mask`=null. A few images |
| have multiple mask regions (`mask_paths`). **One defect image (`0100_025_08`, Warp ball) ships no mask** in the |
| source, so its `mask` is null (the image-level label is still `anomalous`) — faithful to the raw data, not |
| fabricated. Localization is deferred. |
|
|
| **Split.** Single `train` split (247 = 106 defect + 141 defect-free); AFID ships no official train/test split. |
|
|
| ## Provenance |
|
|
| Underlying dataset: **AITEX-AFID**. Upstream license: **other (research use; AITEX AFID, Silvestre-Blanes et al. 2019)** (this card is `license: other`; respect the upstream terms). Converted read-only from the raw source into the unified schema; conversion script: `180/convert_d80.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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|
