| --- |
| tags: |
| - smart-manufacturing |
| - sft |
| - industrial |
| - vision |
| license: other |
| pretty_name: D23 |
| 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. |
| --- |
| |
| # D23 |
|
|
| Defect detection & classification (bbox; polygon GT kept in metadata). 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 |
|
|
| **1,894** records (train=880 · validation=1014). |
|
|
| ## 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: one line per defect, `class,[x, y, width, height]` (plain text). The COCO polygon segmentation is preserved as ground truth in `metadata.objects` (not asked in the query) — 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, segmentation & split |
|
|
| **What this is.** VISION (Bai et al., arXiv:2306.07890, 2023) — a vision-based industrial inspection benchmark: |
| 14 subsets, 44 defect types, with COCO **instance-segmentation** annotations (bounding boxes + polygons). Every |
| image is defective; the underlying goal is to find, classify, and outline each defect instance. |
|
|
| **Query & answer (this repo's SFT task).** `query` is our own instruction template (the raw dataset ships no |
| natural-language question — only COCO json). It names the subset, lists that subset's defect classes, and asks the |
| model to detect each defect and give its **class and bounding box**, answering one line per defect as |
| `class,[x, y, width, height]` — exactly what `annot` holds. |
|
|
| **Why bbox, not the polygon.** VISION's segmentation is a real COCO polygon (text coordinates), but the polygons are |
| often very detailed (median ~40 vertices, up to ~2680), which a text-output model cannot realistically reproduce. So |
| the SFT task here is **detection + classification** (class + bbox). The **full COCO instances — including the polygon |
| segmentation — are preserved as ground truth in `metadata.objects`** (each with `category`, `bbox`, `segmentation`, |
| `area`, `iscrowd`) for pixel-precise / segmentation-model evaluation; the polygon is simply not asked of the model. |
|
|
| **Class names.** 10 of the 14 subsets have meaningful defect names (e.g. `break`, `Scratch`, `Porosity`, `mouse_bite`, |
| `open_circuit`); 4 subsets ship generic placeholder names (Capacitor = `0`, Hemisphere = `Defect-A..D`, |
| PCB_2 = `defect1..7`, Screw = `defect`) — kept as-is (faithful to the source). |
| |
| **Split.** `train` + `validation` (per-subset COCO annotations). The `inference` split ships images without ground |
| truth (official eval only) and is **not** included. See **Records** for counts. |
| |
| ## Provenance |
| |
| Underlying dataset: **VISION**. Upstream license: **CC BY-NC 4.0** (this card is `license: other`; respect the upstream terms). Converted read-only from the raw source into the unified schema; conversion script: `D23/convert_d23.py`, published with `publish/push_to_hf.py`, both in [`AI4Manufacturing/forge_model`](https://github.com/AI4Manufacturing/forge_model). |
|
|
| ## Overlap / de-duplication (§8) |
|
|
| Partial overlap with MMAD / DefectSpectrum; dedup by image hash. 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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|
