| --- |
| tags: |
| - smart-manufacturing |
| - sft |
| - industrial |
| - vision |
| license: other |
| pretty_name: "188" |
| 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. |
| --- |
| |
| # 188 |
|
|
| Mobile-phone screen surface anomaly detection (3 defect types; segmentation GT). 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 |
|
|
| **1,220** records (test=1200 · train=20). 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 oil/scratch/stain). The palette-mode segmentation `mask` is deferred localization GT, with seg info (`mask_path`, `defect_area_fraction`) 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.** MSD (Mobile-phone Screen surface Defect; jianzhang96/MSD) — MVTec-style **unsupervised |
| anomaly detection & segmentation** of phone-screen surfaces (1920×1080, industrial camera). This repo uses |
| the **MSD-US** package: 20 defect-free training images + 1,200 defective test images across **3 defect |
| types** (oil, scratch, stain; 400 each), each with a pixel-level segmentation mask. (The README describes a |
| "PASCAL VOC" packaging; the MSD-US package we use ships **no VOC XML** — it is AD/segmentation with masks.) |
|
|
| **Query & answer (this repo's SFT task).** `query` is our own instruction template (the dataset ships no |
| question); it names the closed set of 3 defect types and asks for the label + type. `annot` = plain-text |
| `{good, null}` or `{anomalous, <defect>}` (one of oil/scratch/stain), the type taken from the source folder. |
|
|
| **Mask (deferred localization GT).** Each defective image ships a palette-mode segmentation `mask` (matched |
| 1:1 by image stem under `test/ground_truth/`), with `mask_path` + `defect_area_fraction` in `metadata`; |
| defect-free images have `mask`=null. A text-output model cannot emit a pixel mask, so localization is deferred. |
|
|
| **Split.** `train` = 20 defect-free images; `test` = 1,200 defective (oil/scratch/stain, 400 each). This is |
| the unsupervised protocol — all defect-free images are used for training, so `test` is defect-only. |
|
|
| ## Provenance |
|
|
| Underlying dataset: **MSD**. Upstream license: **GPL-3.0** (this card is `license: other`; respect the upstream terms). Converted read-only from the raw source into the unified schema; conversion script: `188/convert_d88.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. |
|
|
