tags:
- smart-manufacturing
- sft
- industrial
- vision
license: other
pretty_name: D22
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.
D22
Supermarket-goods anomaly detection with per-category defect naming. 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
6,124 records (test=2987 · train=3137). 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} matching the query form — {good, null} or {anomalous, <defect>} where <defect> is the specific per-category defect name (e.g. {anomalous, opened}). The mask column is localization ground truth for a separate, deferred task — 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. PKU-GoodsAD (Zhang et al., IEEE RA-L 2024, arXiv:2307.04956) — unsupervised anomaly detection
& segmentation over 6 supermarket-goods categories, each with real per-category defect types
(deformation, surface_damage, opened, cap_open, cap_half_open, straw_missing, broken,
surface_anomaly), pixel-precise ground-truth masks.
Query & answer (this repo's SFT task). query is our own instruction template (the AD benchmark ships no
natural-language question — only folder labels + masks). It names the good's category, asks the model to decide
good vs anomalous, and, if anomalous, name the defect type from that category's own closed set (enumerated
in the query). The answer form is {label, defect_type} — exactly what annot holds ({good, null} /
{anomalous, <defect>}). The query does not ask for a pixel mask.
Mask (localization ground truth for a separate, deferred task). Each anomalous image ships a single binary
ground-truth mask (mask column; 1 = defect, 0 = background); normal images have mask=null. A text-output
model cannot emit a pixel mask directly, so this release keeps the masks as ground truth but does not frame
localization as the query task (deferred; masks remain for pixel-level evaluation / segmentation models).
Split. train = normal images only; test = normal + anomalous (all defect types). Standard unsupervised
one-class protocol. 22 images appear in both train and test upstream (source duplication) — de-duplicate via
metadata.image_sha256 before building any split.
Note. The raw PKU-GoodsAD folder also carries an added MMAD-style VQA layer (QA.json) and captions (.txt);
those belong to the aggregated-VQA (MMAD) dataset, not this anomaly-detection task, and are not used here.
Provenance
Underlying dataset: PKU-GoodsAD. 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: D22/convert_d22.py, published with publish/push_to_hf.py, both in AI4Manufacturing/forge_model.
Overlap / de-duplication (§8)
Subset of MMAD's image pool; 22 images appear in both train & test (source duplication) -> dedup downstream. Each record carries metadata.image_sha256 so overlapping images can be kept entirely on one side of a train/eval split.