Datasets:
You need to agree to share your contact information to access this dataset
This repository is publicly accessible, but you have to accept the conditions to access its files and content.
This dataset is released for research use. Access is reviewed and granted manually by the maintainers. Please state your name, affiliation, and intended use.
Log in or Sign Up to review the conditions and access this dataset content.
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 —
queryasks only whether the pictured object is good or anomalous, andannotis the plain-text answergoodoranomalous. - Pixel-level localization / segmentation — for every anomalous image the
maskcolumn carries the ground-truth defect mask: a binary image (pixel1= 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.
Overlap / de-duplication (§8)
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.
- Downloads last month
- 30