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.
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.
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.
- Downloads last month
- 9