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.
207
3C-electronics production-line anomaly detection over 8 manufactured parts (47 defect types; binary masks). 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
27,039 records (test=16546 · train=10493). 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); for multi-image rows, a preview of the first view |
images |
list[Image] | (multi-image rows) all input views / modalities for the row, bytes embedded |
annot |
str | the answer — for this dataset: plain-text {label, defect_type} — {good, null} or {anomalous, <defect>}, where <defect> is the specific defect name from THAT category's own closed set (enumerated in the query), following D20/D22. Multiple-defects is a valid gold answer and is a meta-label, not a 48th defect type — see Task, mask & split below. The binary mask column is deferred localization GT |
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] | (multi-image T-B1 / D21) per-view masks aligned with images (None where a view has no defect), or multi-region masks |
Task, mask & split
What this is. 3CAD (Yang, Xing et al., "3CAD: A Large-Scale Real-World 3C Product Dataset for Unsupervised
Anomaly Detection", AAAI 2025) — 27,039 images captured on real 3C (computer / communication / consumer-
electronics) production lines across 8 manufactured parts: Aluminum_Camera_Cover, Aluminum_Ipad,
Aluminum_Middle_Frame, Aluminum_New_Ipad, Aluminum_New_Middle_Frame, Aluminum_Pc, Copper_Stator,
Iron_Stator. Standard unsupervised-AD layout: train = 10,493 good only; test = 16,546 (5,084 good +
11,462 defective). Image sizes vary by category (288x288 to 1024x1024) and are published at source resolution.
All counts reproduce the paper's Table 1 exactly.
Task & answer. Anomaly detection with defect naming. query is our own template (the source ships no
natural-language question): it names the part and asks whether it is good or anomalous and, if anomalous,
for the defect type from that category's own closed set, which is enumerated in the query. annot is plain text
{good, null} / {anomalous, <defect>}. The query does not ask for a mask.
⚠ Multiple-defects is a meta-label, not a defect type. Five of the eight categories carry a
Multiple-defects folder for images showing several defect types at once, and it is a valid gold answer here
because it is the label the authors assigned. It is not one of the paper's 47 types — confirmed by
construction: the 8 categories hold 52 defect folders in total, 5 of them Multiple-defects, and 52 - 5 = 47.
Its mask is the union of the regions of the types present, and the individual types are not recoverable from
the release. Anyone training a pure defect-type classifier should keep it as its own class or drop those images;
treating it as a 48th type is wrong.
Mask (deferred GT). Every one of the 11,462 defective images has a ground-truth mask (verified: zero missing).
Masks are genuinely binary ({0, 255}) and pixel-aligned with the image. Good images carry mask = null.
⚠ Label-quality defect, upstream: 79 images carry contradictory labels. 79 image files appear byte-identically
in both Copper_Stator and Iron_Stator while being labelled differently — 35 as
{anomalous, wire damage} vs {anomalous, inner warping}, 30 as {good, null} vs {anomalous, inner warping},
and 14 as {anomalous, wire damage} vs {good, null}. These are ordinary images, not blanks or placeholders, so
this is a folder-management error in the source, not a rendering artefact. 158 of 27,039 rows (0.58%) are
affected. We publish the labels exactly as released rather than silently choosing a winner; use
metadata.image_sha256 to find and drop them. A further 49 images (unrelated to the above) appear in both the
train and test splits — dedup by the same key before evaluating.
Lazy-baseline floors (report accuracy against these, not against chance).
| test question | n | majority answer | floor |
|---|---|---|---|
| binary good vs anomalous | 16,546 | anomalous (11,462) |
69.3% |
full {label, defect_type} (26 distinct answers) |
16,546 | {good, null} (5,084) |
30.7% |
The binary framing is close to saturated by guessing; the defect-naming framing is the informative one.
Provenance. The authors ship two releases: 3CAD (English defect names) and 3CAD-Pinyin (identical data,
Pinyin folder names). This repo converts the English release.
Provenance
Underlying dataset: 3CAD. Upstream license: not stated by the authors (public Google Drive release; research use — verify before redistribution) (this card is license: other; respect the upstream terms). Converted read-only from the raw source into the unified schema; conversion code under 207/ (with publish/push_to_hf.py) in AI4Manufacturing/forge_model.
Overlap / de-duplication (§8)
No overlap with any other dataset in this corpus. ⚠ 79 images appear in both Copper_Stator and Iron_Stator with contradictory labels upstream — see the label-quality note below. Each record carries metadata.image_sha256 so overlapping images can be kept entirely on one side of a train/eval split.
- Downloads last month
- 11