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193
Steel-sheet surface anomaly detection (binary; 4 anonymous class ids + segmentation kept as 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
12,568 records (train=12568). 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. Severstal's 4 official defect classes are UNNAMED (numeric ids only), so — following the same principle as DAGM — the anonymous class id is NOT asked of the model; the per-image class-id list and the RLE-decoded class-indexed segmentation mask are kept as deferred ground truth in metadata.defect_classes and 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
What this is. Severstal Steel Defect Detection (Kaggle 2019) — originally pixel-level 4-class defect
segmentation of steel-sheet surfaces (1600×256). The four defect classes are official but UNNAMED:
Severstal (the steel maker) never released what they physically mean — the labels are only the numeric ids
1–4. Data is obtained from the public HF mirror rohanath/severstal-steel-detection.
Query & answer — why BINARY (this repo's SFT task). Because the class ids are anonymous, there is no
semantic concept for a vision-language model to ground: asking it to output 1 vs 3 would be asking it to
reproduce an arbitrary, meaningless label. So — exactly as we do for DAGM (also anonymous classes) — the
task is binary: query asks only good vs anomalous, and annot is the plain-text label good or
anomalous (label = anomalous iff the image has a defect annotation). We do not put the anonymous class
id in annot.
Anonymous class ids + mask (deferred GT, in metadata / mask column). The class information is not
discarded — it is preserved as ground truth: metadata.defect_classes holds the per-image list of defect
class ids present (e.g. [1, 3]; 427 images carry more than one), and the competition's run-length-encoded
(RLE) masks are decoded (column-major) into a class-indexed segmentation mask (pixel value = defect
class id), kept as deferred localization GT with defect_area_fraction in metadata. A downstream user who
obtains a semantic naming for the 4 classes can recover the full multi-label / segmentation task from these.
What the 4 classes are — UNOFFICIAL, community observation (pending manual verification). Severstal never
released the meaning of classes 1–4; the numeric ids remain the only official labels and annot stays binary.
For reference only, independent community exploratory analyses of the competition (Kaggle discussions and
public write-ups) consistently describe the classes by appearance — not official names — roughly as:
Class 1 = small localized spots / pinhole-like marks; Class 2 = thin small linear marks (a small
crack-like defect); Class 3 = larger linear defects / scratches with distinct edges (classes 2 and 3 look
similar and are hard to tell apart); Class 4 = large surface patches (the most visually distinct). The
class frequencies in this dataset — 3 ≫ 1 > 4 > 2 (5,150 / 897 / 801 / 247) — match those write-ups.
These are unofficial community descriptions, NOT Severstal's own defect taxonomy — they are provided only to
help interpret metadata.defect_classes and are subject to manual verification; do not treat them as
ground-truth type names. (A "pitted / crazing / scratches / patches" naming also circulates online but
appears to be borrowed from the unrelated NEU-DET dataset, so it is not adopted here.)
What is excluded (audit). The Kaggle test set (5,506 images, GT withheld) is dropped; the mirror's
visualized_images/ are mask overlays (would leak the answer) and are excluded; the mirror's
labels/*.txt are derived YOLO boxes (computed from the RLE) — not used, the RLE is the raw GT.
Split. Single train pool = 12,568 images: 6,666 defective (RLE-annotated) + 5,902 defect-free.
The mirror's degenerate train(all-defective)/val(all-good) partition is not used as a split.
Provenance
Underlying dataset: Severstal. Upstream license: other (Kaggle competition data; via HF mirror rohanath/severstal-steel-detection) (this card is license: other; respect the upstream terms). Converted read-only from the raw source into the unified schema; conversion script: 193/convert_d93.py, published with publish/push_to_hf.py, both in AI4Manufacturing/forge_model.
Overlap / de-duplication (§8)
The Kaggle test set (GT withheld) and the mirror's derived YOLO labels are excluded; the RLE is the raw GT. Each record carries metadata.image_sha256 so overlapping images can be kept entirely on one side of a train/eval split.
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