README.md

metadata

tags:
  - smart-manufacturing
  - sft
  - industrial
  - vision
license: other
pretty_name: '193'
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.

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.