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); 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: 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]	(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. 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.

On the 4 class ids. Severstal never released the meaning of classes 1–4; the numeric ids remain the only official labels, so this repo's task is strictly binary (good / anomalous) and the ids are kept only as raw deferred GT in metadata.defect_classes (no semantic naming is asserted).

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 code under 193/ (with publish/push_to_hf.py) 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.

Geometry (metadata.geometry)

Every record carries a geometry block inside the existing metadata JSON string, so that its gold can be re-derived at any render size. No schema column changed; existing loaders are unaffected.

Coordinates are native pixels of the image in that record. scale is 1.0 throughout — this repo publishes at source resolution, nothing was downscaled at publish time.

"geometry": {
  "image_wh":  [W, H],        // dims of the image in THIS record
  "source_wh": [W, H],        // dims of the original source image
  "scale": 1.0,               // image_wh / source_wh; < 1.0 would disclose a publish-time downscale
  "n_instances": 2,
  "instances": [
    { "instance_id": 1, "bbox_xywh": [x, y, w, h], "min_side_px": 65, "class": null }
  ],
  "n_dropped_subminimum": 0,  // components removed by the filters below
  "union_box_fallback": false,// true => boxes are per-class unions, NOT real instances
  "conventions": { ... }      // see table
}

instances is present even when empty. [] means the record genuinely has no defects; an absent block would mean geometry could not be recovered. Those are different states and are never conflated.

Conventions used to derive it

There is no universal definition of "one defect instance" — it depends on the mask the source shipped. This repo's is stated, not implied:

field	value
algorithm	transitive_box_merge
binarisation	gt:0
connectivity	8
merge	box_transitive:12
min_area_px	0
max_instances	None
artifact	fine
fill_floor	None
legibility_floor_px	None
min_side_floor_px	None
spec_sha	4619037bbfa82f39

Provenance and verification

records	12,568
carrying a geometry block	12,568 / 12,568
instances per record	0: 5,902, 1: 2,531, 2: 1,785, 3: 991, 4: 545, 5+: 814
total instances	16,471
image dimensions	1600×256 (12,568)
scale values present	[1.0]

Derived from the AI4Manufacturing/193 masks and verified against this repo's own published answers before it was written — a recomputation that disagreed with the shipped gold would have aborted the update rather than overwritten it.

Using it

Coordinates only stay correct if they are rescaled with the image. A patch-based VLM does not render at native size: Qwen2-VL's processor snaps both dimensions to a multiple of 28, so a 1600×256 strip is rendered 1596×252 and native-pixel boxes are then wrong by a few pixels. forge_model/193/adapt.py regenerates coordinates for a target render size, re-derives counts, and drops records whose gold no longer holds there.