score op• Data kinds: labels2d × labels2d → table
• Call: import fullseye as fs; fs.ledger.seg_score_card(labels_pred, labels_true, *, background: 'int' = 0, tau: 'float' = 2.0, min_overlap: 'float' = 0.5) -> 'Dict[str, float]' (to call the implementation directly, import segeval; segeval.seg_score_card(labels_pred, labels_true, *, background: 'int' = 0, tau: 'float' = 2.0, min_overlap: 'float' = 0.5) -> 'Dict[str, float]'; from the registry, opssegmentation.get("seg_score_card"))
All measures on one card: Dice/Jaccard, VI/split/merge, Rand/ARI, boundary F, Hausdorff/HD95/ASSD, over/under, split/merge/missed/false/matched and counts (seg_score_card).
> The detailed description below is the original text — the summary and the headings are translated.
`segcompare` には a = 真、b = 予測で渡す(split = 予測が真を切った量、merge = 予測が真を貼った量)。
値は float か int の平らな dict(表にそのまま並ぶ)。
• halcon_segmentation family guide
• segmentation_scoring_and_worlds — 分割の採点と真値つき合成世界 — どの物差しがどの壊れ方に盲目か
• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).
• Operator provenance and references — the sources of the research/methods this op family came from.
• The canonical algorithm (author, year) and its uses are named in the family usage guide above.
• poc_active_contours — py -3.11 examples/poc_active_contours.py
• poc_graph_hierarchy_segmentation — py -3.11 examples/poc_graph_hierarchy_segmentation.py
• poc_segmentation_gauntlet — py -3.11 examples/poc_segmentation_gauntlet.py
table as input)score)seg_confusion_table · seg_dice_jaccard · seg_boundary_f · seg_hausdorff · seg_mean_surface_distance · seg_under_over_segmentation · seg_object_counts_match
*Provenance: segeval.py — SEGMENTATION operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*
© 2026 Kazufumi Furuse — Fullseye operator documentation. Licensed under Apache-2.0.