classify op• Data kinds: table × matrix → table
• Call: import fullseye as fs; fs.ledger.classify_class_mlp(model, features) (to call the implementation directly, import mvclass; mvclass.classify_class_mlp(model, features); from the registry, opsmv.get("classify_class_mlp"))
• HALCON equivalent: classify_class_mlp (the HALCON reference is a useful guide to its meaning and parameters)
• GPU: none (CPU reference implementation only)
Classify feature rows with the multilayer perceptron from train_class_mlp.
> The detailed description below is the original text — the summary and the headings are translated.
中間層の活性は学習の入口と同じ tanh で、出力の softmax を確信度にする。空の入力(features が 0 要素)は ValueError。
Args:
model: train_class_mlp の返り。
features: (N, D) の特徴。
Returns:
labels (N,) と confidence (N,) を持つ dict。
• machine_vision_classics family guide
• 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.
• machine_vision_tour — py -3.11 examples/machine_vision_tour.py
• poc_real_typewritten_ocr — py -3.11 examples/poc_real_typewritten_ocr.py
• poc_surface_defect_classifiers — py -3.11 examples/poc_surface_defect_classifiers.py
table as input)map_image · rectify_to_world_plane · change_radial_distortion_image · find_ncc_models · run_bg_esti · give_bg_esti · update_bg_esti · classify_class_gmm
classify)train_class_gmm · classify_class_gmm · evaluate_class_gmm · train_class_knn · classify_class_knn · train_class_mlp · train_class_svm · classify_class_svm
*Provenance: mvclass.py — MV 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.