L’interface est localisée ; les présentations et documents des Skills sont traduits automatiquement par IA dans votre langue. Le code, les commandes, chemins et termes techniques restent en version originale, et le document de l’auteur fait foi.
Explorer les Skills
Productivité · SKILL.md

model-inference

model-inference

Plan and validate TIAToolbox model inference with PatchPredictor, DeepFeatureExtractor, semantic segmentation, nucleus detection, and multitask segmentation engines.

Independent GitHub snapshot · Automated classification · Original documentation governs

Documentation de la Skill

GitHub

name: model-inference description: "Plan and validate TIAToolbox model inference with PatchPredictor, DeepFeatureExtractor, semantic segmentation, nucleus detection, and multitask segmentation engines." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Model Inference

Use this sub-skill when a task needs TIAToolbox model engines, pretrained model selection, custom weights, device planning, IO config construction, output format decisions, or migration from deprecated nucleus instance segmentation APIs.

Start Here

  • For engine/API signatures and run-parameter contracts, read references/api-reference.md.
  • For pretrained model key selection without downloading weights, read references/model-registry.md and use scripts/model_registry_probe.py.
  • For end-to-end planning patterns, read references/workflows.md.
  • For common inference failures and safe mitigations, read references/troubleshooting.md.

Owned Capabilities

  • Patch classification with PatchPredictor and deep embedding extraction with DeepFeatureExtractor.
  • Semantic segmentation with SemanticSegmentor and nucleus detection with NucleusDetector.
  • Multitask and nucleus instance segmentation with MultiTaskSegmentor, including migration away from deprecated NucleusInstanceSegmentor.
  • Model zoo key validation, custom weight routing, CPU/GPU/MPS device choices, IO config validation, and output type planning.
  • Inference CLI plan review for patch-predictor, deep-feature-extractor, semantic-segmentor, nucleus-detector, nucleus-instance-segment, and multitask-segmentor.

Boundaries

  • For mask creation, tile selection, stain normalization, and patch extraction fundamentals, route to ../image-preprocessing/.
  • For WSI reader metadata, resolution semantics, and WSIReader behavior, route to ../wsi-io/.
  • For viewing or editing generated AnnotationStore outputs, route to ../annotation-visualization/.
  • For an exhaustive CLI option catalog and project-level configuration conventions, route to ../cli-and-configuration/.

Safety Rules

  • Do not imply pretrained weights are available locally; model-string construction may download weights unless weights points to local compatible weights.
  • Prefer device="cpu" unless the user or environment explicitly confirms cuda or mps availability.
  • Validate model keys with the bundled registry probe before constructing a pretrained engine.
  • In WSI mode, require a save directory for file-backed outputs and verify output_type matches the engine capability.
Skills enregistrées