---

name: model-integrations description: "Route SAHI model loading across supported detector and segmenter frameworks and optional dependencies." disable-model-invocation: true metadata: disco-role: operating license: MIT

model-integrations

Use this sub-skill when a task needs to choose, configure, or debug a SAHI detection model wrapper before prediction. It covers AutoDetectionModel.from_pretrained(...), framework-specific model loading, optional dependency gates, device selection, category mapping, preloaded models, and credential/local-weight distinctions.

Route here when

• Selecting a model_type for Ultralytics/YOLO, YOLOE, YOLO-World, YOLOv5, RT-DETR, HuggingFace detection or segmentation, TorchVision, MMDetection, Detectron2, Roboflow Universe, or local RF-DETR.
• Explaining model_path, config_path, model, device, image_size, category_mapping, category_remapping, HuggingFace text labels/tokens, or Roboflow API-key behavior.
• Diagnosing import failures for optional detector frameworks before running inference.
• Deciding between open-vocabulary routes such as YOLOE, YOLO-World, and HuggingFace GroundingDINO.

Do not handle here

• Generic get_prediction, get_sliced_prediction, CLI prediction, slice sizes, overlap, or export calls: route to sliced-inference.
• NMS/NMM/GreedyNMM backend selection and postprocess acceleration: route to postprocess-backends.
• COCO conversion, dataset slicing/merging, evaluation, and dataset utilities: route to dataset-tools.

Operating steps

1. Identify the detector family and desired source of weights: local file, framework model name, hosted Hub/Universe id, or a preloaded Python model object.

2. Open references/model-matrix.md to map the family to model_type, required arguments, source-backed caveats, and category mapping rules.

3. Open references/optional-dependencies.md before installing or importing optional frameworks. Treat optional wrappers as source-backed unless checked in the target runtime.

4. Run the safe checker when you only need dependency visibility:

   python scripts/check_model_dependencies.py
   

   The checker uses metadata and import-spec probes only; it does not load models or contact remote services.

5. If loading still fails, use references/troubleshooting.md to separate missing optional packages, missing model weights, credential problems, model/config mismatches, device issues, and category-id mapping mistakes.

Credential and network stance

• Prefer local weight/config paths for reproducible offline work.
• HuggingFace private or gated models use a token argument or HF_TOKEN at runtime.
• Roboflow Universe model ids use an api_key argument or ROBOFLOW_API_KEY at runtime.
• Do not bake tokens, API keys, or machine-specific paths into scripts, notebooks, or generated examples.

Verification stance

Core SAHI source, model wrappers, docs, tests, and demo notebooks were inspected. Optional detector frameworks were not all installed in the construction environment, so framework-specific runtime behavior should be treated as source-backed until the checker and a minimal model load are run in the target environment.