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name: sliced-inference description: "Guide SAHI standard and sliced prediction APIs, CLI commands, slicing choices, batching, exports, progress callbacks, and video visualization." disable-model-invocation: true metadata: disco-role: operating license: MIT

sliced-inference

Use this sub-skill when the task is to build or debug SAHI prediction calls: AutoDetectionModel, get_prediction, get_sliced_prediction, high-level predict, predict-fiftyone, sahi predict, folder or video inference, slicing parameters, progress reporting, batching, and prediction export routing.

Do not use it as the owner for detector-specific installation or weights. Route those questions to ../model-integrations/SKILL.md. Route COCO conversion/evaluation to ../dataset-tools/SKILL.md, postprocess backend internals to ../postprocess-backends/SKILL.md, and result object serialization details to ../annotations-and-results/SKILL.md.

Start here

1. Choose the API surface in CLI and Python workflows:
   • get_prediction for one standard full-image call.
   • get_sliced_prediction for one image with tiles and optional standard+slice aggregation.
   • predict / sahi predict for files, folders, videos, and automatic exports.
   • predict-fiftyone for interactive FiftyOne review when that optional dependency is installed.
2. Choose the inference mode:
   • Standard only: disable sliced prediction.
   • Sliced only: disable standard prediction.
   • Standard+sliced: default for sliced workflows; useful when small and large objects both matter.
3. Tune slice_height, slice_width, overlap, batching, progress, and duplicate handling with Slicing parameters.
4. If behavior is wrong, use Troubleshooting before changing model code.
5. To validate that base SAHI prediction plumbing works without model weights or downloads, run the bundled synthetic smoke script.

Safe local smoke

From this sub-skill directory, run:

python scripts/sliced_prediction_smoke.py --mode sliced --slice-size 128 --batch-size 2

The smoke script constructs a deterministic in-memory DetectionModel subclass, creates a synthetic image, exercises the selected prediction path, asserts expected result structure, and prints success. It does not download weights, contact a network service, train a model, or write outputs by default.

Boundaries and assumptions

• A real detector still needs a supported model_type, installed optional backend packages, and accessible local weights or model objects; this sub-skill only shows how those model objects are used by prediction APIs.
• Optional detector frameworks were not assumed to be installed or runtime-verified here.
• COCO result JSON export is covered only as prediction-output routing. Dataset conversion, slicing, evaluation, and error analysis belong to ../dataset-tools/SKILL.md.
• NMS/NMM/GreedyNMM parameter placement is covered here; algorithm details and backend acceleration belong to ../postprocess-backends/SKILL.md.