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name: export-and-deployment description: "Export and deploy RF-DETR models to ONNX, TensorRT, TFLite, ExecuTorch, CoreML, and Roboflow with backend constraints and safe option checks." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

RF-DETR export and deployment

Use this sub-skill when the task is about exporting RF-DETR models or choosing a deployment artifact. Do not use it for ordinary prediction, training, dataset validation, or repository maintenance.

Route quickly

• For the full RFDETR.export(...) contract, format aliases, backend compatibility, file naming, and optional dependencies, read export-reference.md.
• For practical export/deploy choices across ONNX, TensorRT, TFLite, ExecuTorch, CoreML, inference-models, and Roboflow, read deployment-workflows.md.
• For invalid combinations, missing extras, platform constraints, output-name surprises, calibration/import-order issues, and optimized-model failures, read troubleshooting.md.
• Before exporting, run inspect_export_options.py to inspect installed optional packages, validate shape divisibility, check static format/backend constraints, and preview output filename stems without exporting a model.

Default decision rules

1. Prefer format="onnx" as the stable baseline unless the downstream runtime explicitly requires another artifact.
2. Do not export format="tensorrt" for inference-models consumers; give inference-models a plain ONNX or checkpoint path and let it build/manage its own TensorRT engine.
3. Treat TensorRT engines as non-portable build-machine artifacts tied to the local GPU architecture, TensorRT version, and precision.
4. Treat TFLite, ExecuTorch, and native CoreML export as experimental and dependency-sensitive; use isolated environments per backend.
5. Reject dynamic_batch=True for format="executorch" and format="coreml"; export one fixed-batch artifact per target batch size.
6. For ExecuTorch QNN, require backend="qnn" plus a target soc such as SM8650; do not assume the pip wheel contains the QNN delegate.
7. For native CoreML, use format="coreml"; for the ExecuTorch CoreML delegate, use format="executorch", backend="coreml". These are different artifact families.

Minimal safe preflight

python scripts/inspect_export_options.py --variant rfdetr-small --format onnx --shape 512 512
python scripts/inspect_export_options.py --variant rfdetr-small --format executorch --backend qnn --soc SM8650 --shape 512 512

The script is read-only: it imports metadata where available, reports optional-package readiness, validates options, and previews expected filename stems. It does not instantiate RF-DETR, download weights, trace models, or write export artifacts.