---

name: "inference-export" description: "Routes NanoDet image/video/webcam inference, checkpoint export, deploy-conversion, and FLOPs workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

inference-export

Use this sub-skill when you need to run a NanoDet demo, export a checkpoint, or prepare a model for deployment backends.

Use this route for

• Image, video, or webcam inference.
• Exporting a checkpoint to ONNX or TorchScript.
• Preparing RepVGG models for deploy mode before export.
• Inspecting FLOPs or model-complexity behavior.
• Reading deployment notes for ncnn, MNN, OpenVINO, or LibTorch.

Do not use this route for

• Training, validation, or checkpoint resume logic. Use training instead.
• Dataset schema / config validation. Use dataset-config instead.

Read first

• references/workflows.md for inference and export flow details.
• references/deployment.md for backend-specific deployment notes.
• references/troubleshooting.md for export and demo failures.
• ../../references/api-reference.md for verified model / utility signatures.

Skill-owned scripts

• scripts/demo.py — run image, video, or webcam inference with explicit device selection.
• scripts/export_onnx.py — export a checkpoint to ONNX and simplify it when possible.
• scripts/export_torchscript.py — export a checkpoint to TorchScript.
• scripts/flops.py — print FLOPs when the optional helper dependency is available.

Typical workflow

1. Validate the config with dataset-config.
2. Make sure the checkpoint is compatible with the selected model family.
3. Run the skill-owned demo or export script.
4. For deployment backends, read the distilled deployment notes before switching toolchains.

Cross-links

• If you need to convert an old .pth checkpoint before export, use training first.
• If you only need to inspect a config or dataset layout, switch to dataset-config.