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Déploiement et opérations · SKILL.md

export

export

Use this YOLOv5 sub-skill for model export, deployment formats, backend prerequisites, benchmarks, TensorRT/ONNX/CoreML/TFLite/OpenVINO/Paddle issues, and export safety.

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Documentation de la Skill

GitHub

name: export description: "Use this YOLOv5 sub-skill for model export, deployment formats, backend prerequisites, benchmarks, TensorRT/ONNX/CoreML/TFLite/OpenVINO/Paddle issues, and export safety." disable-model-invocation: true metadata: disco-role: operating license: AGPL 3.0

YOLOv5 Export and Benchmarks

Use this route for export.py, benchmarks.py, deployment-format conversion, export prerequisite checks, TensorRT/ONNX/CoreML/OpenVINO/TensorFlow/TFLite/TF.js/Paddle/Edge TPU issues, or benchmark planning.

Choose the workflow

  • Export a checkpoint: read references/formats.md to choose --include formats, dependencies, device, image size, dynamic/NMS options, and validation strategy.
  • Check optional dependencies: run scripts/check_export_prereqs.py --formats onnx torchscript or another format list before installing heavy packages or converting models.
  • Benchmark formats: read references/benchmarks.md before running benchmarks.py; benchmark commands can export models, download weights/data, and run validation.
  • Backend errors: read references/troubleshooting.md for TensorRT version mismatch, ONNX dependency, Edge TPU compiler, TensorFlow/Keras, CoreML, OpenVINO, and path-safety failures.

Common decisions

  • Export only after selecting a task-compatible checkpoint from detection, segmentation, or classification.
  • ONNX is the smallest common non-PyTorch export dependency; TorchScript does not need ONNX.
  • TensorRT engines should be built and validated on the target runtime stack.
  • --half and --device 0 imply CUDA-capable execution; do not force them on CPU.
  • Keep output directories/files explicit and isolated.
  • Do not run broad --include lists just to see what happens; install/check only selected formats.

Handoffs

  • Route model training/selection to ../detection/SKILL.md, ../segmentation/SKILL.md, or ../classification/SKILL.md first.
  • Route Flask HTTP serving to ../serving/SKILL.md if the user wants the repository's Flask example instead of an exported artifact.
  • Read root references/environment.md for optional dependency grouping and backend setup.
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