---

name: deployment-conversion description: "Guide MMYOLO export, checkpoint conversion, and deployment backend workflows." disable-model-invocation: true metadata: disco-role: operating license: GPL 3.0

Deployment Conversion

Use this sub-skill when the user wants to turn an MMYOLO checkpoint into a deployment artifact or reason about a deployment backend.

Use this sub-skill for

• EasyDeploy ONNX export and TensorRT engine build.
• EasyDeploy backend inference over exported ONNX or engine artifacts.
• MMDeploy deployment configs, SDK inference, and backend evaluation.
• YOLO-family checkpoint key conversion into MMYOLO format.
• Optional dependency and hardware gate checks for deployment backends.

Do not use this sub-skill for

• Plain image or video inference from source checkpoints; use inference-visualization.
• Training, validation, testing, or log analysis; use training-evaluation.
• Basic config editing or model-family selection; use config-customization.

Start here

1. Run scripts/check_deployment_dependencies.py.
2. Read references/deployment-conversion.md.
3. Read references/model-converters.md.
4. If the request is blocked, read references/troubleshooting.md.

Coverage

• EasyDeploy export, build, and image-demo flows.
• ONNXRuntime, TensorRT, RKNN, MMDeploy, and SDK-backed deployment paths.
• mmyolo/deploy object detection integration and backend-artifact inference.
• YOLOv5, YOLOv6, YOLOv7, YOLOv8, YOLOX, PPYOLOE, and RTMDet key conversion.
• Optional dependency and hardware gates for ONNXRuntime, TensorRT, RKNN, and DeepStream.

Routing hint

If the user is asking for a deployment artifact, backend config, or conversion plan, stay here. If the user is only asking how to view detections on images or folders, route away to inference-visualization.