41 lines
1.5 KiB
Markdown
41 lines
1.5 KiB
Markdown
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# RKNN optimization for exporting model
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## Source
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Base on https://github.com/ultralytics/ultralytics with commit id as 50497218c25682458ea35b02dcc5d8a364f34591
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## What different
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With inference result values unchanged, the following optimizations were applied:
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- Change output node, remove post-process from the model. (post-process block in model is unfriendly for quantization)
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- Remove dfl structure at the end of the model. (which slowdown the inference speed on NPU device)
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- Add a score-sum output branch to speedup post-process.
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All the removed operation will be done on CPU. (the CPU post-process could be found in **RKNN_Model_Zoo**)
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## Export ONNX model
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After meeting the environment requirements specified in "./requirements.txt," execute the following command to export the model (support detect/segment/pose/obb model):
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```
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# Adjust the model file path in "./ultralytics/cfg/default.yaml" (default is yolo11n.pt). If you trained your own model, please provide the corresponding path.
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# For example, filled with yolo11n.pt for detection model.
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# Filling with yolo11n-seg.pt for segmentation model.
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# Filling with yolo11n-pose.pt for pose model.
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# Filling with yolo11n-obb.pt for obb model.
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export PYTHONPATH=./
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python ./ultralytics/engine/exporter.py
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# Upon completion, the ".onnx" model will be generated. If the original model is "yolo11n.pt," the generated model will be "yolo11n.onnx"
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```
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## Convert to RKNN model, Python demo, C demo
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Please refer to https://github.com/airockchip/rknn_model_zoo.
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