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57 lines
1.6 KiB
57 lines
1.6 KiB
# yolov8/yolov5 Inference C++ |
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Usage: |
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``` |
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# git clone ultralytics |
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pip install . |
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cd examples/cpp_ |
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Add a **yolov8\_.onnx** and/or **yolov5\_.onnx** model(s) to the ultralytics folder. |
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Edit the **main.cpp** to change the **projectBasePath** to match your user. |
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Note that by default the CMake file will try and import the CUDA library to be used with the OpenCVs dnn (cuDNN) GPU Inference. |
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If your OpenCV build does not use CUDA/cuDNN you can remove that import call and run the example on CPU. |
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mkdir build |
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cd build |
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cmake .. |
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make |
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./Yolov8CPPInference |
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``` |
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To export yolov8 models: |
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``` |
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yolo export \ |
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model=yolov8s.pt \ |
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imgsz=[480,640] \ |
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format=onnx \ |
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opset=12 |
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``` |
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To export yolov5 models: |
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``` |
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python3 export.py \ |
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--weights yolov5s.pt \ |
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--img 480 640 \ |
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--include onnx \ |
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--opset 12 |
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``` |
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yolov8s.onnx: |
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yolov5s.onnx: |
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This repository is based on OpenCVs dnn API to run an ONNX exported model of either yolov5/yolov8 (In theory should work |
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for yolov6 and yolov7 but not tested). Note that for this example the networks are exported as rectangular (640x480) |
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resolutions, but it would work for any resolution that you export as although you might want to use the letterBox |
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approach for square images depending on your use-case. |
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The **main** branch version is based on using Qt as a GUI wrapper the main interest here is the **Inference** class file |
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which shows how to transpose yolov8 models to work as yolov5 models.
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