yolov5-lite模型转换

yolov5-lite模型转换的过程可以分为以下几个步骤。首先，将模型转换为ONNX格式，可以使用命令"./onnx2ncnn"来进行转换。例如，"./onnx2ncnn yolov5ss-sim.onnx yolov5-lite.param yolov5-lite.bin"。\[1\]接下来，可以使用命令"./ncnnoptimize"对转换后的模型进行优化，例如"./ncnnoptimize yolov5-lite.param yolov5-lite.bin yolov5-lite-opt.param yolov5-lite-opt.bin 65536"。\[1\]在转换完成后，可以使用Oenvino框架进行推理，而不需要依赖pytorch等库。可以直接复制"利用Oenvino推理"的代码来进行推理。\[2\]最后，可以使用命令"./ncnn2table"将模型转换为table格式，例如"./ncnn2table yolov5-lite-opt.param yolov5-lite-opt.bin imagelist.txt yolov5-lite.table mean=\[104,117,123\] norm=\[0.017,0.017,0.017\] shape=\[640,640,3\] pixel=BGR thread=8 method=kl"。\[3\]如果需要进行int8量化，可以使用命令"./ncnn2int8"来进行转换，例如"./ncnn2int8 yolov5-lite-opt.param yolov5-lite-opt.bin yolov5-ite-opt-int8.param yolov5-lite-opt-int8.bin yolov5-lite.table"。\[3\]这样就完成了yolov5-lite模型的转换过程。
#### 引用[.reference_title]
- *1* *3* [YOLOv5-Lite:NCNN+Int8部署和量化，树莓派也可实时](https://blog.csdn.net/weixin_45829462/article/details/119787840)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^control_2,239^v3^insert_chatgpt"}} ] [.reference_item]
- *2* [(YOLOv5-lite)-ONNX模型转换及Openvino推理](https://blog.csdn.net/weixin_45930948/article/details/124359754)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^control_2,239^v3^insert_chatgpt"}} ] [.reference_item]
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