44 lines
2.5 KiB
Markdown
44 lines
2.5 KiB
Markdown
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# 简介
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* 此仓库为c++实现, 大体改自[rknpu2](https://github.com/rockchip-linux/rknpu2), python快速部署见于[rknn-multi-threaded](https://github.com/leafqycc/rknn-multi-threaded)
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* 使用[线程池](https://github.com/senlinzhan/dpool)异步操作rknn模型, 提高rk3588/rk3588s的NPU使用率, 进而提高推理帧数
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* [yolov5s](https://github.com/rockchip-linux/rknpu2/tree/master/examples/rknn_yolov5_demo/model/RK3588)使用relu激活函数进行优化,提高量化能力
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* **rk3568等**请自行修改include/rknnPool.hpp下的rknn_lite类和rknnPool的构造函数
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# 更新说明
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* 修复了cmake找不到pthread的问题
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* 新建nosigmoid分支,使用[rknn_model_zoo](https://github.com/airockchip/rknn_model_zoo/tree/main/models)下的模型以达到极限性能提升
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# 使用说明
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### 演示
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* 系统需安装有**OpenCV**
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* 下载Releases中的测试视频于项目根目录,运行build-linux_RK3588.sh
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* 可切换至root用户运行performance.sh定频提高性能和稳定性
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* 编译完成后进入install运行命令./rknn_yolov5_demo **模型所在路径** **视频所在路径/摄像头序号**
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### 部署应用
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* 修改include/rknnPool.hpp中的rknn_lite类
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* 修改inclue/rknnPool.hpp中的rknnPool类的构造函数
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# 多线程模型帧率测试
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* 使用performance.sh进行CPU/NPU定频尽量减少误差
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* 测试模型来源:
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* [yolov5s-silu](https://github.com/rockchip-linux/rknn-toolkit2/tree/master/examples/onnx/yolov5)
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* [yolov5s-relu](https://github.com/rockchip-linux/rknpu2/tree/master/examples/rknn_yolov5_demo/model/RK3588)
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* 测试视频可见于 [bilibili](https://www.bilibili.com/video/BV1zo4y1x7aE/?spm_id_from=333.999.0.0)
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| 模型\线程数 | 1 | 2 | 3 | 4 | 5 | 6 | 12 |
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| ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- |
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| Yolov5s - silu | 15.9269 | 32.9192 | 52.8330 | 46.6782 | 58.2921 | 71.8070 | |
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| Yolov5s - relu | 26.8601 | 58.0305 | 77.6904 | 80.7144 | 93.9126 | 101.1400 | 122.7334 |
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# 补充
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* 异常处理尚未完善, 目前仅支持rk3588/rk3588s下的运行
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* relu版本相较于silu有着较大性能提升, 以及存在一些精度损失, 详情见于[rknn_model_zoo](https://github.com/airockchip/rknn_model_zoo/tree/main/models/CV/object_detection/yolo)
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# Acknowledgements
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* https://github.com/rockchip-linux/rknpu2
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* https://github.com/senlinzhan/dpool
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* https://github.com/ultralytics/yolov5
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* https://github.com/airockchip/rknn_model_zoo
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* https://github.com/rockchip-linux/rknn-toolkit2
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