https://github.com/leafqycc/rknn-multi-threaded

A simple demo of yolov5s running on rk3588/3588s using Python (about 72 frames). / 一个使用Python在rk3588/3588s上运行的yolov5s简单demo(大约72帧/s)。
https://github.com/leafqycc/rknn-multi-threaded

Last synced: 1 day ago
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A simple demo of yolov5s running on rk3588/3588s using Python (about 72 frames). / 一个使用Python在rk3588/3588s上运行的yolov5s简单demo(大约72帧/s)。

• Host: GitHub
• URL: https://github.com/leafqycc/rknn-multi-threaded
• Owner: leafqycc
• License: apache-2.0
• Created: 2023-04-29T16:32:29.000Z (about 1 year ago)
• Default Branch: nosigmoid
• Last Pushed: 2023-05-07T11:41:38.000Z (about 1 year ago)
• Last Synced: 2024-03-12T23:34:00.676Z (4 months ago)
• Language: Python
• Homepage:
• Size: 30 MB
• Stars: 137
• Watchers: 2
• Forks: 26
• Open Issues: 10
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Lists

• awesome-stars - leafqycc/rknn-multi-threaded - A simple demo of yolov5s running on rk3588/3588s using Python (about 72 frames). / 一个使用Python在rk3588/3588s上运行的yolov5s简单demo(大约72帧/s)。 (Python)
• awesome-RK3588 - RKNN multi threaded ↗ - A project for running RKNN models on multiple threads for improved performance. (Projects)

README

        
# 简介

* 使用多线程异步操作rknn模型, 提高rk3588/rk3588s的NPU使用率, 进而提高推理帧数(rk3568之类修改后应该也能使用, 但是作者本人并没有rk3568开发板......)

* 此分支使用模型[yolov5s_relu_tk2_RK3588_i8.rknn](https://github.com/airockchip/rknn_model_zoo), 将yolov5s模型的激活函数silu修改为为relu,在损失一点精度的情况下获得较大性能提升,详情见于[rknn_model_zoo](https://github.com/airockchip/rknn_model_zoo/tree/main/models/CV/object_detection/yolo)

* 此项目的[c++](https://github.com/leafqycc/rknn-cpp-Multithreading)实现

# 更新说明

* 无

# 使用说明

### 演示

  * 将仓库拉取至本地, 并将Releases中的演示视频放于项目根目录下, 运行main.py查看演示示例

  * 切换至root用户运行performance.sh可以进行定频操作(约等于开启性能模式)

  * 运行rkcat.sh可以查看当前温度与NPU占用

### 部署应用

  * 修改main.py下的modelPath为你自己的模型所在路径

  * 修改main.py下的cap为你想要运行的视频/摄像头

  * 修改main.py下的TPEs为你想要的线程数, 具体可参考下表

  * 修改func.py为你自己需要的推理函数, 具体可查看myFunc函数

# 多线程模型帧率测试

* 使用performance.sh进行CPU/NPU定频尽量减少误差

* 测试模型为[yolov5s_relu_tk2_RK3588_i8.rknn](https://github.com/airockchip/rknn_model_zoo)

* 测试视频见于Releases

|  模型\线程数   | 1    |  2   | 3  |  4  | 5  | 6  |

|  ----  | ----    | ----  |  ----  | ----  | ----  | ----  |

| yolov5s  | 27.4491 | 49.0747 | 65.3673  | 63.3204 | 71.8407 | 72.0590 |

# 补充

* 多线程下CPU, NPU占用较高, **核心温度相应增高**, 请做好散热。推荐开1, 2, 3线程, 实测小铜片散热下运行三分钟温度约为56°, 64°, 69°

# Acknowledgements

* https://github.com/ultralytics/yolov5

* https://github.com/rockchip-linux/rknn-toolkit2

* https://github.com/airockchip/rknn_model_zoo