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NanoTrack_RK3588_python

基于瑞芯微RK3588 NPU的NanoTrack跟踪算法,可运行于RK3588开发板,可达120FPS.

dependence

	numpy
	opencv
	rknn_toolkit_lite2 == 1.3 

RKNN3588对应的rknn_toolkit_lite2官方开发库以及开发文档请参考rknn-toolkit2

demo

模型转换需先使用rknn-toolkit2转为.rknn格式

	python3 main.py
  • video_name 为目标视频地址
  • init_rect 为初始检测bbox

reference

rknn-toolkit2
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nanotrack_rk3588_python's Issues

模型是否经过剪枝?

您好,我试用了您的模型,发现效果很不错,比NanoTrack官方提供的模型帧率有明显提升,想问一下您是自己又经过了剪枝等工作吗?

请教模型转换为rknn方法

大佬你好,我看你是在rk3588上运行的nonatrack rknn模型,跟踪效果怎么样呢,fps是120吗,我只在pc上测试过nanotrack,能力有限不能转化为rknn,能否分享下你的转换方法呢

模型输入尺寸可以自适应调整吗

您好,像问一下模型输入尺寸是否必须固定为127,255?我尝试设置其他尺寸转为rknn模型,板上推理不报错但是得分很低,这是什么原因?感谢您的回答!

input tensor type problem

Hello, I want to run this implementation using the RKNPU2 C++ APIs, but I encountered a problem. When I examine the models one by one in Netron App, I see that the inputs of the models are in NCHW format. However, when I load the model using the RKNPU2 C++ APIs, I see that the inputs are in NHWC format. Therefore, I believe that the implementation is not working correctly. How can I resolve this issue? Can you assist me?

转rknn的细节

您好,请问转rknn时,mean_values,std_values都是多少,量化提供了多少张图片呢,head模型量化的数据是否来自前两个backbone模型的输出

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