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	<title>rknn - 四号程序员</title>
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		<title>ultralytics的yolov11模型直接转rknn运行</title>
		<link>https://www.coder4.com/archives/8280</link>
					<comments>https://www.coder4.com/archives/8280#respond</comments>
		
		<dc:creator><![CDATA[coder4]]></dc:creator>
		<pubDate>Thu, 27 Feb 2025 04:02:43 +0000</pubDate>
				<category><![CDATA[Linux]]></category>
		<category><![CDATA[机器学习]]></category>
		<category><![CDATA[npu]]></category>
		<category><![CDATA[radxa]]></category>
		<category><![CDATA[rk3566]]></category>
		<category><![CDATA[rknn]]></category>
		<category><![CDATA[yolo]]></category>
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					<description><![CDATA[ultralytics最近官方支持了rknn模型的导入，整体流程比用rknntool简单了不少，当然也有坑，记录下。我用的是yolov11，不确定对于v8等是否能用，大家可以评论区反馈我。 PS：如果你需要瑞莎radxa、香橙派orange pi的 屏幕、外壳、散热器，可以来我的咸鱼(coder4)看看，欢迎扫码关注 1 PC上模型转换 环境 python -m venv ./ultralytics-env source ./ultralytics-env/bin/[......] 继续阅读]]></description>
		
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		<title>[转]记录如何在RK3588板子上跑通paddle的OCR模型</title>
		<link>https://www.coder4.com/archives/8262</link>
					<comments>https://www.coder4.com/archives/8262#respond</comments>
		
		<dc:creator><![CDATA[coder4]]></dc:creator>
		<pubDate>Fri, 06 Dec 2024 16:35:42 +0000</pubDate>
				<category><![CDATA[Linux]]></category>
		<category><![CDATA[机器学习]]></category>
		<category><![CDATA[OCR]]></category>
		<category><![CDATA[rknn]]></category>
		<category><![CDATA[深度学习]]></category>
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					<description><![CDATA[原文链接：https://blog.csdn.net/m0_60657960/article/details/143209851 参考链接：https://blog.csdn.net/Fzq1021/article/details/133508218 1 PC电脑是Ubuntu22.04系统中完成环境搭建(板子是20.04） 安装模型转换环境 conda create -n rknn2 python==3.10 conda activate rknn2 安装Ubuntu依[......] 继续阅读]]></description>
		
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		<title>[转]rk3588对npu的再探索，yolov5使用rknn模型推理教程</title>
		<link>https://www.coder4.com/archives/8229</link>
					<comments>https://www.coder4.com/archives/8229#respond</comments>
		
		<dc:creator><![CDATA[coder4]]></dc:creator>
		<pubDate>Wed, 23 Oct 2024 04:53:11 +0000</pubDate>
				<category><![CDATA[机器学习]]></category>
		<category><![CDATA[npu]]></category>
		<category><![CDATA[rknn]]></category>
		<category><![CDATA[深度学习]]></category>
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					<description><![CDATA[原文转载自《rk3588对npu的再探索，yolov5使用rknn模型推理教程》 🍉零、引言 本文完成于2022-07-02 22:22:27。 博主刚开始在瑞芯微ITX-3588J-8K的开发板上跑了官方的yolov5目标检测算法，检测了ip相机rtsp视频流，但是每帧处理需要833ms左右，和放PPT一样。本来想使用tensorrt进行加速推理，但前提需要cuda，rk的板子上都是arm的手机gpu，没有nvidia的cuda，所以不能这样适配。那么转过来，使用开发板自带的NPU进行加速[......] 继续阅读]]></description>
		
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		<title>[转]rk3588使用npu进行模型转换和推理，加速AI应用落地</title>
		<link>https://www.coder4.com/archives/8208</link>
					<comments>https://www.coder4.com/archives/8208#respond</comments>
		
		<dc:creator><![CDATA[coder4]]></dc:creator>
		<pubDate>Wed, 23 Oct 2024 04:11:47 +0000</pubDate>
				<category><![CDATA[Linux]]></category>
		<category><![CDATA[机器学习]]></category>
		<category><![CDATA[npu]]></category>
		<category><![CDATA[rknn]]></category>
		<category><![CDATA[推理]]></category>
		<category><![CDATA[模型]]></category>
		<category><![CDATA[转换]]></category>
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					<description><![CDATA[转载自：《rk3588使用npu进行模型转换和推理，加速AI应用落地》 🍉零、引言 博主在瑞芯微RK3588的开发板上跑了deepsort跟踪算法，从IP相机中的server拉取rtsp视频流，但是fps只有1.2，和放PPT一样卡顿，无法投入实际应用。本来想使用tensorrt进行加速推理，但是前提需要cuda，rk的板子上都是Arm的手机gpu，没有Nvidia的cuda，所以这条路行不通。那么转过来，使用开发板自带的NPU进行加速推理，岂不是更加可行，而且它本身就是深度学习嵌入式板子，[......] 继续阅读]]></description>
		
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		<title>RK系列芯片在主流AI模型上的性能对比</title>
		<link>https://www.coder4.com/archives/8152</link>
					<comments>https://www.coder4.com/archives/8152#respond</comments>
		
		<dc:creator><![CDATA[coder4]]></dc:creator>
		<pubDate>Fri, 10 May 2024 07:30:25 +0000</pubDate>
				<category><![CDATA[心情随笔]]></category>
		<category><![CDATA[ai模型]]></category>
		<category><![CDATA[rk]]></category>
		<category><![CDATA[rknn]]></category>
		<category><![CDATA[性能]]></category>
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					<description><![CDATA[来源：https://www.scensmart.com/news/comparison-of-ai-model-performance-of-rockchip-mainstream-socs-such-as-rk3588-rk3576-rk3568-rv1126-etc/ [......] 继续阅读]]></description>
		
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