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		<title>YoloV5-Lite目标检测之“微调 + 模型转换”</title>
		<link>https://www.coder4.com/archives/8201</link>
					<comments>https://www.coder4.com/archives/8201#respond</comments>
		
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		<pubDate>Thu, 12 Sep 2024 06:47:09 +0000</pubDate>
				<category><![CDATA[机器学习]]></category>
		<category><![CDATA[ncnn]]></category>
		<category><![CDATA[YoloV5-Lite]]></category>
		<category><![CDATA[微调]]></category>
		<category><![CDATA[模型转换]]></category>
		<category><![CDATA[自定义数据]]></category>
		<category><![CDATA[训练]]></category>
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					<description><![CDATA[在YoloV5-Lite目标检测之“安装推理”中，我们完成了安装和预训练权重的推理，下面介绍自定义训练数据、模型转换(ncnn) 1 训练数据准备 . ├── train │   ├── 000000000049.jpg │   ├── 000000000049.txt ...... │   ├── 000000581880.txt │   ├── 000000581900.jpg │   └── 000000581900.txt └── val ├── 00000[......] 继续阅读]]></description>
		
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		<title>NanoDet目标检测之&quot;搭建预测篇&quot;</title>
		<link>https://www.coder4.com/archives/8192</link>
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		<dc:creator><![CDATA[coder4]]></dc:creator>
		<pubDate>Wed, 11 Sep 2024 06:47:58 +0000</pubDate>
				<category><![CDATA[机器学习]]></category>
		<category><![CDATA[nanodet]]></category>
		<category><![CDATA[ncnn]]></category>
		<category><![CDATA[搭建]]></category>
		<category><![CDATA[目标检测]]></category>
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					<description><![CDATA[NanoDet是一个基于ShuffleNetV2的轻量级的目标检测模型，配合ncnn框架加速后，在中端Android机型能做到20fps+ 1 安装环境 为了避免冲突，需要安装好conda，Python 3.8+ 安装依赖包 git clone https://github.com/RangiLyu/nanodet.git cd nanodet pip install -r requirements.txta 安装代码 python setup.py develop 2 下[......] 继续阅读]]></description>
		
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