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	<title>深度学习 - 四号程序员</title>
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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>YoloV5-Lite目标检测之“安装推理”</title>
		<link>https://www.coder4.com/archives/8199</link>
					<comments>https://www.coder4.com/archives/8199#respond</comments>
		
		<dc:creator><![CDATA[coder4]]></dc:creator>
		<pubDate>Thu, 12 Sep 2024 04:34:24 +0000</pubDate>
				<category><![CDATA[心情随笔]]></category>
		<category><![CDATA[机器学习]]></category>
		<category><![CDATA[YoloV5-Lite]]></category>
		<category><![CDATA[深度学习]]></category>
		<category><![CDATA[目标检测]]></category>
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					<description><![CDATA[1 安装 conda activate py38 git clone https://github.com/ppogg/YOLOv5-Lite pip install -r requirements.txt 2 下载预训练的权重 预训练权重可以在官网下载，我这里下载的是v5lite-s 3 推理 图片推理 python3 ./detect.py --weights ./weights/v5lite-e.pt --source ././python_demo/openvino/[......] 继续阅读]]></description>
		
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		<title>深度学习模型判断图片相似度</title>
		<link>https://www.coder4.com/archives/8176</link>
					<comments>https://www.coder4.com/archives/8176#respond</comments>
		
		<dc:creator><![CDATA[coder4]]></dc:creator>
		<pubDate>Wed, 19 Jun 2024 03:54:32 +0000</pubDate>
				<category><![CDATA[机器学习]]></category>
		<category><![CDATA[图片相似度]]></category>
		<category><![CDATA[深度学习]]></category>
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					<description><![CDATA[思路：使用模型的特征提取层，转化成向量，然后比对向量的距离(比如cos) import torch import torchvision.models as models import torchvision.transforms as transforms from PIL import Image # load model &#38; feature only model = models.mobilenet_v3_small(pretrained=True) mode[......] 继续阅读]]></description>
		
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		<title>PyTorch学习笔记</title>
		<link>https://www.coder4.com/archives/7999</link>
					<comments>https://www.coder4.com/archives/7999#respond</comments>
		
		<dc:creator><![CDATA[coder4]]></dc:creator>
		<pubDate>Mon, 27 Nov 2023 02:53:29 +0000</pubDate>
				<category><![CDATA[机器学习]]></category>
		<category><![CDATA[pyTorch]]></category>
		<category><![CDATA[分类]]></category>
		<category><![CDATA[深度学习]]></category>
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					<description><![CDATA[基础Tensor(张量)操作 TODO 创建线性(玩具)数据集 import torch from torch import nn import matplotlib.pyplot as plt device = "cuda" if torch.cuda.is_available() else "cpu" print(device) # create data weight = 0.6 bias = 0.4 start = 0 end = 1 ste[......] 继续阅读]]></description>
		
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