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Yolov5WhiteLineDetection / openvino / yolov5s_640x640_opt.xml
@sato sato on 1 Mar 2022 215 KB 最初のコミット
<?xml version="1.0" ?>
<net name="yolov5s_640x640_opt" version="10">
	<layers>
		<layer id="0" name="images" type="Parameter" version="opset1">
			<data shape="1, 3, 640, 640" element_type="f32"/>
			<output>
				<port id="0" precision="FP32" names="images">
					<dim>1</dim>
					<dim>3</dim>
					<dim>640</dim>
					<dim>640</dim>
				</port>
			</output>
		</layer>
		<layer id="1" name="model.0.conv.weight" type="Const" version="opset1">
			<data element_type="f32" shape="32, 3, 6, 6" offset="0" size="13824"/>
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					<dim>32</dim>
					<dim>3</dim>
					<dim>6</dim>
					<dim>6</dim>
				</port>
			</output>
		</layer>
		<layer id="2" name="Conv_0/WithoutBiases" type="Convolution" version="opset1">
			<data strides="2, 2" dilations="1, 1" pads_begin="2, 2" pads_end="2, 2" auto_pad="explicit"/>
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				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>3</dim>
					<dim>640</dim>
					<dim>640</dim>
				</port>
				<port id="1" precision="FP32">
					<dim>32</dim>
					<dim>3</dim>
					<dim>6</dim>
					<dim>6</dim>
				</port>
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			<output>
				<port id="2" precision="FP32">
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					<dim>32</dim>
					<dim>320</dim>
					<dim>320</dim>
				</port>
			</output>
		</layer>
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				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>32</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</output>
		</layer>
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			<data auto_broadcast="numpy"/>
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				<port id="0" precision="FP32">
					<dim>1</dim>
					<dim>32</dim>
					<dim>320</dim>
					<dim>320</dim>
				</port>
				<port id="1" precision="FP32">
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					<dim>32</dim>
					<dim>1</dim>
					<dim>1</dim>
				</port>
			</input>
			<output>
				<port id="2" precision="FP32" names="122">
					<dim>1</dim>
					<dim>32</dim>
					<dim>320</dim>
					<dim>320</dim>
				</port>
			</output>
		</layer>
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					<dim>32</dim>
					<dim>320</dim>
					<dim>320</dim>
				</port>
			</input>
			<output>
				<port id="1" precision="FP32" names="124">
					<dim>1</dim>
					<dim>32</dim>
					<dim>320</dim>
					<dim>320</dim>
				</port>
			</output>
		</layer>
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					<dim>32</dim>
					<dim>3</dim>
					<dim>3</dim>
				</port>
			</output>
		</layer>
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					<dim>1</dim>
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					<dim>320</dim>
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				<port id="1" precision="FP32">
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					<dim>32</dim>
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					<dim>64</dim>
					<dim>160</dim>
					<dim>160</dim>
				</port>
			</output>
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				</port>
			</output>
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					<dim>160</dim>
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			<output>
				<port id="2" precision="FP32">
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					<dim>160</dim>
					<dim>160</dim>
				</port>
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		</layer>
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				</port>
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		</layer>
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			<data auto_broadcast="numpy"/>
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				<port id="0" precision="FP32">
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				<port id="2" precision="FP32" names="128">
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