diff --git a/resources/models/intel/person-detection-retail-0013/FP32/person-detection-retail-0013.bin b/resources/models/intel/person-detection-retail-0013/FP32/person-detection-retail-0013.bin
new file mode 100644
index 0000000..4719f58
--- /dev/null
+++ b/resources/models/intel/person-detection-retail-0013/FP32/person-detection-retail-0013.bin
Binary files differ
diff --git a/resources/models/intel/person-detection-retail-0013/FP32/person-detection-retail-0013.xml b/resources/models/intel/person-detection-retail-0013/FP32/person-detection-retail-0013.xml
new file mode 100644
index 0000000..8be2b9f
--- /dev/null
+++ b/resources/models/intel/person-detection-retail-0013/FP32/person-detection-retail-0013.xml
@@ -0,0 +1,13694 @@
+<?xml version="1.0" ?>
+<net name="ResMobNet_v4 (LReLU) with single SSD head" version="10">
+	<layers>
+		<layer id="0" name="data" type="Parameter" version="opset1">
+			<data element_type="f32" shape="1, 3, 320, 544"/>
+			<output>
+				<port id="0" names="data" precision="FP32">
+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>320</dim>
+					<dim>544</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="1" name="data_mul_23644" type="Const" version="opset1">
+			<data element_type="f32" offset="0" shape="1, 3, 1, 1" size="12"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="2" name="data/norm/bn/mean/Fused_Mul_" type="Multiply" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>320</dim>
+					<dim>544</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>320</dim>
+					<dim>544</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="3" name="data_add_23646" type="Const" version="opset1">
+			<data element_type="f32" offset="12" shape="1, 3, 1, 1" size="12"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="4" name="data/norm/bn/variance/Fused_Add_" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>320</dim>
+					<dim>544</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="data/norm/bn" precision="FP32">
+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>320</dim>
+					<dim>544</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="5" name="init_block1/dim_inc/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f32" offset="24" shape="32, 3, 3, 3" size="3456"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>32</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="6" name="init_block1/dim_inc/conv" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" strides="2, 2"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>320</dim>
+					<dim>544</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>32</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>160</dim>
+					<dim>272</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="7" name="data_add_2364923654" type="Const" version="opset1">
+			<data element_type="f32" offset="3480" shape="1, 32, 1, 1" size="128"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="8" name="init_block1/dim_inc/bn/variance/Fused_Add_" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>160</dim>
+					<dim>272</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="init_block1/dim_inc/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>160</dim>
+					<dim>272</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="9" name="init_block1/dim_inc/fn" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>160</dim>
+					<dim>272</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" names="init_block1/dim_inc/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>160</dim>
+					<dim>272</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="10" name="bottleneck1_1/dim_red/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f32" offset="3608" shape="8, 32, 1, 1" size="1024"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>8</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="11" name="bottleneck1_1/dim_red/conv" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>160</dim>
+					<dim>272</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>8</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>8</dim>
+					<dim>160</dim>
+					<dim>272</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="12" name="data_add_2365723662" type="Const" version="opset1">
+			<data element_type="f32" offset="4632" shape="1, 8, 1, 1" size="32"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>8</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="13" name="bottleneck1_1/dim_red/bn/variance/Fused_Add_" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>8</dim>
+					<dim>160</dim>
+					<dim>272</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>8</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="bottleneck1_1/dim_red/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>8</dim>
+					<dim>160</dim>
+					<dim>272</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="14" name="bottleneck1_1/dim_red/fn/weights3096039785" type="Const" version="opset1">
+			<data element_type="f32" offset="4664" shape="1" size="4"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="15" name="bottleneck1_1/dim_red/fn" type="PReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>8</dim>
+					<dim>160</dim>
+					<dim>272</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="bottleneck1_1/dim_red/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>8</dim>
+					<dim>160</dim>
+					<dim>272</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="16" name="bottleneck1_1/inner/dw1/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f32" offset="4668" shape="8, 1, 1, 3, 3" size="288"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>8</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="17" name="bottleneck1_1/inner/dw1/conv" type="GroupConvolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>8</dim>
+					<dim>160</dim>
+					<dim>272</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>8</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>8</dim>
+					<dim>160</dim>
+					<dim>272</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="18" name="data_add_2366523670" type="Const" version="opset1">
+			<data element_type="f32" offset="4956" shape="1, 8, 1, 1" size="32"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>8</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="19" name="bottleneck1_1/inner/dw1/bn/variance/Fused_Add_" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>8</dim>
+					<dim>160</dim>
+					<dim>272</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>8</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="bottleneck1_1/inner/dw1/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>8</dim>
+					<dim>160</dim>
+					<dim>272</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="20" name="bottleneck1_1/inner/dw1/fn/weights3102439659" type="Const" version="opset1">
+			<data element_type="f32" offset="4664" shape="1" size="4"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="21" name="bottleneck1_1/inner/dw1/fn" type="PReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>8</dim>
+					<dim>160</dim>
+					<dim>272</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="bottleneck1_1/inner/dw1/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>8</dim>
+					<dim>160</dim>
+					<dim>272</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="22" name="bottleneck1_1/dim_inc/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f32" offset="4988" shape="32, 8, 1, 1" size="1024"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>32</dim>
+					<dim>8</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="23" name="bottleneck1_1/dim_inc/conv" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>8</dim>
+					<dim>160</dim>
+					<dim>272</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>32</dim>
+					<dim>8</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>160</dim>
+					<dim>272</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="24" name="data_add_2367323678" type="Const" version="opset1">
+			<data element_type="f32" offset="6012" shape="1, 32, 1, 1" size="128"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="25" name="bottleneck1_1/dim_inc/bn/variance/Fused_Add_" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>160</dim>
+					<dim>272</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="bottleneck1_1/dim_inc/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>160</dim>
+					<dim>272</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="26" name="bottleneck1_1/add" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>160</dim>
+					<dim>272</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>160</dim>
+					<dim>272</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="bottleneck1_1/add" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>160</dim>
+					<dim>272</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="27" name="bottleneck1_1/fn/weights3115239677" type="Const" version="opset1">
+			<data element_type="f32" offset="4664" shape="1" size="4"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="28" name="bottleneck1_1/fn" type="PReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>160</dim>
+					<dim>272</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="bottleneck1_1/add" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>160</dim>
+					<dim>272</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="29" name="bottleneck1_2/dim_red/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f32" offset="6140" shape="8, 32, 1, 1" size="1024"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>8</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="30" name="bottleneck1_2/dim_red/conv" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>160</dim>
+					<dim>272</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>8</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>8</dim>
+					<dim>160</dim>
+					<dim>272</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="31" name="data_add_2368123686" type="Const" version="opset1">
+			<data element_type="f32" offset="7164" shape="1, 8, 1, 1" size="32"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>8</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="32" name="bottleneck1_2/dim_red/bn/variance/Fused_Add_" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>8</dim>
+					<dim>160</dim>
+					<dim>272</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>8</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="bottleneck1_2/dim_red/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>8</dim>
+					<dim>160</dim>
+					<dim>272</dim>
+				</port>
+			</output>
+		</layer>
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+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+		</layer>
+		<layer id="34" name="bottleneck1_2/dim_red/fn" type="PReLU" version="opset1">
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+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+				<port id="1" precision="FP32">
+					<dim>1</dim>
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+			<output>
+				<port id="2" names="bottleneck1_2/dim_red/conv" precision="FP32">
+					<dim>1</dim>
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+					<dim>272</dim>
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+			</output>
+		</layer>
+		<layer id="35" name="bottleneck1_2/inner/dw1/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
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+					<dim>3</dim>
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+		</layer>
+		<layer id="36" name="bottleneck1_2/inner/dw1/conv" type="GroupConvolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" strides="1, 1"/>
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+			<data auto_broadcast="numpy"/>
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+				<port id="0" precision="FP32">
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+		<layer id="39" name="bottleneck1_2/inner/dw1/fn/weights3087240085" type="Const" version="opset1">
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+		<layer id="41" name="bottleneck1_2/dim_inc/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
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+		</layer>
+		<layer id="42" name="bottleneck1_2/dim_inc/conv" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
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+			<data auto_broadcast="numpy"/>
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+				<port id="0" precision="FP32">
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+					<dim>160</dim>
+					<dim>272</dim>
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+				<port id="0" precision="FP32">
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+		<layer id="46" name="bottleneck1_2/fn/weights3090439737" type="Const" version="opset1">
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+				<port id="0" precision="FP32">
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+		<layer id="49" name="bottleneck1_3/dim_red/conv" type="Convolution" version="opset1">
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+					<dim>272</dim>
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+		<layer id="51" name="bottleneck1_3/dim_red/bn/variance/Fused_Add_" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
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+					<dim>1</dim>
+					<dim>8</dim>
+					<dim>160</dim>
+					<dim>272</dim>
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+				<port id="0" precision="FP32">
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+				<port id="1" precision="FP32">
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+			<output>
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+					<dim>160</dim>
+					<dim>272</dim>
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+		<layer id="55" name="bottleneck1_3/inner/dw1/conv" type="GroupConvolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" strides="1, 1"/>
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+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
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+					<dim>1</dim>
+					<dim>8</dim>
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+					<dim>272</dim>
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+		<layer id="58" name="bottleneck1_3/inner/dw1/fn/weights3115640004" type="Const" version="opset1">
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+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+				<port id="1" precision="FP32">
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+					<dim>1</dim>
+					<dim>8</dim>
+					<dim>160</dim>
+					<dim>272</dim>
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+		<layer id="60" name="bottleneck1_3/dim_inc/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f32" offset="10044" shape="32, 8, 1, 1" size="1024"/>
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+					<dim>1</dim>
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+		<layer id="61" name="bottleneck1_3/dim_inc/conv" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
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+			<data auto_broadcast="numpy"/>
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+				<port id="0" precision="FP32">
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+					<dim>160</dim>
+					<dim>272</dim>
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+			</output>
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+			<data auto_broadcast="numpy"/>
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+				<port id="0" precision="FP32">
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+		<layer id="65" name="bottleneck1_3/fn/weights3092439836" type="Const" version="opset1">
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+				<port id="0" precision="FP32">
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+				<port id="1" precision="FP32">
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+			<output>
+				<port id="2" names="bottleneck1_3/add" precision="FP32">
+					<dim>1</dim>
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+					<dim>272</dim>
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+			</output>
+		</layer>
+		<layer id="67" name="bottleneck1_4/dim_red/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f32" offset="11196" shape="8, 32, 1, 1" size="1024"/>
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+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="68" name="bottleneck1_4/dim_red/conv" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
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+					<dim>272</dim>
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+			</output>
+		</layer>
+		<layer id="69" name="data_add_2372923734" type="Const" version="opset1">
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+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="70" name="bottleneck1_4/dim_red/bn/variance/Fused_Add_" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
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+					<dim>1</dim>
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+			<output>
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+					<dim>1</dim>
+					<dim>8</dim>
+					<dim>160</dim>
+					<dim>272</dim>
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+			</output>
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+		<layer id="71" name="bottleneck1_4/dim_red/fn/weights3114840622" type="Const" version="opset1">
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+		<layer id="72" name="bottleneck1_4/dim_red/fn" type="PReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
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+				<port id="1" precision="FP32">
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+					<dim>272</dim>
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+		<layer id="73" name="bottleneck1_4/inner/dw1/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
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+		</layer>
+		<layer id="74" name="bottleneck1_4/inner/dw1/conv" type="GroupConvolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" strides="1, 1"/>
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+		<layer id="75" name="data_add_2373723742" type="Const" version="opset1">
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+		<layer id="76" name="bottleneck1_4/inner/dw1/bn/variance/Fused_Add_" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
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+				<port id="0" precision="FP32">
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+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+					<dim>1</dim>
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+					<dim>272</dim>
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+		<layer id="79" name="bottleneck1_4/dim_inc/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
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+					<dim>1</dim>
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+		<layer id="80" name="bottleneck1_4/dim_inc/conv" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
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+				<port id="0" precision="FP32">
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+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
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+					<dim>160</dim>
+					<dim>272</dim>
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+			<data auto_broadcast="numpy"/>
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+				<port id="0" precision="FP32">
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+		<layer id="84" name="bottleneck1_4/fn/weights3087639962" type="Const" version="opset1">
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+			<output>
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+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+			<data auto_pad="explicit" kernel="2, 2" pads_begin="0, 0" pads_end="0, 0" rounding_type="ceil" strides="2, 2"/>
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+		<layer id="90" name="bottleneck2_0/skip/bn/variance/Fused_Add_" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
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+					<dim>1</dim>
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+			<data auto_broadcast="numpy"/>
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+				<port id="0" precision="FP32">
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+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
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+				<port id="2" names="bottleneck2_0/inner/dw1/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>80</dim>
+					<dim>136</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="101" name="bottleneck2_0/inner/dw1/fn/weights3088840568" type="Const" version="opset1">
+			<data element_type="f32" offset="4664" shape="1" size="4"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="102" name="bottleneck2_0/inner/dw1/fn" type="PReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+				<port id="1" precision="FP32">
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="bottleneck2_0/inner/dw1/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>80</dim>
+					<dim>136</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="103" name="bottleneck2_0/dim_inc/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f32" offset="24924" shape="64, 16, 1, 1" size="4096"/>
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+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="104" name="bottleneck2_0/dim_inc/conv" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP32">
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+					<dim>1</dim>
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+					<dim>1</dim>
+					<dim>64</dim>
+					<dim>80</dim>
+					<dim>136</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="105" name="data_add_2377723782" type="Const" version="opset1">
+			<data element_type="f32" offset="29020" shape="1, 64, 1, 1" size="256"/>
+			<output>
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+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="106" name="bottleneck2_0/dim_inc/bn/variance/Fused_Add_" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
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+					<dim>1</dim>
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+			</input>
+			<output>
+				<port id="2" names="bottleneck2_0/dim_inc/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>64</dim>
+					<dim>80</dim>
+					<dim>136</dim>
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+			</output>
+		</layer>
+		<layer id="107" name="bottleneck2_0/add" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
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+			</input>
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+				<port id="2" names="bottleneck2_0/add" precision="FP32">
+					<dim>1</dim>
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+					<dim>136</dim>
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+		</layer>
+		<layer id="108" name="bottleneck2_0/fn/weights3086440226" type="Const" version="opset1">
+			<data element_type="f32" offset="4664" shape="1" size="4"/>
+			<output>
+				<port id="0" precision="FP32">
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+		</layer>
+		<layer id="109" name="bottleneck2_0/fn" type="PReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+				<port id="1" precision="FP32">
+					<dim>1</dim>
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+			</input>
+			<output>
+				<port id="2" names="bottleneck2_0/add" precision="FP32">
+					<dim>1</dim>
+					<dim>64</dim>
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+			</output>
+		</layer>
+		<layer id="110" name="bottleneck2_1/dim_red/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f32" offset="29276" shape="16, 64, 1, 1" size="4096"/>
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+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="111" name="bottleneck2_1/dim_red/conv" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP32">
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+					<dim>1</dim>
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+				<port id="2" precision="FP32">
+					<dim>1</dim>
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+					<dim>80</dim>
+					<dim>136</dim>
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+		</layer>
+		<layer id="112" name="data_add_2378523790" type="Const" version="opset1">
+			<data element_type="f32" offset="33372" shape="1, 16, 1, 1" size="64"/>
+			<output>
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+					<dim>1</dim>
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+			</output>
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+		<layer id="113" name="bottleneck2_1/dim_red/bn/variance/Fused_Add_" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>80</dim>
+					<dim>136</dim>
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+			<data element_type="f32" offset="4664" shape="1" size="4"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+		<layer id="115" name="bottleneck2_1/dim_red/fn" type="PReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+				<port id="1" precision="FP32">
+					<dim>1</dim>
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+			<output>
+				<port id="2" names="bottleneck2_1/dim_red/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>80</dim>
+					<dim>136</dim>
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+			<data element_type="f32" offset="33436" shape="16, 1, 1, 3, 3" size="576"/>
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+					<dim>3</dim>
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+		</layer>
+		<layer id="117" name="bottleneck2_1/inner/dw1/conv" type="GroupConvolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" strides="1, 1"/>
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+				<port id="0" precision="FP32">
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+			<output>
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+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
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+					<dim>1</dim>
+					<dim>16</dim>
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+					<dim>136</dim>
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+		<layer id="120" name="bottleneck2_1/inner/dw1/fn/weights3110039803" type="Const" version="opset1">
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+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+				<port id="1" precision="FP32">
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+					<dim>1</dim>
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+					<dim>136</dim>
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+			<data element_type="f32" offset="34076" shape="64, 16, 1, 1" size="4096"/>
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+					<dim>1</dim>
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+		<layer id="123" name="bottleneck2_1/dim_inc/conv" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP32">
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+					<dim>1</dim>
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+					<dim>1</dim>
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+			<data element_type="f32" offset="38172" shape="1, 64, 1, 1" size="256"/>
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+			<data auto_broadcast="numpy"/>
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+				<port id="0" precision="FP32">
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+					<dim>1</dim>
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+			<output>
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+					<dim>1</dim>
+					<dim>64</dim>
+					<dim>80</dim>
+					<dim>136</dim>
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+			</output>
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+			<data auto_broadcast="numpy"/>
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+				<port id="0" precision="FP32">
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+					<dim>1</dim>
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+		<layer id="127" name="bottleneck2_1/fn/weights3081640076" type="Const" version="opset1">
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+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+				<port id="1" precision="FP32">
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+					<dim>1</dim>
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+			<data element_type="f32" offset="38428" shape="16, 64, 1, 1" size="4096"/>
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+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
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+			<data auto_broadcast="numpy"/>
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+				<port id="0" precision="FP32">
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+					<dim>1</dim>
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+					<dim>80</dim>
+					<dim>136</dim>
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+			<input>
+				<port id="0" precision="FP32">
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+				<port id="1" precision="FP32">
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+			<output>
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+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>80</dim>
+					<dim>136</dim>
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+			<data auto_broadcast="numpy"/>
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+			<data auto_broadcast="numpy"/>
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+			<data auto_broadcast="numpy"/>
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+			<data auto_broadcast="numpy"/>
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+				<port id="0" precision="FP32">
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+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
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+			<data auto_broadcast="numpy"/>
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+					<dim>1</dim>
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+					<dim>80</dim>
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+				</port>
+			</output>
+		</layer>
+		<layer id="203" name="bottleneck2_5/fn/weights3085239683" type="Const" version="opset1">
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+			<output>
+				<port id="0" precision="FP32">
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+		<layer id="204" name="bottleneck2_5/fn" type="PReLU" version="opset1">
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+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+				<port id="2" names="bottleneck2_5/add" precision="FP32">
+					<dim>1</dim>
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+					<dim>136</dim>
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+			</output>
+		</layer>
+		<layer id="205" name="bottleneck2_6/dim_red/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f32" offset="75036" shape="16, 64, 1, 1" size="4096"/>
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+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="206" name="bottleneck2_6/dim_red/conv" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
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+				<port id="0" precision="FP32">
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+					<dim>1</dim>
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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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+					<dim>1</dim>
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+					<dim>80</dim>
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+			</output>
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+				<port id="0" precision="FP32">
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+		</layer>
+		<layer id="212" name="bottleneck2_6/inner/dw1/conv" type="GroupConvolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" strides="1, 1"/>
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+				<port id="0" precision="FP32">
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+		<layer id="214" name="bottleneck2_6/inner/dw1/bn/variance/Fused_Add_" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
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+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+				<port id="1" precision="FP32">
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+					<dim>136</dim>
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+		<layer id="217" name="bottleneck2_6/dim_inc/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f32" offset="79836" shape="64, 16, 1, 1" size="4096"/>
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+					<dim>1</dim>
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+		<layer id="218" name="bottleneck2_6/dim_inc/conv" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
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+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
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+			<output>
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+					<dim>1</dim>
+					<dim>64</dim>
+					<dim>80</dim>
+					<dim>136</dim>
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+			<data auto_broadcast="numpy"/>
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+				<port id="0" precision="FP32">
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+					<dim>80</dim>
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+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+		<layer id="224" name="bottleneck2_7/dim_red/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
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+		<layer id="225" name="bottleneck2_7/dim_red/conv" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
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+					<dim>136</dim>
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+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
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+					<dim>1</dim>
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+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>80</dim>
+					<dim>136</dim>
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+				<port id="0" precision="FP32">
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+				<port id="1" precision="FP32">
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+			<output>
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+					<dim>16</dim>
+					<dim>80</dim>
+					<dim>136</dim>
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+			<data auto_pad="explicit" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" strides="1, 1"/>
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+			<data auto_broadcast="numpy"/>
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+				<port id="0" precision="FP32">
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+					<dim>136</dim>
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+				<port id="0" precision="FP32">
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+					<dim>16</dim>
+					<dim>80</dim>
+					<dim>136</dim>
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+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
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+			<data auto_broadcast="numpy"/>
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+			<data auto_broadcast="numpy"/>
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+				<port id="2" names="bottleneck3_0/dim_red/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>80</dim>
+					<dim>136</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="271" name="bottleneck3_0/dim_red/fn/weights3097640670" type="Const" version="opset1">
+			<data element_type="f32" offset="4664" shape="1" size="4"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="272" name="bottleneck3_0/dim_red/fn" type="PReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>80</dim>
+					<dim>136</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="bottleneck3_0/dim_red/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>80</dim>
+					<dim>136</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="273" name="bottleneck3_0/inner/dw1/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f32" offset="144092" shape="32, 1, 1, 3, 3" size="1152"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="274" name="bottleneck3_0/inner/dw1/conv" type="GroupConvolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" strides="2, 2"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>80</dim>
+					<dim>136</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="275" name="data_add_2399323998" type="Const" version="opset1">
+			<data element_type="f32" offset="145244" shape="1, 32, 1, 1" size="128"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="276" name="bottleneck3_0/inner/dw1/bn/variance/Fused_Add_" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="bottleneck3_0/inner/dw1/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="277" name="bottleneck3_0/inner/dw1/fn/weights3079640607" type="Const" version="opset1">
+			<data element_type="f32" offset="4664" shape="1" size="4"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="278" name="bottleneck3_0/inner/dw1/fn" type="PReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
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+				<port id="1" precision="FP32">
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="bottleneck3_0/inner/dw1/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="279" name="bottleneck3_0/dim_inc/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f32" offset="145372" shape="128, 32, 1, 1" size="16384"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>128</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="280" name="bottleneck3_0/dim_inc/conv" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>128</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
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+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="281" name="data_add_2400124006" type="Const" version="opset1">
+			<data element_type="f32" offset="161756" shape="1, 128, 1, 1" size="512"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="282" name="bottleneck3_0/dim_inc/bn/variance/Fused_Add_" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="bottleneck3_0/dim_inc/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="283" name="bottleneck3_0/add" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="bottleneck3_0/add" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="284" name="bottleneck3_0/fn/weights3080840268" type="Const" version="opset1">
+			<data element_type="f32" offset="4664" shape="1" size="4"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="285" name="bottleneck3_0/fn" type="PReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="bottleneck3_0/add" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="286" name="bottleneck3_1/dim_red/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f32" offset="162268" shape="32, 128, 1, 1" size="16384"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>32</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="287" name="bottleneck3_1/dim_red/conv" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>32</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
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+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="288" name="data_add_2400924014" type="Const" version="opset1">
+			<data element_type="f32" offset="178652" shape="1, 32, 1, 1" size="128"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="289" name="bottleneck3_1/dim_red/bn/variance/Fused_Add_" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="bottleneck3_1/dim_red/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="290" name="bottleneck3_1/dim_red/fn/weights3102040538" type="Const" version="opset1">
+			<data element_type="f32" offset="4664" shape="1" size="4"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="291" name="bottleneck3_1/dim_red/fn" type="PReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="bottleneck3_1/dim_red/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="292" name="bottleneck3_1/inner/dw1/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f32" offset="178780" shape="32, 1, 1, 3, 3" size="1152"/>
+			<output>
+				<port id="0" precision="FP32">
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+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="293" name="bottleneck3_1/inner/dw1/conv" type="GroupConvolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
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+				<port id="1" precision="FP32">
+					<dim>32</dim>
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+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>3</dim>
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+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="294" name="data_add_2401724022" type="Const" version="opset1">
+			<data element_type="f32" offset="179932" shape="1, 32, 1, 1" size="128"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="295" name="bottleneck3_1/inner/dw1/bn/variance/Fused_Add_" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="bottleneck3_1/inner/dw1/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="296" name="bottleneck3_1/inner/dw1/fn/weights3082440517" type="Const" version="opset1">
+			<data element_type="f32" offset="4664" shape="1" size="4"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="297" name="bottleneck3_1/inner/dw1/fn" type="PReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="bottleneck3_1/inner/dw1/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="298" name="bottleneck3_1/dim_inc/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f32" offset="180060" shape="128, 32, 1, 1" size="16384"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>128</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="299" name="bottleneck3_1/dim_inc/conv" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>128</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
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+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="300" name="data_add_2402524030" type="Const" version="opset1">
+			<data element_type="f32" offset="196444" shape="1, 128, 1, 1" size="512"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="301" name="bottleneck3_1/dim_inc/bn/variance/Fused_Add_" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="bottleneck3_1/dim_inc/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="302" name="bottleneck3_1/add" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="bottleneck3_1/add" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="303" name="bottleneck3_1/fn/weights3086039869" type="Const" version="opset1">
+			<data element_type="f32" offset="4664" shape="1" size="4"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="304" name="bottleneck3_1/fn" type="PReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="bottleneck3_1/add" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="305" name="bottleneck3_2/dim_red/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f32" offset="196956" shape="32, 128, 1, 1" size="16384"/>
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+				<port id="0" precision="FP32">
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+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="306" name="bottleneck3_2/dim_red/conv" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+					<dim>68</dim>
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+				<port id="1" precision="FP32">
+					<dim>32</dim>
+					<dim>128</dim>
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+					<dim>1</dim>
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+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="307" name="data_add_2403324038" type="Const" version="opset1">
+			<data element_type="f32" offset="213340" shape="1, 32, 1, 1" size="128"/>
+			<output>
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+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="308" name="bottleneck3_2/dim_red/bn/variance/Fused_Add_" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+				<port id="1" precision="FP32">
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+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="bottleneck3_2/dim_red/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="309" name="bottleneck3_2/dim_red/fn/weights3117639980" type="Const" version="opset1">
+			<data element_type="f32" offset="4664" shape="1" size="4"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="310" name="bottleneck3_2/dim_red/fn" type="PReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+				<port id="1" precision="FP32">
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="bottleneck3_2/dim_red/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="311" name="bottleneck3_2/inner/dw1/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f32" offset="213468" shape="32, 1, 1, 3, 3" size="1152"/>
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+					<dim>3</dim>
+					<dim>3</dim>
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+			</output>
+		</layer>
+		<layer id="312" name="bottleneck3_2/inner/dw1/conv" type="GroupConvolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP32">
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+				<port id="2" precision="FP32">
+					<dim>1</dim>
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+					<dim>68</dim>
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+			</output>
+		</layer>
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+					<dim>1</dim>
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+		</layer>
+		<layer id="314" name="bottleneck3_2/inner/dw1/bn/variance/Fused_Add_" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+					<dim>1</dim>
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+				<port id="2" names="bottleneck3_2/inner/dw1/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
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+			</output>
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+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+			</output>
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+		<layer id="316" name="bottleneck3_2/inner/dw1/fn" type="PReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+				<port id="1" precision="FP32">
+					<dim>1</dim>
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+			</input>
+			<output>
+				<port id="2" names="bottleneck3_2/inner/dw1/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
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+			</output>
+		</layer>
+		<layer id="317" name="bottleneck3_2/dim_inc/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f32" offset="214748" shape="128, 32, 1, 1" size="16384"/>
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+					<dim>128</dim>
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+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="318" name="bottleneck3_2/dim_inc/conv" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP32">
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+					<dim>1</dim>
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+					<dim>1</dim>
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+					<dim>40</dim>
+					<dim>68</dim>
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+			<output>
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+					<dim>1</dim>
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+		<layer id="320" name="bottleneck3_2/dim_inc/bn/variance/Fused_Add_" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+			<output>
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+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
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+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
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+					<dim>68</dim>
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+			</input>
+			<output>
+				<port id="2" names="bottleneck3_2/add" precision="FP32">
+					<dim>1</dim>
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+					<dim>40</dim>
+					<dim>68</dim>
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+			</output>
+		</layer>
+		<layer id="322" name="bottleneck3_2/fn/weights3093640130" type="Const" version="opset1">
+			<data element_type="f32" offset="4664" shape="1" size="4"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+		</layer>
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+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+				<port id="1" precision="FP32">
+					<dim>1</dim>
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+			</input>
+			<output>
+				<port id="2" names="bottleneck3_2/add" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
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+			</output>
+		</layer>
+		<layer id="324" name="bottleneck3_3/dim_red/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f32" offset="231644" shape="32, 128, 1, 1" size="16384"/>
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+					<dim>1</dim>
+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="325" name="bottleneck3_3/dim_red/conv" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP32">
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+				<port id="2" precision="FP32">
+					<dim>1</dim>
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+					<dim>68</dim>
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+			</output>
+		</layer>
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+			<data element_type="f32" offset="248028" shape="1, 32, 1, 1" size="128"/>
+			<output>
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+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="327" name="bottleneck3_3/dim_red/bn/variance/Fused_Add_" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+					<dim>40</dim>
+					<dim>68</dim>
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+				<port id="1" precision="FP32">
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+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
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+			</input>
+			<output>
+				<port id="2" names="bottleneck3_3/dim_red/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
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+			</output>
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+		<layer id="328" name="bottleneck3_3/dim_red/fn/weights3107239758" type="Const" version="opset1">
+			<data element_type="f32" offset="4664" shape="1" size="4"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+		<layer id="329" name="bottleneck3_3/dim_red/fn" type="PReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+				<port id="1" precision="FP32">
+					<dim>1</dim>
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+			<output>
+				<port id="2" names="bottleneck3_3/dim_red/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
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+			<data element_type="f32" offset="248156" shape="32, 1, 1, 3, 3" size="1152"/>
+			<output>
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+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>3</dim>
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+		</layer>
+		<layer id="331" name="bottleneck3_3/inner/dw1/conv" type="GroupConvolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP32">
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+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
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+			</output>
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+			<output>
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+		<layer id="333" name="bottleneck3_3/inner/dw1/bn/variance/Fused_Add_" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
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+			</output>
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+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+				<port id="1" precision="FP32">
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+			<output>
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+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
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+		<layer id="336" name="bottleneck3_3/dim_inc/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f32" offset="249436" shape="128, 32, 1, 1" size="16384"/>
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+					<dim>1</dim>
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+			</output>
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+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP32">
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+					<dim>1</dim>
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+					<dim>40</dim>
+					<dim>68</dim>
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+					<dim>1</dim>
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+			</output>
+		</layer>
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+			<data auto_broadcast="numpy"/>
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+				<port id="0" precision="FP32">
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+					<dim>1</dim>
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+			</input>
+			<output>
+				<port id="2" names="bottleneck3_3/dim_inc/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
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+			<data auto_broadcast="numpy"/>
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+				<port id="0" precision="FP32">
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+			</output>
+		</layer>
+		<layer id="341" name="bottleneck3_3/fn/weights3083640325" type="Const" version="opset1">
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+			<output>
+				<port id="0" precision="FP32">
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+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+		</layer>
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+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
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+			<data auto_broadcast="numpy"/>
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+				<port id="0" precision="FP32">
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+				<port id="0" precision="FP32">
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+			<output>
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+			<data auto_broadcast="numpy"/>
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+				<port id="0" precision="FP32">
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+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
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+			<data auto_broadcast="numpy"/>
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+				<port id="0" precision="FP32">
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+					<dim>68</dim>
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+			<data auto_broadcast="numpy"/>
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+				<port id="0" precision="FP32">
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+			<data auto_broadcast="numpy"/>
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+				<port id="0" precision="FP32">
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+			</output>
+		</layer>
+		<layer id="373" name="bottleneck3_5/inner/dw1/fn" type="PReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="bottleneck3_5/inner/dw1/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="374" name="bottleneck3_5/dim_inc/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f32" offset="318812" shape="128, 32, 1, 1" size="16384"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>128</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="375" name="bottleneck3_5/dim_inc/conv" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>128</dim>
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+					<dim>1</dim>
+					<dim>1</dim>
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+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="376" name="data_add_2412124126" type="Const" version="opset1">
+			<data element_type="f32" offset="335196" shape="1, 128, 1, 1" size="512"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="377" name="bottleneck3_5/dim_inc/bn/variance/Fused_Add_" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+				<port id="1" precision="FP32">
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+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="bottleneck3_5/dim_inc/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="378" name="bottleneck3_5/add" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="bottleneck3_5/add" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="379" name="bottleneck3_5/fn/weights3108439911" type="Const" version="opset1">
+			<data element_type="f32" offset="4664" shape="1" size="4"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="380" name="bottleneck3_5/fn" type="PReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
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+				<port id="1" precision="FP32">
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="bottleneck3_5/add" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="381" name="bottleneck3_6/dim_red/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f32" offset="335708" shape="32, 128, 1, 1" size="16384"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>32</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="382" name="bottleneck3_6/dim_red/conv" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>32</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="383" name="data_add_2412924134" type="Const" version="opset1">
+			<data element_type="f32" offset="352092" shape="1, 32, 1, 1" size="128"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="384" name="bottleneck3_6/dim_red/bn/variance/Fused_Add_" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+					<dim>40</dim>
+					<dim>68</dim>
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+				<port id="1" precision="FP32">
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+					<dim>1</dim>
+					<dim>1</dim>
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+			</input>
+			<output>
+				<port id="2" names="bottleneck3_6/dim_red/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="385" name="bottleneck3_6/dim_red/fn/weights3084040604" type="Const" version="opset1">
+			<data element_type="f32" offset="4664" shape="1" size="4"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="386" name="bottleneck3_6/dim_red/fn" type="PReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+					<dim>40</dim>
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+				<port id="1" precision="FP32">
+					<dim>1</dim>
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+			</input>
+			<output>
+				<port id="2" names="bottleneck3_6/dim_red/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="387" name="bottleneck3_6/inner/dw1/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f32" offset="352220" shape="32, 1, 1, 3, 3" size="1152"/>
+			<output>
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+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>3</dim>
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+			</output>
+		</layer>
+		<layer id="388" name="bottleneck3_6/inner/dw1/conv" type="GroupConvolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP32">
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+					<dim>68</dim>
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+				<port id="1" precision="FP32">
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+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>3</dim>
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+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="389" name="data_add_2413724142" type="Const" version="opset1">
+			<data element_type="f32" offset="353372" shape="1, 32, 1, 1" size="128"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="390" name="bottleneck3_6/inner/dw1/bn/variance/Fused_Add_" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
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+				<port id="1" precision="FP32">
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+					<dim>1</dim>
+					<dim>1</dim>
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+			</input>
+			<output>
+				<port id="2" names="bottleneck3_6/inner/dw1/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="391" name="bottleneck3_6/inner/dw1/fn/weights3090840310" type="Const" version="opset1">
+			<data element_type="f32" offset="4664" shape="1" size="4"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="392" name="bottleneck3_6/inner/dw1/fn" type="PReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
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+					<dim>68</dim>
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+				<port id="1" precision="FP32">
+					<dim>1</dim>
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+			</input>
+			<output>
+				<port id="2" names="bottleneck3_6/inner/dw1/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="393" name="bottleneck3_6/dim_inc/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f32" offset="353500" shape="128, 32, 1, 1" size="16384"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>128</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="394" name="bottleneck3_6/dim_inc/conv" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+					<dim>40</dim>
+					<dim>68</dim>
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+				<port id="1" precision="FP32">
+					<dim>128</dim>
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+					<dim>1</dim>
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+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="395" name="data_add_2414524150" type="Const" version="opset1">
+			<data element_type="f32" offset="369884" shape="1, 128, 1, 1" size="512"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="396" name="bottleneck3_6/dim_inc/bn/variance/Fused_Add_" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="bottleneck3_6/dim_inc/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="397" name="bottleneck3_6/add" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
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+				<port id="1" precision="FP32">
+					<dim>1</dim>
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+					<dim>40</dim>
+					<dim>68</dim>
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+			</input>
+			<output>
+				<port id="2" names="bottleneck3_6/add" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="398" name="bottleneck3_6/fn/weights3090039914" type="Const" version="opset1">
+			<data element_type="f32" offset="4664" shape="1" size="4"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="399" name="bottleneck3_6/fn" type="PReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="bottleneck3_6/add" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="400" name="bottleneck3_7/dim_red/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f32" offset="370396" shape="32, 128, 1, 1" size="16384"/>
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+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="401" name="bottleneck3_7/dim_red/conv" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP32">
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+					<dim>68</dim>
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+				<port id="1" precision="FP32">
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+					<dim>128</dim>
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+					<dim>1</dim>
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+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
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+			</output>
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+			<data element_type="f32" offset="386780" shape="1, 32, 1, 1" size="128"/>
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+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="403" name="bottleneck3_7/dim_red/bn/variance/Fused_Add_" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
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+					<dim>68</dim>
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+				<port id="1" precision="FP32">
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+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="bottleneck3_7/dim_red/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="404" name="bottleneck3_7/dim_red/fn/weights3113640679" type="Const" version="opset1">
+			<data element_type="f32" offset="4664" shape="1" size="4"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="405" name="bottleneck3_7/dim_red/fn" type="PReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="bottleneck3_7/dim_red/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="406" name="bottleneck3_7/inner/dw1/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f32" offset="386908" shape="32, 1, 1, 3, 3" size="1152"/>
+			<output>
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+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>3</dim>
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+			</output>
+		</layer>
+		<layer id="407" name="bottleneck3_7/inner/dw1/conv" type="GroupConvolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>32</dim>
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+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="408" name="data_add_2416124166" type="Const" version="opset1">
+			<data element_type="f32" offset="388060" shape="1, 32, 1, 1" size="128"/>
+			<output>
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+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="409" name="bottleneck3_7/inner/dw1/bn/variance/Fused_Add_" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+				<port id="1" precision="FP32">
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+					<dim>1</dim>
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+			</input>
+			<output>
+				<port id="2" names="bottleneck3_7/inner/dw1/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="410" name="bottleneck3_7/inner/dw1/fn/weights3098040349" type="Const" version="opset1">
+			<data element_type="f32" offset="4664" shape="1" size="4"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="411" name="bottleneck3_7/inner/dw1/fn" type="PReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+				<port id="1" precision="FP32">
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+			</input>
+			<output>
+				<port id="2" names="bottleneck3_7/inner/dw1/conv" precision="FP32">
+					<dim>1</dim>
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+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="412" name="bottleneck3_7/dim_inc/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f32" offset="388188" shape="128, 32, 1, 1" size="16384"/>
+			<output>
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+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="413" name="bottleneck3_7/dim_inc/conv" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP32">
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+					<dim>1</dim>
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+					<dim>1</dim>
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+					<dim>68</dim>
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+		</layer>
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+			<data element_type="f32" offset="404572" shape="1, 128, 1, 1" size="512"/>
+			<output>
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+					<dim>1</dim>
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+		<layer id="415" name="bottleneck3_7/dim_inc/bn/variance/Fused_Add_" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+				<port id="1" precision="FP32">
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+					<dim>1</dim>
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+			</input>
+			<output>
+				<port id="2" names="bottleneck3_7/dim_inc/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
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+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
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+				<port id="1" precision="FP32">
+					<dim>1</dim>
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+					<dim>68</dim>
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+			</input>
+			<output>
+				<port id="2" names="bottleneck3_7/add" precision="FP32">
+					<dim>1</dim>
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+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="417" name="bottleneck3_7/fn/weights3102840676" type="Const" version="opset1">
+			<data element_type="f32" offset="4664" shape="1" size="4"/>
+			<output>
+				<port id="0" precision="FP32">
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+			</output>
+		</layer>
+		<layer id="418" name="bottleneck3_7/fn" type="PReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+				<port id="1" precision="FP32">
+					<dim>1</dim>
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+			</input>
+			<output>
+				<port id="2" names="bottleneck3_7/add" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
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+			</output>
+		</layer>
+		<layer id="419" name="bottleneck3_8/dim_red/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f32" offset="405084" shape="32, 128, 1, 1" size="16384"/>
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+					<dim>1</dim>
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+		</layer>
+		<layer id="420" name="bottleneck3_8/dim_red/conv" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP32">
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+					<dim>1</dim>
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+				<port id="2" precision="FP32">
+					<dim>1</dim>
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+					<dim>68</dim>
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+			</output>
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+			<data element_type="f32" offset="421468" shape="1, 32, 1, 1" size="128"/>
+			<output>
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+					<dim>1</dim>
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+			</output>
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+		<layer id="422" name="bottleneck3_8/dim_red/bn/variance/Fused_Add_" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+					<dim>68</dim>
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+				<port id="1" precision="FP32">
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+					<dim>1</dim>
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+			</input>
+			<output>
+				<port id="2" names="bottleneck3_8/dim_red/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
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+			</output>
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+		<layer id="423" name="bottleneck3_8/dim_red/fn/weights3098440022" type="Const" version="opset1">
+			<data element_type="f32" offset="4664" shape="1" size="4"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+		</layer>
+		<layer id="424" name="bottleneck3_8/dim_red/fn" type="PReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+				<port id="1" precision="FP32">
+					<dim>1</dim>
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+			<output>
+				<port id="2" names="bottleneck3_8/dim_red/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
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+			</output>
+		</layer>
+		<layer id="425" name="bottleneck3_8/inner/dw1/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f32" offset="421596" shape="32, 1, 1, 3, 3" size="1152"/>
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+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>3</dim>
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+		</layer>
+		<layer id="426" name="bottleneck3_8/inner/dw1/conv" type="GroupConvolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP32">
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+				<port id="1" precision="FP32">
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+					<dim>3</dim>
+					<dim>3</dim>
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+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
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+					<dim>68</dim>
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+			</output>
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+		<layer id="427" name="data_add_2418524190" type="Const" version="opset1">
+			<data element_type="f32" offset="422748" shape="1, 32, 1, 1" size="128"/>
+			<output>
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+		<layer id="428" name="bottleneck3_8/inner/dw1/bn/variance/Fused_Add_" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+				<port id="1" precision="FP32">
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+					<dim>1</dim>
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+			</input>
+			<output>
+				<port id="2" names="bottleneck3_8/inner/dw1/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="429" name="bottleneck3_8/inner/dw1/fn/weights3104839899" type="Const" version="opset1">
+			<data element_type="f32" offset="4664" shape="1" size="4"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="430" name="bottleneck3_8/inner/dw1/fn" type="PReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+					<dim>68</dim>
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+				<port id="1" precision="FP32">
+					<dim>1</dim>
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+			</input>
+			<output>
+				<port id="2" names="bottleneck3_8/inner/dw1/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="431" name="bottleneck3_8/dim_inc/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f32" offset="422876" shape="128, 32, 1, 1" size="16384"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>128</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="432" name="bottleneck3_8/dim_inc/conv" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP32">
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+				<port id="1" precision="FP32">
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+					<dim>1</dim>
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+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
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+			</output>
+		</layer>
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+			<data element_type="f32" offset="439260" shape="1, 128, 1, 1" size="512"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+					<dim>1</dim>
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+			</output>
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+		<layer id="434" name="bottleneck3_8/dim_inc/bn/variance/Fused_Add_" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+					<dim>68</dim>
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+				<port id="1" precision="FP32">
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+					<dim>128</dim>
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+					<dim>1</dim>
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+			</input>
+			<output>
+				<port id="2" names="bottleneck3_8/dim_inc/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="435" name="bottleneck3_8/add" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
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+					<dim>68</dim>
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+				<port id="1" precision="FP32">
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+					<dim>40</dim>
+					<dim>68</dim>
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+			</input>
+			<output>
+				<port id="2" names="bottleneck3_8/add" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
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+		<layer id="436" name="bottleneck3_8/fn/weights3083239761" type="Const" version="opset1">
+			<data element_type="f32" offset="4664" shape="1" size="4"/>
+			<output>
+				<port id="0" precision="FP32">
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+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
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+					<dim>68</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
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+			</input>
+			<output>
+				<port id="2" names="bottleneck3_8/add" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="438" name="bottleneck3_9/dim_red/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f32" offset="439772" shape="32, 128, 1, 1" size="16384"/>
+			<output>
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+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="439" name="bottleneck3_9/dim_red/conv" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
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+				<port id="0" precision="FP32">
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+					<dim>1</dim>
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+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
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+			</output>
+		</layer>
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+			<output>
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+					<dim>32</dim>
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+					<dim>1</dim>
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+			</output>
+		</layer>
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+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
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+			</input>
+			<output>
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+		</layer>
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+			<data auto_broadcast="numpy"/>
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+			<data auto_broadcast="numpy"/>
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+					<dim>68</dim>
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+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+			<data auto_broadcast="numpy"/>
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+				<port id="0" precision="FP32">
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+					<dim>68</dim>
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+				<port id="0" precision="FP32">
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+					<dim>68</dim>
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+			<data auto_broadcast="numpy"/>
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+				<port id="0" precision="FP32">
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+				<port id="1" precision="FP32">
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+			<data auto_broadcast="numpy"/>
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+					<dim>68</dim>
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+				</port>
+			</output>
+		</layer>
+		<layer id="475" name="bottleneck3_10/fn" type="PReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="bottleneck3_10/add" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="476" name="bottleneck4_0/skip/pooling" type="MaxPool" version="opset1">
+			<data auto_pad="explicit" kernel="2, 2" pads_begin="0, 0" pads_end="0, 0" rounding_type="ceil" strides="2, 2"/>
+			<input>
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+					<dim>68</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" names="bottleneck4_0/skip/pooling" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>20</dim>
+					<dim>34</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="477" name="bottleneck4_0/skip/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f32" offset="509148" shape="256, 128, 1, 1" size="131072"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>256</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="478" name="bottleneck4_0/skip/conv" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+				<port id="1" precision="FP32">
+					<dim>256</dim>
+					<dim>128</dim>
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+					<dim>1</dim>
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+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>20</dim>
+					<dim>34</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="479" name="data_add_2424924254" type="Const" version="opset1">
+			<data element_type="f32" offset="640220" shape="1, 256, 1, 1" size="1024"/>
+			<output>
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+					<dim>256</dim>
+					<dim>1</dim>
+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="480" name="bottleneck4_0/skip/bn/variance/Fused_Add_" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+					<dim>1</dim>
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+			</input>
+			<output>
+				<port id="2" names="bottleneck4_0/skip/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>20</dim>
+					<dim>34</dim>
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+			</output>
+		</layer>
+		<layer id="481" name="bottleneck4_0/dim_red/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f32" offset="641244" shape="64, 128, 1, 1" size="32768"/>
+			<output>
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+					<dim>1</dim>
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+		<layer id="482" name="bottleneck4_0/dim_red/conv" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP32">
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+				<port id="1" precision="FP32">
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+					<dim>128</dim>
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+					<dim>1</dim>
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+				<port id="2" precision="FP32">
+					<dim>1</dim>
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+					<dim>40</dim>
+					<dim>68</dim>
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+			</output>
+		</layer>
+		<layer id="483" name="data_add_2425724262" type="Const" version="opset1">
+			<data element_type="f32" offset="674012" shape="1, 64, 1, 1" size="256"/>
+			<output>
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+					<dim>1</dim>
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+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="484" name="bottleneck4_0/dim_red/bn/variance/Fused_Add_" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>64</dim>
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+				<port id="1" precision="FP32">
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+					<dim>1</dim>
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+			</input>
+			<output>
+				<port id="2" names="bottleneck4_0/dim_red/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>64</dim>
+					<dim>40</dim>
+					<dim>68</dim>
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+			</output>
+		</layer>
+		<layer id="485" name="bottleneck4_0/dim_red/fn/weights3109639941" type="Const" version="opset1">
+			<data element_type="f32" offset="4664" shape="1" size="4"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="486" name="bottleneck4_0/dim_red/fn" type="PReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+				<port id="1" precision="FP32">
+					<dim>1</dim>
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+			<output>
+				<port id="2" names="bottleneck4_0/dim_red/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>64</dim>
+					<dim>40</dim>
+					<dim>68</dim>
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+			</output>
+		</layer>
+		<layer id="487" name="bottleneck4_0/inner/dw1/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f32" offset="674268" shape="64, 1, 1, 3, 3" size="2304"/>
+			<output>
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+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>3</dim>
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+		</layer>
+		<layer id="488" name="bottleneck4_0/inner/dw1/conv" type="GroupConvolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" strides="2, 2"/>
+			<input>
+				<port id="0" precision="FP32">
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+					<dim>3</dim>
+					<dim>3</dim>
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+				<port id="2" precision="FP32">
+					<dim>1</dim>
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+					<dim>20</dim>
+					<dim>34</dim>
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+		<layer id="489" name="data_add_2426524270" type="Const" version="opset1">
+			<data element_type="f32" offset="676572" shape="1, 64, 1, 1" size="256"/>
+			<output>
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+					<dim>1</dim>
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+		</layer>
+		<layer id="490" name="bottleneck4_0/inner/dw1/bn/variance/Fused_Add_" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+					<dim>1</dim>
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+			</input>
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+					<dim>1</dim>
+					<dim>64</dim>
+					<dim>20</dim>
+					<dim>34</dim>
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+			</output>
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+			<data element_type="f32" offset="4664" shape="1" size="4"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+		<layer id="492" name="bottleneck4_0/inner/dw1/fn" type="PReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+				<port id="1" precision="FP32">
+					<dim>1</dim>
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+			</input>
+			<output>
+				<port id="2" names="bottleneck4_0/inner/dw1/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>64</dim>
+					<dim>20</dim>
+					<dim>34</dim>
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+			</output>
+		</layer>
+		<layer id="493" name="bottleneck4_0/dim_inc/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f32" offset="676828" shape="256, 64, 1, 1" size="65536"/>
+			<output>
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+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="494" name="bottleneck4_0/dim_inc/conv" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
+			<input>
+				<port id="0" precision="FP32">
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+					<dim>1</dim>
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+					<dim>1</dim>
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+					<dim>20</dim>
+					<dim>34</dim>
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+			<data element_type="f32" offset="742364" shape="1, 256, 1, 1" size="1024"/>
+			<output>
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+					<dim>1</dim>
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+					<dim>1</dim>
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+		</layer>
+		<layer id="496" name="bottleneck4_0/dim_inc/bn/variance/Fused_Add_" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+			<output>
+				<port id="2" names="bottleneck4_0/dim_inc/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>20</dim>
+					<dim>34</dim>
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+			</output>
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+		<layer id="497" name="bottleneck4_0/add" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+			</input>
+			<output>
+				<port id="2" names="bottleneck4_0/add" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>20</dim>
+					<dim>34</dim>
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+			</output>
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+		<layer id="498" name="bottleneck4_0/fn/weights3078039842" type="Const" version="opset1">
+			<data element_type="f32" offset="4664" shape="1" size="4"/>
+			<output>
+				<port id="0" precision="FP32">
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+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>20</dim>
+					<dim>34</dim>
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+				<port id="1" precision="FP32">
+					<dim>1</dim>
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+			</input>
+			<output>
+				<port id="2" names="bottleneck4_0/add" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>20</dim>
+					<dim>34</dim>
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+			</output>
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+		<layer id="500" name="bottleneck4_1/dim_red/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f32" offset="743388" shape="64, 256, 1, 1" size="65536"/>
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+					<dim>1</dim>
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+		</layer>
+		<layer id="501" name="bottleneck4_1/dim_red/conv" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
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+				<port id="0" precision="FP32">
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+					<dim>1</dim>
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+					<dim>34</dim>
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+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
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+					<dim>1</dim>
+					<dim>64</dim>
+					<dim>20</dim>
+					<dim>34</dim>
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+				<port id="0" precision="FP32">
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+			<output>
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+					<dim>1</dim>
+					<dim>64</dim>
+					<dim>20</dim>
+					<dim>34</dim>
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+			</output>
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+			<data element_type="f32" offset="809180" shape="64, 1, 1, 3, 3" size="2304"/>
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+		</layer>
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+			<data auto_pad="explicit" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" strides="1, 1"/>
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+					<dim>1</dim>
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+					<dim>34</dim>
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+			<data auto_broadcast="numpy"/>
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+				<port id="0" precision="FP32">
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+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="544" name="bottleneck4_3/inner/dw1/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
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+		</layer>
+		<layer id="545" name="bottleneck4_3/inner/dw1/conv" type="GroupConvolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" strides="1, 1"/>
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+			<data auto_broadcast="numpy"/>
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+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
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+			<data auto_broadcast="numpy"/>
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+				<port id="0" precision="FP32">
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+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+			<data auto_broadcast="numpy"/>
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+				<port id="0" precision="FP32">
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+				<port id="0" precision="FP32">
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+			<data auto_broadcast="numpy"/>
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+				<port id="0" precision="FP32">
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+			<data auto_broadcast="numpy"/>
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+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="577" name="bottleneck4_5/dim_red/conv" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
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+			</output>
+		</layer>
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+		</layer>
+		<layer id="579" name="bottleneck4_5/dim_red/bn/variance/Fused_Add_" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
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+				<port id="0" precision="FP32">
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+			<data auto_broadcast="numpy"/>
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+			<data auto_broadcast="numpy"/>
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+					<dim>1</dim>
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+					<dim>20</dim>
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+			</output>
+		</layer>
+		<layer id="611" name="bottleneck4_6/add" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
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+				<port id="0" precision="FP32">
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+					<dim>1</dim>
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+			</output>
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+		<layer id="612" name="bottleneck4_6/fn/weights3111640460" type="Const" version="opset1">
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+				<port id="0" precision="FP32">
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+				<port id="2" names="bottleneck4_6/add" precision="FP32">
+					<dim>1</dim>
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+			</output>
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+		<layer id="614" name="bottleneck4_7/dim_red/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
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+					<dim>1</dim>
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+		<layer id="615" name="bottleneck4_7/dim_red/conv" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
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+		<layer id="617" name="bottleneck4_7/dim_red/bn/variance/Fused_Add_" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
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+					<dim>64</dim>
+					<dim>20</dim>
+					<dim>34</dim>
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+			</output>
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+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+					<dim>3</dim>
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+		</layer>
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+			<data auto_pad="explicit" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" strides="1, 1"/>
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+		<layer id="623" name="bottleneck4_7/inner/dw1/bn/variance/Fused_Add_" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
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+				<port id="0" precision="FP32">
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+				<port id="0" precision="FP32">
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+					<dim>20</dim>
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+			<data element_type="f32" offset="1621212" shape="256, 64, 1, 1" size="65536"/>
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+					<dim>1</dim>
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+		<layer id="627" name="bottleneck4_7/dim_inc/conv" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
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+			<data auto_broadcast="numpy"/>
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+				<port id="0" precision="FP32">
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+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>20</dim>
+					<dim>34</dim>
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+				<port id="0" precision="FP32">
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+				<port id="0" precision="FP32">
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+					<dim>1</dim>
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+			<data auto_broadcast="numpy"/>
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+				<port id="0" precision="FP32">
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+					<dim>64</dim>
+					<dim>20</dim>
+					<dim>34</dim>
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+				<port id="0" precision="FP32">
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+			<data auto_pad="explicit" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" strides="1, 1"/>
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+			<data auto_broadcast="numpy"/>
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+				<port id="0" precision="FP32">
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+				<port id="2" names="bottleneck4_8/inner/dw1/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>64</dim>
+					<dim>20</dim>
+					<dim>34</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="645" name="bottleneck4_8/dim_inc/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f32" offset="1756124" shape="256, 64, 1, 1" size="65536"/>
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+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="646" name="bottleneck4_8/dim_inc/conv" type="Convolution" version="opset1">
+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
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+				<port id="0" precision="FP32">
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+					<dim>1</dim>
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+				</port>
+			</output>
+		</layer>
+		<layer id="647" name="data_add_2446524470" type="Const" version="opset1">
+			<data element_type="f32" offset="1821660" shape="1, 256, 1, 1" size="1024"/>
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+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="648" name="bottleneck4_8/dim_inc/bn/variance/Fused_Add_" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
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+				<port id="0" precision="FP32">
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+			<output>
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+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>20</dim>
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+			</output>
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+				<port id="0" precision="FP32">
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+				<port id="0" precision="FP32">
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+			<data element_type="f32" offset="1822684" shape="64, 256, 1, 1" size="65536"/>
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+					<dim>1</dim>
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+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
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+			<data auto_broadcast="numpy"/>
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+				<port id="0" precision="FP32">
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+					<dim>1</dim>
+					<dim>64</dim>
+					<dim>20</dim>
+					<dim>34</dim>
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+					<dim>1</dim>
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+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+			<data auto_pad="explicit" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" strides="1, 1"/>
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+			<data auto_broadcast="numpy"/>
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+				<port id="0" precision="FP32">
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+					<dim>1</dim>
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+				<port id="0" precision="FP32">
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+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
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+			<data auto_broadcast="numpy"/>
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+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>20</dim>
+					<dim>34</dim>
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+				<port id="0" precision="FP32">
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+				<port id="0" precision="FP32">
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+				<port id="1" precision="FP32">
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+					<dim>1</dim>
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+			<data element_type="f32" offset="1957596" shape="64, 256, 1, 1" size="65536"/>
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+			<data auto_broadcast="numpy"/>
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+				<port id="0" precision="FP32">
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+					<dim>64</dim>
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+				<port id="0" precision="FP32">
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+					<dim>1</dim>
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+					<dim>34</dim>
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+	<meta_data>
+		<MO_version value="2021.4.0-3827-c5b65f2cb1d-releases/2021/4"/>
+		<cli_parameters>
+			<caffe_parser_path value="DIR"/>
+			<data_type value="FP32"/>
+			<disable_nhwc_to_nchw value="False"/>
+			<disable_omitting_optional value="False"/>
+			<disable_resnet_optimization value="False"/>
+			<disable_weights_compression value="False"/>
+			<enable_concat_optimization value="False"/>
+			<enable_flattening_nested_params value="False"/>
+			<enable_ssd_gluoncv value="False"/>
+			<extensions value="DIR"/>
+			<framework value="caffe"/>
+			<freeze_placeholder_with_value value="{}"/>
+			<generate_deprecated_IR_V7 value="False"/>
+			<input value="data"/>
+			<input_model value="DIR/rmnet_lrelu_pd_ssd.caffemodel"/>
+			<input_model_is_text value="False"/>
+			<input_proto value="DIR/rmnet_lrelu_pd_ssd.prototxt"/>
+			<input_shape value="[1,3,320,544]"/>
+			<k value="DIR/CustomLayersMapping.xml"/>
+			<keep_shape_ops value="True"/>
+			<legacy_ir_generation value="False"/>
+			<legacy_mxnet_model value="False"/>
+			<log_level value="ERROR"/>
+			<mean_scale_values value="{}"/>
+			<mean_values value="()"/>
+			<model_name value="person-detection-retail-0013"/>
+			<output value="['detection_out']"/>
+			<output_dir value="DIR"/>
+			<placeholder_data_types value="{}"/>
+			<placeholder_shapes value="{'data': array([  1,   3, 320, 544])}"/>
+			<progress value="False"/>
+			<remove_memory value="False"/>
+			<remove_output_softmax value="False"/>
+			<reverse_input_channels value="False"/>
+			<save_params_from_nd value="False"/>
+			<scale_values value="()"/>
+			<silent value="False"/>
+			<static_shape value="False"/>
+			<stream_output value="False"/>
+			<transform value=""/>
+			<unset unset_cli_parameters="batch, counts, disable_fusing, disable_gfusing, finegrain_fusing, input_checkpoint, input_meta_graph, input_symbol, mean_file, mean_file_offsets, move_to_preprocess, nd_prefix_name, pretrained_model_name, saved_model_dir, saved_model_tags, scale, tensorboard_logdir, tensorflow_custom_layer_libraries, tensorflow_custom_operations_config_update, tensorflow_object_detection_api_pipeline_config, tensorflow_use_custom_operations_config, transformations_config"/>
+		</cli_parameters>
+	</meta_data>
+</net>