diff --git a/models/yolov6n_thermal_people_256x192/model.yml b/models/yolov6n_thermal_people_256x192/model.yml
new file mode 100644
index 0000000..d0bc168
--- /dev/null
+++ b/models/yolov6n_thermal_people_256x192/model.yml
@@ -0,0 +1,40 @@
+description: Real-time Object detection with YoloV6n pre-trained on SCUT FIR Pedestrian Dataset
+files:
+- checksum: 43a54554b8f8f8beb9520dd1c2fa74137b83798d5ee6f2970f243ae596b678bf5c8af2238e30bfbf30c818d1773bb608
+  name: FP16/yolov6n_thermal_people_256x192.xml
+  sha256: 85654dc85d9b511d3dce768f4f9d1140f8b15d11bffe0d0d201fb2b96b3fa105
+  size: 289841
+  source: https://github.com/luxonis/depthai-model-zoo/raw/main/models/yolov6n_thermal_people_256x192/yolov6n_thermal_people_256x192.xml
+- checksum: 9228c79e7096c9cbf34756c64d75e8c066e7c959ee34e4825dbb9e4704cfeaeb7e8c9e29daefc9acd1a032569f184927
+  name: FP16/yolov6n_thermal_people_256x192.bin
+  sha256: f0012af5a75e72f8591e2ff68f1636f6b98217e7fd7b31ac9ffc02d8450bbd36
+  size: 9257446
+  source: https://robothub.fra1.cdn.digitaloceanspaces.com/models/yolov6n_thermal_people_256x192/yolov6n_thermal_people_256x192.bin
+framework: dldt
+license: https://github.com/luxonis/depthai-model-zoo/tree/main/models/yolov6n_thermal_people_256x192
+meta:
+  FPS:
+    mean: /
+    std: /
+  backbone: EfficientRep
+  downloads:
+  - name: yolov6n_thermal_people_vehicles_256x192.xml
+    url: https://github.com/luxonis/depthai-model-zoo/raw/main/models/yolov6n_thermal_people_256x192/yolov6n_thermal_people_256x192.xml
+  - name: yolov6n_thermal_people_vehicles_256x192.bin
+    url: https://robothub.fra1.cdn.digitaloceanspaces.com/models/yolov6n_thermal_people_256x192/yolov6n_thermal_people_256x192.bin
+  experiment: null
+  featured: true
+  inputs:
+  - name: images
+    shape: 1, 192, 256
+    type: image
+  license:
+    name: GNU GPL-3
+    url: https://github.com/meituan/YOLOv6/blob/main/LICENSE
+  source:
+  - name: GitHub
+    url: https://github.com/meituan/YOLOv6
+  thumbnail: null
+  uses_depth: false
+  verbose_name: YoloV6 Nano
+task_type: detection
diff --git a/models/yolov6n_thermal_people_256x192/yolov6n_thermal_people_256x192.xml b/models/yolov6n_thermal_people_256x192/yolov6n_thermal_people_256x192.xml
new file mode 100644
index 0000000..3e42b94
--- /dev/null
+++ b/models/yolov6n_thermal_people_256x192/yolov6n_thermal_people_256x192.xml
@@ -0,0 +1,10456 @@
+<?xml version="1.0"?>
+<net name="torch_jit" version="11">
+	<layers>
+		<layer id="0" name="images" type="Parameter" version="opset1">
+			<data shape="1,1,192,256" element_type="f32" />
+			<rt_info>
+				<attribute name="old_api_map_element_type" version="0" value="f16" />
+			</rt_info>
+			<output>
+				<port id="0" precision="FP32" names="images">
+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>192</dim>
+					<dim>256</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="1" name="Constant_3238_compressed" type="Const" version="opset1">
+			<data element_type="f16" shape="16, 1, 3, 3" offset="0" size="288" />
+			<rt_info>
+				<attribute name="preprocessing" version="0" />
+			</rt_info>
+			<output>
+				<port id="0" precision="FP16">
+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="2" name="Constant_3238" type="Convert" version="opset1">
+			<data destination_type="f32" />
+			<rt_info>
+				<attribute name="decompression" version="0" />
+				<attribute name="preprocessing" version="0" />
+			</rt_info>
+			<input>
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+					<dim>16</dim>
+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>3</dim>
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+				<port id="1" precision="FP32">
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+					<dim>3</dim>
+					<dim>3</dim>
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+			</output>
+		</layer>
+		<layer id="3" name="/backbone/stem/rbr_reparam/Conv/WithoutBiases" type="Convolution" version="opset1">
+			<data strides="2, 2" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" />
+			<rt_info>
+				<attribute name="preprocessing" version="0" />
+			</rt_info>
+			<input>
+				<port id="0" precision="FP32">
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+					<dim>192</dim>
+					<dim>256</dim>
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+				<port id="1" precision="FP32">
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+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
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+					<dim>16</dim>
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+					<dim>128</dim>
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+			</output>
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+		<layer id="4" name="Reshape_155_compressed" type="Const" version="opset1">
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+		</layer>
+		<layer id="5" name="Reshape_155" type="Convert" version="opset1">
+			<data destination_type="f32" />
+			<rt_info>
+				<attribute name="decompression" version="0" />
+			</rt_info>
+			<input>
+				<port id="0" precision="FP16">
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+			</output>
+		</layer>
+		<layer id="6" name="/backbone/stem/rbr_reparam/Conv" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
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+		</layer>
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+			<rt_info>
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+					<dim>128</dim>
+					<dim>6</dim>
+					<dim>8</dim>
+				</port>
+			</input>
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+				<port id="1" precision="FP32" names="/backbone/ERBlock_5/ERBlock_5.2/cspsppf/cv1/block/act/Relu_output_0">
+					<dim>1</dim>
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+					<dim>6</dim>
+					<dim>8</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="148" name="backbone.ERBlock_5.2.cspsppf.cv3.block.conv.weight_compressed" type="Const" version="opset1">
+			<data element_type="f16" shape="128, 128, 3, 3" offset="5379968" size="294912" />
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+					<dim>128</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="149" name="backbone.ERBlock_5.2.cspsppf.cv3.block.conv.weight" type="Convert" version="opset1">
+			<data destination_type="f32" />
+			<rt_info>
+				<attribute name="decompression" version="0" />
+			</rt_info>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>128</dim>
+					<dim>128</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32" names="backbone.ERBlock_5.2.cspsppf.cv3.block.conv.weight">
+					<dim>128</dim>
+					<dim>128</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="150" name="/backbone/ERBlock_5/ERBlock_5.2/cspsppf/cv3/block/conv/Conv/WithoutBiases" type="Convolution" version="opset1">
+			<data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" />
+			<input>
+				<port id="0" precision="FP32">
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+				<port id="1" precision="FP32">
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+					<dim>128</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>6</dim>
+					<dim>8</dim>
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+			</output>
+		</layer>
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+			<data element_type="f16" shape="1, 128, 1, 1" offset="5674880" size="256" />
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+					<dim>1</dim>
+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="152" name="Reshape_475" type="Convert" version="opset1">
+			<data destination_type="f32" />
+			<rt_info>
+				<attribute name="decompression" version="0" />
+			</rt_info>
+			<input>
+				<port id="0" precision="FP16">
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+					<dim>1</dim>
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+			<output>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="153" name="/backbone/ERBlock_5/ERBlock_5.2/cspsppf/cv3/block/conv/Conv" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>6</dim>
+					<dim>8</dim>
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+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="/backbone/ERBlock_5/ERBlock_5.2/cspsppf/cv3/block/conv/Conv_output_0">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>6</dim>
+					<dim>8</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="154" name="/backbone/ERBlock_5/ERBlock_5.2/cspsppf/cv3/block/act/Relu" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>6</dim>
+					<dim>8</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32" names="/backbone/ERBlock_5/ERBlock_5.2/cspsppf/cv3/block/act/Relu_output_0">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>6</dim>
+					<dim>8</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="155" name="backbone.ERBlock_5.2.cspsppf.cv4.block.conv.weight_compressed" type="Const" version="opset1">
+			<data element_type="f16" shape="128, 128, 1, 1" offset="5675136" size="32768" />
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+					<dim>128</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="156" name="backbone.ERBlock_5.2.cspsppf.cv4.block.conv.weight" type="Convert" version="opset1">
+			<data destination_type="f32" />
+			<rt_info>
+				<attribute name="decompression" version="0" />
+			</rt_info>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>128</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
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+			</input>
+			<output>
+				<port id="1" precision="FP32" names="backbone.ERBlock_5.2.cspsppf.cv4.block.conv.weight">
+					<dim>128</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="157" name="/backbone/ERBlock_5/ERBlock_5.2/cspsppf/cv4/block/conv/Conv/WithoutBiases" type="Convolution" version="opset1">
+			<data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" />
+			<input>
+				<port id="0" precision="FP32">
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+					<dim>8</dim>
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+				<port id="1" precision="FP32">
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+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>6</dim>
+					<dim>8</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="158" name="Reshape_491_compressed" type="Const" version="opset1">
+			<data element_type="f16" shape="1, 128, 1, 1" offset="5707904" size="256" />
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+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="159" name="Reshape_491" type="Convert" version="opset1">
+			<data destination_type="f32" />
+			<rt_info>
+				<attribute name="decompression" version="0" />
+			</rt_info>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
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+					<dim>1</dim>
+					<dim>1</dim>
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+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="160" name="/backbone/ERBlock_5/ERBlock_5.2/cspsppf/cv4/block/conv/Conv" type="Add" version="opset1">
+			<data auto_broadcast="numpy" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
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+					<dim>8</dim>
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+				<port id="1" precision="FP32">
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+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32" names="/backbone/ERBlock_5/ERBlock_5.2/cspsppf/cv4/block/conv/Conv_output_0">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>6</dim>
+					<dim>8</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="161" name="/backbone/ERBlock_5/ERBlock_5.2/cspsppf/cv4/block/act/Relu" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>6</dim>
+					<dim>8</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32" names="/backbone/ERBlock_5/ERBlock_5.2/cspsppf/cv4/block/act/Relu_output_0">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>6</dim>
+					<dim>8</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="162" name="/backbone/ERBlock_5/ERBlock_5.2/cspsppf/m/MaxPool" type="MaxPool" version="opset8">
+			<data strides="1, 1" dilations="1, 1" pads_begin="2, 2" pads_end="2, 2" kernel="5, 5" rounding_type="floor" auto_pad="explicit" index_element_type="i64" axis="0" />
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
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+					<dim>8</dim>
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+			</input>
+			<output>
+				<port id="1" precision="FP32" names="/backbone/ERBlock_5/ERBlock_5.2/cspsppf/m/MaxPool_output_0">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>6</dim>
+					<dim>8</dim>
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+				<port id="2" precision="I64">
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+					<dim>128</dim>
+					<dim>6</dim>
+					<dim>8</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="163" name="/backbone/ERBlock_5/ERBlock_5.2/cspsppf/m_1/MaxPool" type="MaxPool" version="opset8">
+			<data strides="1, 1" dilations="1, 1" pads_begin="2, 2" pads_end="2, 2" kernel="5, 5" rounding_type="floor" auto_pad="explicit" index_element_type="i64" axis="0" />
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+			</input>
+			<output>
+				<port id="1" precision="FP32" names="/backbone/ERBlock_5/ERBlock_5.2/cspsppf/m_1/MaxPool_output_0">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>6</dim>
+					<dim>8</dim>
+				</port>
+				<port id="2" precision="I64">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>6</dim>
+					<dim>8</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="164" name="/backbone/ERBlock_5/ERBlock_5.2/cspsppf/m_2/MaxPool" type="MaxPool" version="opset8">
+			<data strides="1, 1" dilations="1, 1" pads_begin="2, 2" pads_end="2, 2" kernel="5, 5" rounding_type="floor" auto_pad="explicit" index_element_type="i64" axis="0" />
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+					<dim>128</dim>
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+					<dim>8</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32" names="/backbone/ERBlock_5/ERBlock_5.2/cspsppf/m_2/MaxPool_output_0">
+					<dim>1</dim>
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+					<dim>8</dim>
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+				<port id="2" precision="I64">
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+					<dim>8</dim>
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+			</output>
+		</layer>
+		<layer id="165" name="/backbone/ERBlock_5/ERBlock_5.2/cspsppf/Concat" type="Concat" version="opset1">
+			<data axis="1" />
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+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>6</dim>
+					<dim>8</dim>
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+				<port id="1" precision="FP32">
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+					<dim>128</dim>
+					<dim>6</dim>
+					<dim>8</dim>
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+				<port id="2" precision="FP32">
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+					<dim>8</dim>
+				</port>
+				<port id="3" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>6</dim>
+					<dim>8</dim>
+				</port>
+			</input>
+			<output>
+				<port id="4" precision="FP32" names="/backbone/ERBlock_5/ERBlock_5.2/cspsppf/Concat_output_0">
+					<dim>1</dim>
+					<dim>512</dim>
+					<dim>6</dim>
+					<dim>8</dim>
+				</port>
+			</output>
+		</layer>
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+			<data element_type="f16" shape="128, 512, 1, 1" offset="5708160" size="131072" />
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+					<dim>512</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
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+			<data destination_type="f32" />
+			<rt_info>
+				<attribute name="decompression" version="0" />
+			</rt_info>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>128</dim>
+					<dim>512</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
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+			<output>
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+					<dim>128</dim>
+					<dim>512</dim>
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+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="168" name="/backbone/ERBlock_5/ERBlock_5.2/cspsppf/cv5/block/conv/Conv/WithoutBiases" type="Convolution" version="opset1">
+			<data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" />
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+				<port id="0" precision="FP32">
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+					<dim>8</dim>
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+				<port id="1" precision="FP32">
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+					<dim>512</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>6</dim>
+					<dim>8</dim>
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+			</output>
+		</layer>
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+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="170" name="Reshape_527" type="Convert" version="opset1">
+			<data destination_type="f32" />
+			<rt_info>
+				<attribute name="decompression" version="0" />
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+				<port id="0" precision="FP16">
+					<dim>1</dim>
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+					<dim>1</dim>
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+			<output>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
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+			<data auto_broadcast="numpy" />
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+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>6</dim>
+					<dim>8</dim>
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+				<port id="1" precision="FP32">
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+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
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+			</input>
+			<output>
+				<port id="2" precision="FP32" names="/backbone/ERBlock_5/ERBlock_5.2/cspsppf/cv5/block/conv/Conv_output_0">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>6</dim>
+					<dim>8</dim>
+				</port>
+			</output>
+		</layer>
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+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>6</dim>
+					<dim>8</dim>
+				</port>
+			</input>
+			<output>
+				<port id="1" precision="FP32" names="/backbone/ERBlock_5/ERBlock_5.2/cspsppf/cv5/block/act/Relu_output_0">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>6</dim>
+					<dim>8</dim>
+				</port>
+			</output>
+		</layer>
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+			<data element_type="f16" shape="128, 128, 3, 3" offset="5839488" size="294912" />
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+					<dim>128</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="174" name="backbone.ERBlock_5.2.cspsppf.cv6.block.conv.weight" type="Convert" version="opset1">
+			<data destination_type="f32" />
+			<rt_info>
+				<attribute name="decompression" version="0" />
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+			<input>
+				<port id="0" precision="FP16">
+					<dim>128</dim>
+					<dim>128</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
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+			<output>
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+					<dim>128</dim>
+					<dim>128</dim>
+					<dim>3</dim>
+					<dim>3</dim>
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+			</output>
+		</layer>
+		<layer id="175" name="/backbone/ERBlock_5/ERBlock_5.2/cspsppf/cv6/block/conv/Conv/WithoutBiases" type="Convolution" version="opset1">
+			<data strides="1, 1" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" auto_pad="explicit" />
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+				<port id="0" precision="FP32">
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+				<port id="1" precision="FP32">
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+					<dim>128</dim>
+					<dim>3</dim>
+					<dim>3</dim>
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+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>6</dim>
+					<dim>8</dim>
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+			</output>
+		</layer>
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+			</output>
+		</layer>
+		<layer id="177" name="Reshape_543" type="Convert" version="opset1">
+			<data destination_type="f32" />
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+				<attribute name="decompression" version="0" />
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+			</output>
+		</layer>
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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>128</dim>
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+				</port>
+			</input>
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+				<port id="2" precision="FP32" names="/backbone/ERBlock_5/ERBlock_5.2/cspsppf/cv6/block/conv/Conv_output_0">
+					<dim>1</dim>
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+					<dim>6</dim>
+					<dim>8</dim>
+				</port>
+			</output>
+		</layer>
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+				<port id="1" precision="FP32" names="/backbone/ERBlock_5/ERBlock_5.2/cspsppf/cv6/block/act/Relu_output_0">
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+			</output>
+		</layer>
+		<layer id="180" name="/backbone/ERBlock_5/ERBlock_5.2/cspsppf/Concat_1" type="Concat" version="opset1">
+			<data axis="1" />
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+			</input>
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+				<port id="2" precision="FP32" names="/backbone/ERBlock_5/ERBlock_5.2/cspsppf/Concat_1_output_0">
+					<dim>1</dim>
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+			</output>
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+			<data destination_type="f32" />
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+			<data destination_type="f32" />
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+				<attribute name="decompression" version="0" />
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+			<data auto_broadcast="numpy" />
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+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+				<port id="2" precision="FP32" names="/backbone/ERBlock_5/ERBlock_5.2/cspsppf/cv7/block/conv/Conv_output_0">
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+					<dim>6</dim>
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+			</output>
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+					<dim>6</dim>
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+			</output>
+		</layer>
+		<layer id="188" name="neck.reduce_layer0.block.conv.weight_compressed" type="Const" version="opset1">
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+			<data destination_type="f32" />
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+				<attribute name="decompression" version="0" />
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+			<data strides="1, 1" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" auto_pad="explicit" />
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+			<data destination_type="f32" />
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+				<attribute name="decompression" version="0" />
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+			<data auto_broadcast="numpy" />
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+				<port id="0" precision="FP32">
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+			</input>
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+				<port id="2" precision="FP32" names="/neck/reduce_layer0/block/conv/Conv_output_0">
+					<dim>1</dim>
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+					<dim>6</dim>
+					<dim>8</dim>
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+			</output>
+		</layer>
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+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+				<port id="1" precision="FP32" names="/neck/reduce_layer0/block/act/Relu_output_0">
+					<dim>1</dim>
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+					<dim>6</dim>
+					<dim>8</dim>
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+			</output>
+		</layer>
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+			<data destination_type="f32" />
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+			</output>
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+			<data auto_broadcast="numpy" />
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