diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..483de3d
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,2 @@
+.venv
+.idea
diff --git a/LICENSES/LicenseRef-ONF-Member-1.0.txt b/LICENSES/LicenseRef-ONF-Member-1.0.txt
new file mode 100644
index 0000000..3625ce5
--- /dev/null
+++ b/LICENSES/LicenseRef-ONF-Member-1.0.txt
@@ -0,0 +1,295 @@
+NF Member-Only Software License
+Version 1.0
+June 2020
+
+This ONF Member-Only Software License (this "License") is entered into by and
+between Open Networking Foundation ("ONF") and you and/or the entity on whose
+behalf you are accessing the Works (as defined in Section 1 below) ("you").
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+to your access to and use of the Works. You may not access or use the Works if
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+
+By accessing or using the Works, you hereby agree to the terms of this License.
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+barred from receiving the Works under the laws of the United States or other
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+authority, you may not accept this License or access or use the Works on behalf
+of your employer or any other entity.  You and ONF acknowledge and agree as
+follows:
+
+1. DEFINITIONS.
+
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+but not limited to software source code, documentation source, and
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+
+"Object" form shall mean any form resulting from mechanical transformation or
+translation of a Source form, including but not limited to compiled object
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+
+"Works" shall mean the works of authorship, whether in Source or Object form,
+made available under this License, as indicated by a copyright notice/header
+file that is included in or attached to the Works.
+
+2. BACKGROUND.
+
+The purpose of this License is to provide ONF member companies in good standing
+("Members") with early access to software projects currently in development by
+ONF prior to their public release, to allow Members to review and test the
+Works internally for the purpose of making further contributions to the
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+of the final code base for the applicable project to third parties under the
+2.0 version of the Apache License, or other permissive license terms selected
+by ONF in its sole discretion.
+
+The Purpose of this License is to incubate the development of new software
+projects by providing a framework for ONF Members to access and contribute to
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+
+3. LIMITED LICENSE TO ALL MEMBERS IN GOOD STANDING.
+
+Subject to your complete and ongoing compliance with all the terms and
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+
+Each project subject to this License shall be declared by ONF to be in one of
+two phases:  a) Incubation Phase, or b) Partner Commercialization Phase.  Each
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+4.1 GRACE PERIOD FOR NON-PARTNER-LEVEL MEMBERS
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+
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+
+11. LIMITATION OF LIABILITY.
+
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+OF AMOUNTS PAID BY YOU FOR THE WORKS, IF ANY.
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+12. INDEMNIFICATION.
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+copyright, trademark, trade secret, trade dress, patent or other intellectual
+property right of any person, and (c) any non-compliance by you with this
+License or any applicable law or regulation.
+
+13. CHANGES TO THIS LICENSE.
+
+ONF may make changes to this License as it distributes new versions of the
+Works. When these changes are made, ONF will make a new version of this License
+available on the ONF website.
+
+14. MISCELLANEOUS TERMS.
+
+This License constitutes the entire legal agreement between you and ONF with
+regard to your access and use of the Works, and governs your access and use of
+the Works and completely supersedes any prior agreements between you and ONF in
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+enforceable so long as this License, as so modified, does not substantially
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+The rights granted in this License may not be assigned or transferred by you
+without the prior written approval of ONF. You shall not be permitted to
+delegate your responsibilities or obligations under this License without the
+Prior written approval of ONF.
+
diff --git a/Makefile b/Makefile
index e057470..e4ca58d 100644
--- a/Makefile
+++ b/Makefile
@@ -1,5 +1,67 @@
-all:
+# SPDX-FileCopyrightText: 2020-present Open Networking Foundation <info@opennetworking.org>
+# SPDX-License-Identifier: LicenseRef-ONF-Member-1.01
+#
+define PROJECT_HELP_MSG
+Usage:
+    make help                   show this message
+    make clean                  remove intermediate files
 
-build:
+    make ${VENV}                  make a virtualenv in the base directory
+    make python-reqs            install python packages in requirements.pip
+    make git-config             set local git configuration
+    make setup                  make python-reqs
+
+    make run                    launch network-video-recorder
+    make run-native             run native application (no docker)
+    make run-native-no-show     run native applicaiton (no docker) w/o video output
+endef
+export PROJECT_HELP_MSG
+
+SHELL = /bin/bash
+VENV = .venv
+PYTHON = $(VENV)/bin/python3
+PIP = $(VENV)/bin/pip
+IMAGE = person-detection-app
+
+help:
+	echo "$$PROJECT_HELP_MSG" | less
+
+
+$(VENV): $(VENV)/touchfile
+
+$(VENV)/touchfile: requirements.txt
+	test -d $(VENV) || python3 -m venv $(VENV)
+	. $(VENV)/bin/activate; pip install -Ur requirements.txt
+	touch $(VENV)/touchfile
+
+$(VENV)/bin/activate: requirements.txt
+	python3 -m venv $(VENV)
+	$(PIP) install -r requirements.txt
+
+build: $(VENV)
+	docker build -f docker/Dockerfile -t $(IMAGE) .
+
+run:
+	docker run -itu root:root --privileged --network host --name $(IMAGE) --rm $(IMAGE)
+
+run-native-test:
+	. ./person_detection.sh -i ./resources/run.mp4
+
+run-native:
+	. ./person_detection.sh -i gstreamer
+
+run-native-test-no-show:
+	. ./person_detection.sh -i ./resources/run.mp4 -ns
+
+run-native-no-show:
+	. ./person_detection.sh -i gstreamer -ns
 
 test:
+
+
+CLEANUP = *.pyc $(VENV)
+clean:
+	rm -rf ${CLEANUP}
+
+.PHONY: run clean
+
diff --git a/README.md b/README.md
index 8485038..94e9c2a 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,25 @@
 # Persion-detection Applicaiton
 
 person-detection is a demo application that runs in the Aether edge and showcases Aether's support for for low-latency, AI/ML driven IOT applications that make use of Aether's support for end-to-end slicing.
+
+## Build base image
+```
+git clone git@github.com:openvinotoolkit/docker_ci.git
+cd docker_ci
+python3 docker_openvino.py build -d cpu -os ubuntu18 --distribution runtime --product_version 2021.4
+```
+
+## Build person-detection
+```
+make build
+```
+
+## Start RPi camera
+```
+gst-launch-1.0 rpicamsrc bitrate=1000000 ! video/x-raw,width=640,height=480,framerate=25/1,profile=baseline ! jpegenc ! rtpjpegpay ! udpsink host=10.128.99.49 port=5000
+```
+
+## Start persion-detection
+```
+make run
+```
diff --git a/docker/Dockerfile b/docker/Dockerfile
new file mode 100644
index 0000000..6fcc485
--- /dev/null
+++ b/docker/Dockerfile
@@ -0,0 +1,27 @@
+#python3 docker_openvino.py build -d cpu -os ubuntu18 --distribution runtime --product_version 2021.4
+FROM ubuntu18_runtime:2021.4
+
+USER root
+
+RUN apt-get update && apt-get install -y \
+   wget \
+   unzip \
+   libglib2.0-0 \
+   libsm6 \
+   libxrender1 \
+   libxext6 \
+   vim \
+   ffmpeg \
+   x11-apps
+
+RUN apt-get install -y libgstreamer1.0-dev libgstreamer-plugins-base1.0-dev libgstreamer-plugins-bad1.0-dev gstreamer1.0-plugins-base gstreamer1.0-plugins-good gstreamer1.0-plugins-bad gstreamer1.0-plugins-ugly gstreamer1.0-libav gstreamer1.0-doc gstreamer1.0-tools gstreamer1.0-x gstreamer1.0-alsa gstreamer1.0-gl gstreamer1.0-gtk3 gstreamer1.0-qt5 gstreamer1.0-pulseaudio
+
+WORKDIR /var/person-detection-app
+
+COPY . .
+
+RUN . .venv/bin/activate && \
+     pip3 install -r requirements.txt
+
+ENTRYPOINT ["./person_detection.sh"]
+CMD ["-i gstreamer",  "-ns"]
diff --git a/person_detection.sh b/person_detection.sh
new file mode 100755
index 0000000..a58351c
--- /dev/null
+++ b/person_detection.sh
@@ -0,0 +1,5 @@
+#!/bin/bash
+args="$@"
+source .venv/bin/activate
+source /opt/intel/openvino/bin/setupvars.sh
+python3 person_detection/person_detection.py -d CPU -m ./resources/models/intel/person-detection-retail-0013/FP32/person-detection-retail-0013.xml $args
diff --git a/person_detection/person_detection.py b/person_detection/person_detection.py
new file mode 100644
index 0000000..037734c
--- /dev/null
+++ b/person_detection/person_detection.py
@@ -0,0 +1,273 @@
+"""
+SPDX-FileCopyrightText: 2020-present Open Networking Foundation <info@opennetworking.org>
+SPDX-License-Identifier: LicenseRef-ONF-Member-1.01
+"""
+
+from __future__ import print_function
+
+import logging as log
+import os
+import sys
+import time
+from argparse import ArgumentParser, SUPPRESS
+
+import cv2
+from imutils import build_montages
+from openvino.inference_engine import IECore
+
+
+def build_argparser():
+    parser = ArgumentParser(add_help=False)
+    args = parser.add_argument_group('Options')
+    args.add_argument('-h', '--help', action='help', default=SUPPRESS, help='Show this help message and exit.')
+    args.add_argument("-m", "--model", help="Required. Path to an .xml file with a trained model.",
+                      required=True, type=str)
+    args.add_argument("-i", "--input",
+                      help="Required. Path to video file or image. 'cam' for capturing video stream from camera",
+                      required=True, type=str)
+    # args.add_argument("-i2", "--input2",
+    #                   help="Optional. Path to second video file or image. 'cam' for capturing video stream from camera",
+    #                   default=None, type=str)
+    args.add_argument("-l", "--cpu_extension",
+                      help="Optional. Required for CPU custom layers. Absolute path to a shared library with the "
+                           "kernels implementations.", type=str, default=None)
+    args.add_argument("-pp", "--plugin_dir", help="Optional. Path to a plugin folder", type=str, default=None)
+    args.add_argument("-d", "--device",
+                      help="Optional. Specify the target device to infer on; CPU, GPU, FPGA, HDDL or MYRIAD is "
+                           "acceptable. The demo will look for a suitable plugin for device specified. "
+                           "Default value is CPU", default="CPU", type=str)
+    args.add_argument("--labels", help="Optional. Path to labels mapping file", default=None, type=str)
+    args.add_argument("-pt", "--prob_threshold", help="Optional. Probability threshold for detections filtering",
+                      default=0.5, type=float)
+    args.add_argument("-ns", help='No show output', action='store_true')
+
+    return parser
+
+
+def main():
+    log.basicConfig(format="[ %(levelname)s ] %(message)s", level=log.INFO, stream=sys.stdout)
+    args = build_argparser().parse_args()
+    model_xml = args.model
+    model_bin = os.path.splitext(model_xml)[0] + ".bin"
+    # Plugin initialization for specified device and load extensions library if specified
+    log.info("Initializing plugin for {} device...".format(args.device))
+    # plugin = IEPlugin(device=args.device, plugin_dirs=args.plugin_dir)
+    # if args.cpu_extension and 'CPU' in args.device:
+    #   plugin.add_cpu_extension(args.cpu_extension)
+    # Read IR
+    log.info("Reading IR...")
+    net = IECore().read_network(model=model_xml, weights=model_bin)
+
+    assert len(net.inputs.keys()) == 1, "Demo supports only single input topologies"
+    assert len(net.outputs) == 1, "Demo supports only single output topologies"
+    input_blob = next(iter(net.inputs))
+    out_blob = next(iter(net.outputs))
+
+    # input_blob2 = next(iter(net.inputs))
+    # out_blob2 = next(iter(net.outputs))
+
+    log.info("Loading IR to the plugin...")
+    # exec_net = IECore().load_network(network=net, device_name=args.device, num_requests=2)
+    exec_net = IECore().load_network(network=net, device_name=args.device, num_requests=1)
+    # Read and pre-process input image
+    n, c, h, w = net.inputs[input_blob].shape
+    # n2, c2, h2, w2 = net.inputs[input_blob2].shape
+    del net
+    if args.input == 'cam':
+        input_stream = 0
+    elif args.input == 'gstreamer':
+        # gst rtp sink
+        input_stream = 'udpsrc port=5000 caps = " application/x-rtp, encoding-name=JPEG,payload=26" ! rtpjpegdepay ! decodebin ! videoconvert ! appsink'
+        #input_stream = 'udpsrc port=5000 caps = "application/x-rtp, media=(string)video, clock-rate=(int)90000, encoding-name=(string)H264, payload=(int)96" ! rtph264depay ! decodebin ! videoconvert ! appsink'
+    else:
+        input_stream = args.input
+        assert os.path.isfile(args.input), "Specified input file doesn't exist"
+
+    if input_stream == 'gstreamer':
+        cap = cv2.VideoCapture(input_stream, cv2.CAP_GSTREAMER)
+    else:
+        cap = cv2.VideoCapture(input_stream)
+
+    # if args.input2 == 'cam':
+    #     input_stream2 = 0
+    # elif args.input2 == 'gstreamer':
+    #     input_stream2 = 'udpsrc port=5001 caps = " application/x-rtp, encoding-name=JPEG,payload=26" ! rtpjpegdepay ! decodebin ! videoconvert ! appsink'
+    # else:
+    #     input_stream2 = args.input2
+    #     assert os.path.isfile(args.input2), "Specified input file doesn't exist"
+    if args.labels:
+        with open(args.labels, 'r') as f:
+            labels_map = [x.strip() for x in f]
+    else:
+        labels_map = None
+
+    # if input_stream2 == 'gstreamer':
+    #     cap2 = cv2.VideoCapture(input_stream2, cv2.CAP_GSTREAMER)
+    # else:
+    #     cap2 = cv2.VideoCapture(input_stream2)
+
+    cur_request_id = 0
+    next_request_id = 1
+
+    # cur_request_id2 = 1
+    # next_request_id2 = 0
+
+    log.info("Starting inference in async mode...")
+    log.info("To switch between sync and async modes press Tab button")
+    log.info("To stop the demo execution press Esc button")
+
+    # Async doesn't work if True
+    # Request issues = Runtime Error: [REQUEST BUSY]
+    is_async_mode = False
+    render_time = 0
+    ret, frame = cap.read()
+    # ret2, frame2 = cap2.read()
+
+    # Montage width and height
+    # In this case means 2x1 boxes
+    mW = 2
+    mH = 1
+
+    frameList = []
+
+    print("To close the application, press 'CTRL+C' or any key with focus on the output window")
+    # while cap.isOpened() or cap2.isOpened():
+    while cap.isOpened():
+        if is_async_mode:
+            ret, next_frame = cap.read()
+            # ret2, next_frame2 = cap2.read()
+        else:
+            ret, frame = cap.read()
+            # ret2, frame2 = cap2.read()
+        #if not (ret and ret2):
+        if not ret:
+            break
+        initial_w = cap.get(3)
+        initial_h = cap.get(4)
+        # initial_w2 = cap2.get(3)
+        # initial_h2 = cap2.get(4)
+        # Main sync point:
+        # in the truly Async mode we start the NEXT infer request, while waiting for the CURRENT to complete
+        # in the regular mode we start the CURRENT request and immediately wait for it's completion
+        inf_start = time.time()
+        if is_async_mode:
+            # if ret and ret2:
+            if ret:
+                in_frame = cv2.resize(next_frame, (w, h))
+                in_frame = in_frame.transpose((2, 0, 1))  # Change data layout from HWC to CHW
+                in_frame = in_frame.reshape((n, c, h, w))
+                exec_net.start_async(request_id=next_request_id, inputs={input_blob: in_frame})
+
+                # in_frame2 = cv2.resize(next_frame2, (w2, h2))
+                # in_frame2 = in_frame2.transpose((2, 0, 1))  # Change data layout from HWC to CHW
+                # in_frame2 = in_frame2.reshape((n2, c2, h2, w2))
+                # exec_net.start_async(request_id=next_request_id2, inputs={input_blob2: in_frame2})
+
+        else:
+            # if (ret and ret2):
+            if ret:
+                in_frame = cv2.resize(frame, (w, h))
+                in_frame = in_frame.transpose((2, 0, 1))  # Change data layout from HWC to CHW
+                in_frame = in_frame.reshape((n, c, h, w))
+                exec_net.start_async(request_id=cur_request_id, inputs={input_blob: in_frame})
+
+                # in_frame2 = cv2.resize(frame2, (w2, h2))
+                # in_frame2 = in_frame2.transpose((2, 0, 1))  # Change data layout from HWC to CHW
+                # in_frame2 = in_frame2.reshape((n2, c2, h2, w2))
+                # exec_net.start_async(request_id=cur_request_id2, inputs={input_blob2: in_frame2})
+
+        # if exec_net.requests[cur_request_id].wait(-1) == 0 and exec_net.requests[cur_request_id2].wait(-1) == 0:
+        if exec_net.requests[cur_request_id].wait(-1) == 0:
+            inf_end = time.time()
+            det_time = inf_end - inf_start
+
+            # Parse detection results of the current request
+            res = exec_net.requests[cur_request_id].outputs[out_blob]
+            # res2 = exec_net.requests[cur_request_id2].outputs[out_blob2]
+
+            for obj in res[0][0]:
+                # Draw only objects when probability more than specified threshold
+                if obj[2] > args.prob_threshold:
+                    xmin = int(obj[3] * initial_w)
+                    ymin = int(obj[4] * initial_h)
+                    xmax = int(obj[5] * initial_w)
+                    ymax = int(obj[6] * initial_h)
+                    class_id = int(obj[1])
+                    # Draw box and label\class_id
+                    color = (min(class_id * 12.5, 255), min(class_id * 7, 255), min(class_id * 5, 255))
+                    cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), color, 2)
+                    det_label = labels_map[class_id] if labels_map else str(class_id)
+                    cv2.putText(frame, det_label + ' ' + str(round(obj[2] * 100, 1)) + ' %', (xmin, ymin - 7),
+                                cv2.FONT_HERSHEY_COMPLEX, 0.6, color, 1)
+                    print('Object detected, class_id:', class_id, 'probability:', obj[2], 'xmin:', xmin, 'ymin:', ymin,
+                          'xmax:', xmax, 'ymax:', ymax)
+
+            # for obj in res2[0][0]:
+            #     # Draw only objects when probability more than specified threshold
+            #     if obj[2] > args.prob_threshold:
+            #         xmin = int(obj[3] * initial_w2)
+            #         ymin = int(obj[4] * initial_h2)
+            #         xmax = int(obj[5] * initial_w2)
+            #         ymax = int(obj[6] * initial_h2)
+            #         class_id = int(obj[1])
+            #         # Draw box and label\class_id
+            #         color = (min(class_id * 12.5, 255), min(class_id * 7, 255), min(class_id * 5, 255))
+            #         cv2.rectangle(frame2, (xmin, ymin), (xmax, ymax), color, 2)
+            #         det_label = labels_map[class_id] if labels_map else str(class_id)
+            #         cv2.putText(frame2, det_label + ' ' + str(round(obj[2] * 100, 1)) + ' %', (xmin, ymin - 7),
+            #                     cv2.FONT_HERSHEY_COMPLEX, 0.6, color, 1)
+            #         print('Object detected, class_id:', class_id, 'probability:', obj[2], 'xmin:', xmin, 'ymin:', ymin,
+            #               'xmax:', xmax, 'ymax:', ymax)
+
+            # Draw performance stats
+            inf_time_message = "Inference time: Not applicable for async mode" if is_async_mode else \
+                "Inference time: {:.3f} ms".format(det_time * 1000)
+            render_time_message = "OpenCV rendering time: {:.3f} ms".format(render_time * 1000)
+            if is_async_mode:
+                async_mode_message = "Async mode is on. Processing request {}".format(cur_request_id)
+            else:
+                async_mode_message = "Async mode is off. Processing request {}".format(cur_request_id)
+
+            cv2.putText(frame, inf_time_message, (15, 15), cv2.FONT_HERSHEY_COMPLEX, 0.5, (200, 10, 10), 1)
+            cv2.putText(frame, render_time_message, (15, 30), cv2.FONT_HERSHEY_COMPLEX, 0.5, (10, 10, 200), 1)
+            cv2.putText(frame, async_mode_message, (10, int(initial_h - 20)), cv2.FONT_HERSHEY_COMPLEX, 0.5,
+                        (10, 10, 200), 1)
+
+            # cv2.putText(frame2, inf_time_message, (15, 15), cv2.FONT_HERSHEY_COMPLEX, 0.5, (200, 10, 10), 1)
+            # cv2.putText(frame2, render_time_message, (15, 30), cv2.FONT_HERSHEY_COMPLEX, 0.5, (10, 10, 200), 1)
+            # cv2.putText(frame2, async_mode_message, (10, int(initial_h - 20)), cv2.FONT_HERSHEY_COMPLEX, 0.5,
+            #             (10, 10, 200), 1)
+
+        render_start = time.time()
+
+        if not args.ns:
+            # if ret and ret2:
+            if ret:
+            #     frameList.append(frame)
+            #     # frameList.append(frame2)
+            # montages = build_montages(frameList, (640, 480), (mW, mH))
+            # for montage in montages:
+            #     cv2.imshow("Detection results", montage)
+                cv2.imshow("Detection results", frame)
+            render_end = time.time()
+            render_time = render_end - render_start
+
+        if is_async_mode:
+            cur_request_id, next_request_id = next_request_id, cur_request_id
+
+            frame = next_frame
+            # frame2 = next_frame2
+        key = cv2.waitKey(1)
+        if key == 27:
+            break
+        if 9 == key:
+            is_async_mode = not is_async_mode
+            log.info("Switched to {} mode".format("async" if is_async_mode else "sync"))
+
+    cap.release()
+    # cap2.release()
+    cv2.destroyAllWindows()
+
+
+if __name__ == '__main__':
+    sys.exit(main() or 0)
diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..9d6d5da
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,2 @@
+imutils==0.5.4
+numpy==1.19.5
diff --git a/resources/models/intel/person-detection-retail-0013/FP16-INT8/person-detection-retail-0013.bin b/resources/models/intel/person-detection-retail-0013/FP16-INT8/person-detection-retail-0013.bin
new file mode 100644
index 0000000..dec950a
--- /dev/null
+++ b/resources/models/intel/person-detection-retail-0013/FP16-INT8/person-detection-retail-0013.bin
Binary files differ
diff --git a/resources/models/intel/person-detection-retail-0013/FP16-INT8/person-detection-retail-0013.xml b/resources/models/intel/person-detection-retail-0013/FP16-INT8/person-detection-retail-0013.xml
new file mode 100644
index 0000000..7bf3027
--- /dev/null
+++ b/resources/models/intel/person-detection-retail-0013/FP16-INT8/person-detection-retail-0013.xml
@@ -0,0 +1,35067 @@
+<?xml version="1.0" ?>
+<net name="ResMobNet_v4 (LReLU) with single SSD head" version="10">
+	<layers>
+		<layer id="0" name="3284328819830" type="Const" version="opset1">
+			<data element_type="f16" offset="0" shape="" size="2"/>
+			<output>
+				<port id="0" precision="FP16"/>
+			</output>
+		</layer>
+		<layer id="1" name="3285328922773" type="Const" version="opset1">
+			<data element_type="f16" offset="2" shape="" size="2"/>
+			<output>
+				<port id="0" precision="FP16"/>
+			</output>
+		</layer>
+		<layer id="2" name="3286329020898" type="Const" version="opset1">
+			<data element_type="f16" offset="0" shape="" size="2"/>
+			<output>
+				<port id="0" precision="FP16"/>
+			</output>
+		</layer>
+		<layer id="3" name="3287329120439" type="Const" version="opset1">
+			<data element_type="f16" offset="2" shape="" size="2"/>
+			<output>
+				<port id="0" precision="FP16"/>
+			</output>
+		</layer>
+		<layer id="4" name="4284428822554" type="Const" version="opset1">
+			<data element_type="f16" offset="4" shape="" size="2"/>
+			<output>
+				<port id="0" precision="FP16"/>
+			</output>
+		</layer>
+		<layer id="5" name="4285428922101" type="Const" version="opset1">
+			<data element_type="f16" offset="6" shape="" size="2"/>
+			<output>
+				<port id="0" precision="FP16"/>
+			</output>
+		</layer>
+		<layer id="6" name="4286429021780" type="Const" version="opset1">
+			<data element_type="f16" offset="4" shape="" size="2"/>
+			<output>
+				<port id="0" precision="FP16"/>
+			</output>
+		</layer>
+		<layer id="7" name="4287429120886" type="Const" version="opset1">
+			<data element_type="f16" offset="6" shape="" size="2"/>
+			<output>
+				<port id="0" precision="FP16"/>
+			</output>
+		</layer>
+		<layer id="8" name="5444544820604" type="Const" version="opset1">
+			<data element_type="f16" offset="8" shape="" size="2"/>
+			<output>
+				<port id="0" precision="FP16"/>
+			</output>
+		</layer>
+		<layer id="9" name="5445544919815" type="Const" version="opset1">
+			<data element_type="f16" offset="10" shape="" size="2"/>
+			<output>
+				<port id="0" precision="FP16"/>
+			</output>
+		</layer>
+		<layer id="10" name="5446545020175" type="Const" version="opset1">
+			<data element_type="f16" offset="8" shape="" size="2"/>
+			<output>
+				<port id="0" precision="FP16"/>
+			</output>
+		</layer>
+		<layer id="11" name="5447545122041" type="Const" version="opset1">
+			<data element_type="f16" offset="10" shape="" size="2"/>
+			<output>
+				<port id="0" precision="FP16"/>
+			</output>
+		</layer>
+		<layer id="12" name="2964296819803" type="Const" version="opset1">
+			<data element_type="f16" offset="12" shape="" size="2"/>
+			<output>
+				<port id="0" precision="FP16"/>
+			</output>
+		</layer>
+		<layer id="13" name="2965296922185" type="Const" version="opset1">
+			<data element_type="f16" offset="14" shape="" size="2"/>
+			<output>
+				<port id="0" precision="FP16"/>
+			</output>
+		</layer>
+		<layer id="14" name="2966297020562" type="Const" version="opset1">
+			<data element_type="f16" offset="12" shape="" size="2"/>
+			<output>
+				<port id="0" precision="FP16"/>
+			</output>
+		</layer>
+		<layer id="15" name="2967297122368" type="Const" version="opset1">
+			<data element_type="f16" offset="14" shape="" size="2"/>
+			<output>
+				<port id="0" precision="FP16"/>
+			</output>
+		</layer>
+		<layer id="16" name="5084508822452" type="Const" version="opset1">
+			<data element_type="f16" offset="16" shape="" size="2"/>
+			<output>
+				<port id="0" precision="FP16"/>
+			</output>
+		</layer>
+		<layer id="17" name="5085508920694" type="Const" version="opset1">
+			<data element_type="f16" offset="18" shape="" size="2"/>
+			<output>
+				<port id="0" precision="FP16"/>
+			</output>
+		</layer>
+		<layer id="18" name="5086509022689" type="Const" version="opset1">
+			<data element_type="f16" offset="16" shape="" size="2"/>
+			<output>
+				<port id="0" precision="FP16"/>
+			</output>
+		</layer>
+		<layer id="19" name="5087509122584" type="Const" version="opset1">
+			<data element_type="f16" offset="18" shape="" size="2"/>
+			<output>
+				<port id="0" precision="FP16"/>
+			</output>
+		</layer>
+		<layer id="20" name="2704270821135" type="Const" version="opset1">
+			<data element_type="f16" offset="20" shape="" size="2"/>
+			<output>
+				<port id="0" precision="FP16"/>
+			</output>
+		</layer>
+		<layer id="21" name="2705270921897" type="Const" version="opset1">
+			<data element_type="f16" offset="22" shape="" size="2"/>
+			<output>
+				<port id="0" precision="FP16"/>
+			</output>
+		</layer>
+		<layer id="22" name="2706271019395" type="Const" version="opset1">
+			<data element_type="f16" offset="20" shape="" size="2"/>
+			<output>
+				<port id="0" precision="FP16"/>
+			</output>
+		</layer>
+		<layer id="23" name="2707271122200" type="Const" version="opset1">
+			<data element_type="f16" offset="22" shape="" size="2"/>
+			<output>
+				<port id="0" precision="FP16"/>
+			</output>
+		</layer>
+		<layer id="24" name="3644364819641" type="Const" version="opset1">
+			<data element_type="f16" offset="24" shape="" size="2"/>
+			<output>
+				<port id="0" precision="FP16"/>
+			</output>
+		</layer>
+		<layer id="25" name="3645364922491" type="Const" version="opset1">
+			<data element_type="f16" offset="26" shape="" size="2"/>
+			<output>
+				<port id="0" precision="FP16"/>
+			</output>
+		</layer>
+		<layer id="26" name="3646365020877" type="Const" version="opset1">
+			<data element_type="f16" offset="24" shape="" size="2"/>
+			<output>
+				<port id="0" precision="FP16"/>
+			</output>
+		</layer>
+		<layer id="27" name="3647365121072" type="Const" version="opset1">
+			<data element_type="f16" offset="26" shape="" size="2"/>
+			<output>
+				<port id="0" precision="FP16"/>
+			</output>
+		</layer>
+		<layer id="28" name="4264426819806" type="Const" version="opset1">
+			<data element_type="f16" offset="28" shape="" size="2"/>
+			<output>
+				<port id="0" precision="FP16"/>
+			</output>
+		</layer>
+		<layer id="29" name="4265426922113" type="Const" version="opset1">
+			<data element_type="f16" offset="30" shape="" size="2"/>
+			<output>
+				<port id="0" precision="FP16"/>
+			</output>
+		</layer>
+		<layer id="30" name="4266427022503" type="Const" version="opset1">
+			<data element_type="f16" offset="28" shape="" size="2"/>
+			<output>
+				<port id="0" precision="FP16"/>
+			</output>
+		</layer>
+		<layer id="31" name="4267427119686" type="Const" version="opset1">
+			<data element_type="f16" offset="30" shape="" size="2"/>
+			<output>
+				<port id="0" precision="FP16"/>
+			</output>
+		</layer>
+		<layer id="32" name="3544354821201" type="Const" version="opset1">
+			<data element_type="f16" offset="32" shape="" size="2"/>
+			<output>
+				<port id="0" precision="FP16"/>
+			</output>
+		</layer>
+		<layer id="33" name="3545354922467" type="Const" version="opset1">
+			<data element_type="f16" offset="34" shape="" size="2"/>
+			<output>
+				<port id="0" precision="FP16"/>
+			</output>
+		</layer>
+		<layer id="34" name="3546355019728" type="Const" version="opset1">
+			<data element_type="f16" offset="32" shape="" size="2"/>
+			<output>
+				<port id="0" precision="FP16"/>
+			</output>
+		</layer>
+		<layer id="35" name="3547355122629" type="Const" version="opset1">
+			<data element_type="f16" offset="34" shape="" size="2"/>
+			<output>
+				<port id="0" precision="FP16"/>
+			</output>
+		</layer>
+		<layer id="36" name="4184418820538" type="Const" version="opset1">
+			<data element_type="f16" offset="36" shape="" size="2"/>
+			<output>
+				<port id="0" precision="FP16"/>
+			</output>
+		</layer>
+		<layer id="37" name="4185418922371" type="Const" version="opset1">
+			<data element_type="f16" offset="38" shape="" size="2"/>
+			<output>
+				<port id="0" precision="FP16"/>
+			</output>
+		</layer>
+		<layer id="38" name="4186419021999" type="Const" version="opset1">
+			<data element_type="f16" offset="36" shape="" size="2"/>
+			<output>
+				<port id="0" precision="FP16"/>
+			</output>
+		</layer>
+		<layer id="39" name="4187419121210" type="Const" version="opset1">
+			<data element_type="f16" offset="38" shape="" size="2"/>
+			<output>
+				<port id="0" precision="FP16"/>
+			</output>
+		</layer>
+		<layer id="40" name="2684268821042" type="Const" version="opset1">
+			<data element_type="f16" offset="40" shape="" size="2"/>
+			<output>
+				<port id="0" precision="FP16"/>
+			</output>
+		</layer>
+		<layer id="41" name="2685268921624" type="Const" version="opset1">
+			<data element_type="f16" offset="42" shape="" size="2"/>
+			<output>
+				<port id="0" precision="FP16"/>
+			</output>
+		</layer>
+		<layer id="42" name="2686269019476" type="Const" version="opset1">
+			<data element_type="f16" offset="40" shape="" size="2"/>
+			<output>
+				<port id="0" precision="FP16"/>
+			</output>
+		</layer>
+		<layer id="43" name="2687269119869" type="Const" version="opset1">
+			<data element_type="f16" offset="42" shape="" size="2"/>
+			<output>
+				<port id="0" precision="FP16"/>
+			</output>
+		</layer>
+		<layer id="44" name="3964396821630" type="Const" version="opset1">
+			<data element_type="f16" offset="44" shape="" size="2"/>
+			<output>
+				<port id="0" precision="FP16"/>
+			</output>
+		</layer>
+		<layer id="45" name="3965396921000" type="Const" version="opset1">
+			<data element_type="f16" offset="46" shape="" size="2"/>
+			<output>
+				<port id="0" precision="FP16"/>
+			</output>
+		</layer>
+		<layer id="46" name="3966397020868" type="Const" version="opset1">
+			<data element_type="f16" offset="44" shape="" size="2"/>
+			<output>
+				<port id="0" precision="FP16"/>
+			</output>
+		</layer>
+		<layer id="47" name="3967397119716" type="Const" version="opset1">
+			<data element_type="f16" offset="46" shape="" size="2"/>
+			<output>
+				<port id="0" precision="FP16"/>
+			</output>
+		</layer>
+		<layer id="48" name="3104310821618" type="Const" version="opset1">
+			<data element_type="f16" offset="48" shape="" size="2"/>
+			<output>
+				<port id="0" precision="FP16"/>
+			</output>
+		</layer>
+		<layer id="49" name="3105310921432" type="Const" version="opset1">
+			<data element_type="f16" offset="50" shape="" size="2"/>
+			<output>
+				<port id="0" precision="FP16"/>
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+			<data auto_broadcast="numpy"/>
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+			<data auto_broadcast="numpy"/>
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+			<data auto_broadcast="numpy" levels="256"/>
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+	</edges>
+	<meta_data>
+		<MO_version value="custom_HEAD_149c43044cb1e8ed8cd4f3f196b23f7b3f129a36"/>
+		<cli_parameters>
+			<caffe_parser_path value="DIR"/>
+			<data_type value="FP16"/>
+			<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>
+	<quantization_parameters>
+		<config>{
+		'compression': {
+			'algorithms': [
+				{
+					'name': 'DefaultQuantization',
+					'params': {
+						'num_samples_for_tuning': 2000,
+						'preset': 'performance',
+						'stat_subset_size': 300,
+						'use_layerwise_tuning': false
+					}
+				}
+			],
+			'dump_intermediate_model': true,
+			'target_device': 'ANY'
+		},
+		'engine': {
+			'models': [
+				{
+					'name': 'person-detection-retail-0013',
+					'launchers': [
+						{
+							'framework': 'dlsdk',
+							'adapter': 'ssd',
+							'device': 'CPU'
+						}
+					],
+					'datasets': [
+						{
+							'name': 'person_detection',
+							'data_source': 'PATH',
+							'annotation_conversion': {
+								'converter': 'datatang_street_subway',
+								'annotation_file': 'PATH'
+							},
+							'annotation': 'PATH',
+							'dataset_meta': 'PATH',
+							'preprocessing': [
+								{
+									'type': 'resize',
+									'dst_width': 544,
+									'dst_height': 320
+								}
+							],
+							'postprocessing': [
+								{
+									'type': 'resize_prediction_boxes'
+								},
+								{
+									'type': 'filter',
+									'apply_to': 'annotation',
+									'height_range': 100,
+									'aspect_ratio': '0.666, 5',
+									'is_empty': true,
+									'min_visibility': 'partially occluded'
+								},
+								{
+									'type': 'filter',
+									'apply_to': 'prediction',
+									'height_range': 100,
+									'is_empty': true,
+									'aspect_ratio': '0.666, 5'
+								}
+							],
+							'metrics': [
+								{
+									'type': 'map',
+									'ignore_difficult': true,
+									'include_boundaries': true,
+									'allow_multiple_matches_per_ignored': false,
+									'distinct_conf': false
+								}
+							],
+							'_command_line_mapping': {
+								'annotation_file': 'PATH'
+							}
+						}
+					]
+				}
+			],
+			'stat_requests_number': null,
+			'eval_requests_number': null,
+			'type': 'accuracy_checker'
+		}
+	}</config>
+		<version value="1.0"/>
+		<cli_params value="{'quantize': None, 'preset': None, 'model': None, 'weights': None, 'name': None, 'ac_config': None, 'max_drop': None, 'evaluate': False, 'output_dir': 'PATH', 'direct_dump': True, 'log_level': 'INFO', 'pbar': False, 'stream_output': False, 'keep_uncompressed_weights': False}"/>
+	</quantization_parameters>
+</net>
diff --git a/resources/models/intel/person-detection-retail-0013/FP16/person-detection-retail-0013.bin b/resources/models/intel/person-detection-retail-0013/FP16/person-detection-retail-0013.bin
new file mode 100644
index 0000000..ddc38c5
--- /dev/null
+++ b/resources/models/intel/person-detection-retail-0013/FP16/person-detection-retail-0013.bin
Binary files differ
diff --git a/resources/models/intel/person-detection-retail-0013/FP16/person-detection-retail-0013.xml b/resources/models/intel/person-detection-retail-0013/FP16/person-detection-retail-0013.xml
new file mode 100644
index 0000000..2d45fca
--- /dev/null
+++ b/resources/models/intel/person-detection-retail-0013/FP16/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="f16" shape="1, 3, 320, 544"/>
+			<output>
+				<port id="0" names="data" precision="FP16">
+					<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="f16" offset="0" shape="1, 3, 1, 1" size="6"/>
+			<output>
+				<port id="0" precision="FP16">
+					<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="FP16">
+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>320</dim>
+					<dim>544</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<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="f16" offset="6" shape="1, 3, 1, 1" size="6"/>
+			<output>
+				<port id="0" precision="FP16">
+					<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="FP16">
+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>320</dim>
+					<dim>544</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="data/norm/bn" precision="FP16">
+					<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="f16" offset="12" shape="32, 3, 3, 3" size="1728"/>
+			<output>
+				<port id="0" precision="FP16">
+					<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="FP16">
+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>320</dim>
+					<dim>544</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>32</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<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="f16" offset="1740" shape="1, 32, 1, 1" size="64"/>
+			<output>
+				<port id="0" precision="FP16">
+					<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="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>160</dim>
+					<dim>272</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<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="FP16">
+					<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="FP16">
+					<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="FP16">
+					<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="f16" offset="1804" shape="8, 32, 1, 1" size="512"/>
+			<output>
+				<port id="0" precision="FP16">
+					<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="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>160</dim>
+					<dim>272</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>8</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<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="f16" offset="2316" shape="1, 8, 1, 1" size="16"/>
+			<output>
+				<port id="0" precision="FP16">
+					<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="FP16">
+					<dim>1</dim>
+					<dim>8</dim>
+					<dim>160</dim>
+					<dim>272</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<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="FP16">
+					<dim>1</dim>
+					<dim>8</dim>
+					<dim>160</dim>
+					<dim>272</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="14" name="bottleneck1_1/dim_red/fn/weights3098439866" type="Const" version="opset1">
+			<data element_type="f32" offset="2332" 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="FP16">
+					<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="FP16">
+					<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="f16" offset="2336" shape="8, 1, 1, 3, 3" size="144"/>
+			<output>
+				<port id="0" precision="FP16">
+					<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="FP16">
+					<dim>1</dim>
+					<dim>8</dim>
+					<dim>160</dim>
+					<dim>272</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>8</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<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="f16" offset="2480" shape="1, 8, 1, 1" size="16"/>
+			<output>
+				<port id="0" precision="FP16">
+					<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="FP16">
+					<dim>1</dim>
+					<dim>8</dim>
+					<dim>160</dim>
+					<dim>272</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<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="FP16">
+					<dim>1</dim>
+					<dim>8</dim>
+					<dim>160</dim>
+					<dim>272</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="20" name="bottleneck1_1/inner/dw1/fn/weights3117639995" type="Const" version="opset1">
+			<data element_type="f32" offset="2332" 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="FP16">
+					<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="FP16">
+					<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="f16" offset="2496" shape="32, 8, 1, 1" size="512"/>
+			<output>
+				<port id="0" precision="FP16">
+					<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="FP16">
+					<dim>1</dim>
+					<dim>8</dim>
+					<dim>160</dim>
+					<dim>272</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>32</dim>
+					<dim>8</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<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="f16" offset="3008" shape="1, 32, 1, 1" size="64"/>
+			<output>
+				<port id="0" precision="FP16">
+					<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="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>160</dim>
+					<dim>272</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<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="FP16">
+					<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="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>160</dim>
+					<dim>272</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>160</dim>
+					<dim>272</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="bottleneck1_1/add" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>160</dim>
+					<dim>272</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="27" name="bottleneck1_1/fn/weights3117240232" type="Const" version="opset1">
+			<data element_type="f32" offset="2332" 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="FP16">
+					<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="FP16">
+					<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="f16" offset="3072" shape="8, 32, 1, 1" size="512"/>
+			<output>
+				<port id="0" precision="FP16">
+					<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="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>160</dim>
+					<dim>272</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>8</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<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="f16" offset="3584" shape="1, 8, 1, 1" size="16"/>
+			<output>
+				<port id="0" precision="FP16">
+					<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="FP16">
+					<dim>1</dim>
+					<dim>8</dim>
+					<dim>160</dim>
+					<dim>272</dim>
+				</port>
+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>8</dim>
+					<dim>1</dim>
+					<dim>1</dim>
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+			</input>
+			<output>
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+					<dim>1</dim>
+					<dim>8</dim>
+					<dim>160</dim>
+					<dim>272</dim>
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+			</output>
+		</layer>
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+				<port id="0" precision="FP32">
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+		</layer>
+		<layer id="34" name="bottleneck1_2/dim_red/fn" type="PReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
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+				<port id="1" precision="FP32">
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+					<dim>1</dim>
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+					<dim>160</dim>
+					<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">
+			<data element_type="f16" offset="3600" shape="8, 1, 1, 3, 3" size="144"/>
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+					<dim>3</dim>
+					<dim>3</dim>
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+			</output>
+		</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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+				<port id="0" precision="FP16">
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+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
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+		<layer id="39" name="bottleneck1_2/inner/dw1/fn/weights3080440244" type="Const" version="opset1">
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+				<port id="0" precision="FP16">
+					<dim>1</dim>
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+				<port id="1" precision="FP32">
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+					<dim>272</dim>
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+		<layer id="41" name="bottleneck1_2/dim_inc/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f16" offset="3760" shape="32, 8, 1, 1" size="512"/>
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+					<dim>1</dim>
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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="FP16">
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+					<dim>1</dim>
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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="FP16">
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+				<port id="0" precision="FP16">
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+			<output>
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+					<dim>1</dim>
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+		<layer id="48" name="bottleneck1_3/dim_red/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
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+		<layer id="49" name="bottleneck1_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"/>
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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="FP16">
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+					<dim>1</dim>
+					<dim>8</dim>
+					<dim>160</dim>
+					<dim>272</dim>
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+				<port id="0" precision="FP16">
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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="FP16">
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+					<dim>8</dim>
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+					<dim>272</dim>
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+				<port id="0" precision="FP16">
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+					<dim>160</dim>
+					<dim>272</dim>
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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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+					<dim>160</dim>
+					<dim>272</dim>
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+				<port id="0" precision="FP16">
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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>160</dim>
+					<dim>272</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="67" name="bottleneck1_4/dim_red/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f16" offset="5600" shape="8, 32, 1, 1" size="512"/>
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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"/>
+			<input>
+				<port id="0" precision="FP16">
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+					<dim>1</dim>
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+					<dim>1</dim>
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+					<dim>160</dim>
+					<dim>272</dim>
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+			</output>
+		</layer>
+		<layer id="69" name="data_add_2372923734" type="Const" version="opset1">
+			<data element_type="f16" offset="6112" shape="1, 8, 1, 1" size="16"/>
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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="FP16">
+					<dim>1</dim>
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+					<dim>1</dim>
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+			</input>
+			<output>
+				<port id="2" names="bottleneck1_4/dim_red/conv" precision="FP16">
+					<dim>1</dim>
+					<dim>8</dim>
+					<dim>160</dim>
+					<dim>272</dim>
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+			</output>
+		</layer>
+		<layer id="71" name="bottleneck1_4/dim_red/fn/weights3110439965" type="Const" version="opset1">
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+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+		</layer>
+		<layer id="72" name="bottleneck1_4/dim_red/fn" type="PReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP16">
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+				<port id="1" precision="FP32">
+					<dim>1</dim>
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+			<output>
+				<port id="2" names="bottleneck1_4/dim_red/conv" precision="FP16">
+					<dim>1</dim>
+					<dim>8</dim>
+					<dim>160</dim>
+					<dim>272</dim>
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+			</output>
+		</layer>
+		<layer id="73" name="bottleneck1_4/inner/dw1/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f16" offset="6128" shape="8, 1, 1, 3, 3" size="144"/>
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+					<dim>3</dim>
+					<dim>3</dim>
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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"/>
+			<input>
+				<port id="0" precision="FP16">
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+					<dim>1</dim>
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+					<dim>272</dim>
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+		<layer id="75" name="data_add_2373723742" type="Const" version="opset1">
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+			<output>
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+		<layer id="76" name="bottleneck1_4/inner/dw1/bn/variance/Fused_Add_" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
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+					<dim>1</dim>
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+					<dim>1</dim>
+					<dim>8</dim>
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+					<dim>272</dim>
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+			</output>
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+		<layer id="77" name="bottleneck1_4/inner/dw1/fn/weights3102040547" type="Const" version="opset1">
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+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+		<layer id="78" name="bottleneck1_4/inner/dw1/fn" type="PReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP16">
+					<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="bottleneck1_4/inner/dw1/conv" precision="FP16">
+					<dim>1</dim>
+					<dim>8</dim>
+					<dim>160</dim>
+					<dim>272</dim>
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+		</layer>
+		<layer id="79" name="bottleneck1_4/dim_inc/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f16" offset="6288" shape="32, 8, 1, 1" size="512"/>
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+					<dim>1</dim>
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+			</output>
+		</layer>
+		<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"/>
+			<input>
+				<port id="0" precision="FP16">
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+					<dim>272</dim>
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+					<dim>1</dim>
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+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
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+			<output>
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+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>160</dim>
+					<dim>272</dim>
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+			</output>
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+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
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+					<dim>1</dim>
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+					<dim>272</dim>
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+			</input>
+			<output>
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+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>160</dim>
+					<dim>272</dim>
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+			</output>
+		</layer>
+		<layer id="84" name="bottleneck1_4/fn/weights3094440088" type="Const" version="opset1">
+			<data element_type="f32" offset="2332" shape="1" size="4"/>
+			<output>
+				<port id="0" precision="FP32">
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+		<layer id="85" name="bottleneck1_4/fn" type="PReLU" version="opset1">
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+				<port id="0" precision="FP16">
+					<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_4/add" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>160</dim>
+					<dim>272</dim>
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+		<layer id="86" name="bottleneck2_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"/>
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+					<dim>160</dim>
+					<dim>272</dim>
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+			<output>
+				<port id="1" names="bottleneck2_0/skip/pooling" precision="FP16">
+					<dim>1</dim>
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+					<dim>80</dim>
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+		<layer id="87" name="bottleneck2_0/skip/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f16" offset="6864" shape="64, 32, 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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+				<port id="0" precision="FP16">
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+					<dim>1</dim>
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+					<dim>80</dim>
+					<dim>136</dim>
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+			<output>
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+					<dim>1</dim>
+					<dim>1</dim>
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+		</layer>
+		<layer id="90" name="bottleneck2_0/skip/bn/variance/Fused_Add_" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
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+				<port id="2" names="bottleneck2_0/skip/conv" precision="FP16">
+					<dim>1</dim>
+					<dim>64</dim>
+					<dim>80</dim>
+					<dim>136</dim>
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+			</output>
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+		<layer id="91" name="bottleneck2_0/dim_red/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f16" offset="11088" shape="16, 32, 1, 1" size="1024"/>
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+					<dim>1</dim>
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+		</layer>
+		<layer id="92" name="bottleneck2_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="FP16">
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+					<dim>1</dim>
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+					<dim>1</dim>
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+					<dim>160</dim>
+					<dim>272</dim>
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+					<dim>1</dim>
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+		</layer>
+		<layer id="94" name="bottleneck2_0/dim_red/bn/variance/Fused_Add_" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
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+			<output>
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+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>160</dim>
+					<dim>272</dim>
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+		<layer id="95" name="bottleneck2_0/dim_red/fn/weights3105640580" type="Const" version="opset1">
+			<data element_type="f32" offset="2332" shape="1" size="4"/>
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+		</layer>
+		<layer id="96" name="bottleneck2_0/dim_red/fn" type="PReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
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+				<port id="1" precision="FP32">
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+			<output>
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+					<dim>1</dim>
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+					<dim>160</dim>
+					<dim>272</dim>
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+		</layer>
+		<layer id="98" name="bottleneck2_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"/>
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+				<port id="0" precision="FP16">
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+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>80</dim>
+					<dim>136</dim>
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+		<layer id="100" name="bottleneck2_0/inner/dw1/bn/variance/Fused_Add_" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
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+			</output>
+		</layer>
+		<layer id="101" name="bottleneck2_0/inner/dw1/fn/weights3113240199" type="Const" version="opset1">
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+				<port id="0" precision="FP32">
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+		</layer>
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+				<port id="0" precision="FP16">
+					<dim>1</dim>
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+					<dim>136</dim>
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+			</output>
+		</layer>
+		<layer id="103" name="bottleneck2_0/dim_inc/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f16" offset="12464" shape="64, 16, 1, 1" size="2048"/>
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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"/>
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+			<data auto_broadcast="numpy"/>
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+				<port id="0" precision="FP16">
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+			</output>
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+			<data auto_broadcast="numpy"/>
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+				<port id="0" precision="FP16">
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+		</layer>
+		<layer id="108" name="bottleneck2_0/fn/weights3100839908" type="Const" version="opset1">
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+				<port id="0" precision="FP32">
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+				<port id="0" precision="FP16">
+					<dim>1</dim>
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+		</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"/>
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+			<data auto_broadcast="numpy"/>
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+				<port id="0" precision="FP16">
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+				<port id="0" precision="FP16">
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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"/>
+			<input>
+				<port id="0" precision="FP16">
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+				<port id="0" precision="FP16">
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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="FP16">
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+					<dim>64</dim>
+					<dim>80</dim>
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+				<port id="0" precision="FP16">
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+				<port id="1" precision="FP32">
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+			<data auto_broadcast="numpy"/>
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+				<port id="0" precision="FP16">
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+					<dim>136</dim>
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+					<dim>1</dim>
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+					<dim>80</dim>
+					<dim>136</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="135" name="bottleneck2_2/inner/dw1/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f16" offset="21296" shape="16, 1, 1, 3, 3" size="288"/>
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+					<dim>3</dim>
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+			</output>
+		</layer>
+		<layer id="136" name="bottleneck2_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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+				<port id="0" precision="FP16">
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+					<dim>136</dim>
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+			</output>
+		</layer>
+		<layer id="137" name="data_add_2381723822" type="Const" version="opset1">
+			<data element_type="f16" offset="21584" shape="1, 16, 1, 1" size="32"/>
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+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="138" name="bottleneck2_2/inner/dw1/bn/variance/Fused_Add_" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
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+					<dim>1</dim>
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+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>80</dim>
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+		<layer id="139" name="bottleneck2_2/inner/dw1/fn/weights3105240634" type="Const" version="opset1">
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+				<port id="0" precision="FP16">
+					<dim>1</dim>
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+		<layer id="141" name="bottleneck2_2/dim_inc/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
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+					<dim>1</dim>
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+		</layer>
+		<layer id="142" name="bottleneck2_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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+					<dim>1</dim>
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+		<layer id="144" name="bottleneck2_2/dim_inc/bn/variance/Fused_Add_" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
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+				<port id="0" precision="FP16">
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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="FP16">
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+					<dim>1</dim>
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+		<layer id="146" name="bottleneck2_2/fn/weights3115240121" type="Const" version="opset1">
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+			<output>
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+				<port id="0" precision="FP16">
+					<dim>1</dim>
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+				<port id="1" precision="FP32">
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+					<dim>1</dim>
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+					<dim>1</dim>
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+		</layer>
+		<layer id="149" name="bottleneck2_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"/>
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+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
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+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>80</dim>
+					<dim>136</dim>
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+		<layer id="152" name="bottleneck2_3/dim_red/fn/weights3114840157" type="Const" version="opset1">
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+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
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+				<port id="1" precision="FP32">
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+					<dim>16</dim>
+					<dim>80</dim>
+					<dim>136</dim>
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+			<data element_type="f16" offset="25872" shape="16, 1, 1, 3, 3" size="288"/>
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+		<layer id="155" name="bottleneck2_3/inner/dw1/conv" type="GroupConvolution" version="opset1">
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+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
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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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+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
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+				<port id="1" precision="FP32">
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+				<port id="2" names="bottleneck2_3/inner/dw1/conv" precision="FP16">
+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>80</dim>
+					<dim>136</dim>
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+			</output>
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+		<layer id="160" name="bottleneck2_3/dim_inc/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f16" offset="26192" shape="64, 16, 1, 1" size="2048"/>
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+					<dim>1</dim>
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+			</output>
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+		<layer id="161" name="bottleneck2_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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+					<dim>1</dim>
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+					<dim>80</dim>
+					<dim>136</dim>
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+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
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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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+				<port id="0" precision="FP16">
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+				<port id="0" precision="FP16">
+					<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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+					<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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+			</input>
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+					<dim>80</dim>
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+				</port>
+			</output>
+		</layer>
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+				<port id="0" precision="FP32">
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+				<port id="0" precision="FP16">
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+					<dim>1</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="f16" offset="37520" shape="16, 64, 1, 1" size="2048"/>
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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="FP16">
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+			<data auto_broadcast="numpy"/>
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+				<port id="0" precision="FP16">
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+					<dim>80</dim>
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+				<port id="0" precision="FP16">
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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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+			<data auto_broadcast="numpy"/>
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+				<port id="0" precision="FP16">
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+			<input>
+				<port id="0" precision="FP16">
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+				<port id="1" precision="FP32">
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+					<dim>136</dim>
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+			<data element_type="f16" offset="39920" shape="64, 16, 1, 1" size="2048"/>
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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"/>
+			<input>
+				<port id="0" precision="FP16">
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+					<dim>136</dim>
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+				<port id="0" precision="FP16">
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+				<port id="0" precision="FP16">
+					<dim>1</dim>
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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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+			<data auto_broadcast="numpy"/>
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+				<port id="0" precision="FP16">
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+					<dim>1</dim>
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+					<dim>80</dim>
+					<dim>136</dim>
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+			<data element_type="f32" offset="2332" shape="1" size="4"/>
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+				<port id="0" precision="FP16">
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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="FP16">
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+				<port id="0" precision="FP16">
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+				<port id="1" precision="FP32">
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+					<dim>16</dim>
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+					<dim>136</dim>
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+			</output>
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+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="237" name="bottleneck2_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="FP16">
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+					<dim>80</dim>
+					<dim>136</dim>
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+				<port id="1" precision="FP16">
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+					<dim>1</dim>
+					<dim>1</dim>
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+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>64</dim>
+					<dim>80</dim>
+					<dim>136</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="238" name="data_add_2394523950" type="Const" version="opset1">
+			<data element_type="f16" offset="46544" shape="1, 64, 1, 1" size="128"/>
+			<output>
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+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="239" name="bottleneck2_7/dim_inc/bn/variance/Fused_Add_" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
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+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="bottleneck2_7/dim_inc/conv" precision="FP16">
+					<dim>1</dim>
+					<dim>64</dim>
+					<dim>80</dim>
+					<dim>136</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="240" name="bottleneck2_7/add" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
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+				<port id="2" names="bottleneck2_7/add" precision="FP16">
+					<dim>1</dim>
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+					<dim>136</dim>
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+		</layer>
+		<layer id="241" name="bottleneck2_7/fn/weights3107639704" type="Const" version="opset1">
+			<data element_type="f32" offset="2332" shape="1" size="4"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="242" name="bottleneck2_7/fn" type="PReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
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+				<port id="1" precision="FP32">
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+				</port>
+			</input>
+			<output>
+				<port id="2" names="bottleneck2_7/add" precision="FP16">
+					<dim>1</dim>
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+		</layer>
+		<layer id="243" name="bottleneck2_8/dim_red/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f16" offset="46672" shape="16, 64, 1, 1" size="2048"/>
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+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="244" name="bottleneck2_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="FP16">
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+					<dim>1</dim>
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+					<dim>136</dim>
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+			</output>
+		</layer>
+		<layer id="245" name="data_add_2395323958" type="Const" version="opset1">
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+			<output>
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+					<dim>1</dim>
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+			</output>
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+		<layer id="246" name="bottleneck2_8/dim_red/bn/variance/Fused_Add_" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
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+					<dim>1</dim>
+					<dim>16</dim>
+					<dim>80</dim>
+					<dim>136</dim>
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+			</output>
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+		<layer id="247" name="bottleneck2_8/dim_red/fn/weights3090040466" type="Const" version="opset1">
+			<data element_type="f32" offset="2332" shape="1" size="4"/>
+			<output>
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+					<dim>1</dim>
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+		</layer>
+		<layer id="248" name="bottleneck2_8/dim_red/fn" type="PReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP16">
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+				<port id="1" precision="FP32">
+					<dim>1</dim>
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+			<output>
+				<port id="2" names="bottleneck2_8/dim_red/conv" precision="FP16">
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+					<dim>80</dim>
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+			<data element_type="f16" offset="48752" shape="16, 1, 1, 3, 3" size="288"/>
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+					<dim>3</dim>
+					<dim>3</dim>
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+		</layer>
+		<layer id="250" name="bottleneck2_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"/>
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+				<port id="0" precision="FP16">
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+			<data auto_broadcast="numpy"/>
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+				<port id="0" precision="FP16">
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+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
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+				<port id="1" precision="FP32">
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+					<dim>80</dim>
+					<dim>136</dim>
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+		<layer id="255" name="bottleneck2_8/dim_inc/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f16" offset="49072" shape="64, 16, 1, 1" size="2048"/>
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+					<dim>1</dim>
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+		<layer id="256" name="bottleneck2_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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+			<data auto_broadcast="numpy"/>
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+				<port id="0" precision="FP16">
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+				<port id="0" precision="FP16">
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+					<dim>1</dim>
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+					<dim>80</dim>
+					<dim>136</dim>
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+		<layer id="260" name="bottleneck2_8/fn/weights3112840481" type="Const" version="opset1">
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+				<port id="0" precision="FP16">
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+				<port id="1" precision="FP32">
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+			<output>
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+			<data auto_broadcast="numpy"/>
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+				<port id="0" precision="FP16">
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+			<data auto_broadcast="numpy"/>
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+				<port id="0" precision="FP16">
+					<dim>1</dim>
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+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="bottleneck3_0/dim_red/conv" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>80</dim>
+					<dim>136</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="271" name="bottleneck3_0/dim_red/fn/weights3096440007" type="Const" version="opset1">
+			<data element_type="f32" offset="2332" 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="FP16">
+					<dim>1</dim>
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+					<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="FP16">
+					<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="f16" offset="72048" shape="32, 1, 1, 3, 3" size="576"/>
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+				<port id="0" precision="FP16">
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+					<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="FP16">
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+					<dim>136</dim>
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+				<port id="1" precision="FP16">
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+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>3</dim>
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+			<output>
+				<port id="2" precision="FP16">
+					<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="f16" offset="72624" shape="1, 32, 1, 1" size="64"/>
+			<output>
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+					<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="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
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+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
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+			</input>
+			<output>
+				<port id="2" names="bottleneck3_0/inner/dw1/conv" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="277" name="bottleneck3_0/inner/dw1/fn/weights3084840604" type="Const" version="opset1">
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+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="278" name="bottleneck3_0/inner/dw1/fn" type="PReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP16">
+					<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_0/inner/dw1/conv" precision="FP16">
+					<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="f16" offset="72688" shape="128, 32, 1, 1" size="8192"/>
+			<output>
+				<port id="0" precision="FP16">
+					<dim>128</dim>
+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
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+			</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="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
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+				<port id="1" precision="FP16">
+					<dim>128</dim>
+					<dim>32</dim>
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+					<dim>1</dim>
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+			</input>
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+				<port id="2" precision="FP16">
+					<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="f16" offset="80880" shape="1, 128, 1, 1" size="256"/>
+			<output>
+				<port id="0" precision="FP16">
+					<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="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
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+				<port id="1" precision="FP16">
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+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
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+			</input>
+			<output>
+				<port id="2" names="bottleneck3_0/dim_inc/conv" precision="FP16">
+					<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="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
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+					<dim>68</dim>
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+				<port id="1" precision="FP16">
+					<dim>1</dim>
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+					<dim>68</dim>
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+			</input>
+			<output>
+				<port id="2" names="bottleneck3_0/add" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="284" name="bottleneck3_0/fn/weights3096040034" type="Const" version="opset1">
+			<data element_type="f32" offset="2332" shape="1" size="4"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="285" name="bottleneck3_0/fn" type="PReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
+					<dim>128</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_0/add" precision="FP16">
+					<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="f16" offset="81136" shape="32, 128, 1, 1" size="8192"/>
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+					<dim>1</dim>
+					<dim>1</dim>
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+			</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="FP16">
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+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
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+				<port id="1" precision="FP16">
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+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
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+			<output>
+				<port id="2" precision="FP16">
+					<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="f16" offset="89328" shape="1, 32, 1, 1" size="64"/>
+			<output>
+				<port id="0" precision="FP16">
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+					<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="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
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+				<port id="1" precision="FP16">
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+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
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+			</input>
+			<output>
+				<port id="2" names="bottleneck3_1/dim_red/conv" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
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+			</output>
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+		<layer id="290" name="bottleneck3_1/dim_red/fn/weights3089640577" type="Const" version="opset1">
+			<data element_type="f32" offset="2332" shape="1" size="4"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="291" name="bottleneck3_1/dim_red/fn" type="PReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP16">
+					<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_1/dim_red/conv" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
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+			</output>
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+		<layer id="292" name="bottleneck3_1/inner/dw1/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f16" offset="89392" shape="32, 1, 1, 3, 3" size="576"/>
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+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>3</dim>
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+		</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="FP16">
+					<dim>1</dim>
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+					<dim>68</dim>
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+				<port id="1" precision="FP16">
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+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>3</dim>
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+			<output>
+				<port id="2" precision="FP16">
+					<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="f16" offset="89968" shape="1, 32, 1, 1" size="64"/>
+			<output>
+				<port id="0" precision="FP16">
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+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
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+			</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="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+				<port id="1" precision="FP16">
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+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
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+			</input>
+			<output>
+				<port id="2" names="bottleneck3_1/inner/dw1/conv" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
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+			</output>
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+		<layer id="296" name="bottleneck3_1/inner/dw1/fn/weights3095639668" type="Const" version="opset1">
+			<data element_type="f32" offset="2332" shape="1" size="4"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="297" name="bottleneck3_1/inner/dw1/fn" type="PReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP16">
+					<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_1/inner/dw1/conv" precision="FP16">
+					<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="f16" offset="90032" shape="128, 32, 1, 1" size="8192"/>
+			<output>
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+					<dim>32</dim>
+					<dim>1</dim>
+					<dim>1</dim>
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+			</output>
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+		<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="FP16">
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+					<dim>68</dim>
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+				<port id="1" precision="FP16">
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+					<dim>1</dim>
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+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
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+			<data element_type="f16" offset="98224" shape="1, 128, 1, 1" size="256"/>
+			<output>
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+					<dim>1</dim>
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+			</output>
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+		<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="FP16">
+					<dim>1</dim>
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+					<dim>40</dim>
+					<dim>68</dim>
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+				<port id="1" precision="FP16">
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+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
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+			<output>
+				<port id="2" names="bottleneck3_1/dim_inc/conv" precision="FP16">
+					<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="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
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+					<dim>68</dim>
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+				<port id="1" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
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+			</input>
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+				<port id="2" names="bottleneck3_1/add" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="303" name="bottleneck3_1/fn/weights3088040013" type="Const" version="opset1">
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+			<output>
+				<port id="0" precision="FP32">
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+			</output>
+		</layer>
+		<layer id="304" name="bottleneck3_1/fn" type="PReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP16">
+					<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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+				<port id="2" names="bottleneck3_1/add" precision="FP16">
+					<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="f16" offset="98480" shape="32, 128, 1, 1" size="8192"/>
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+					<dim>1</dim>
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+			</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="FP16">
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+					<dim>128</dim>
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+					<dim>1</dim>
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+			</input>
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+				<port id="2" precision="FP16">
+					<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="f16" offset="106672" shape="1, 32, 1, 1" size="64"/>
+			<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="FP16">
+					<dim>1</dim>
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+					<dim>1</dim>
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+			</input>
+			<output>
+				<port id="2" names="bottleneck3_2/dim_red/conv" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="309" name="bottleneck3_2/dim_red/fn/weights3082040079" type="Const" version="opset1">
+			<data element_type="f32" offset="2332" 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="FP16">
+					<dim>1</dim>
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+				<port id="1" precision="FP32">
+					<dim>1</dim>
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+			</input>
+			<output>
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+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
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+			<data element_type="f16" offset="106736" shape="32, 1, 1, 3, 3" size="576"/>
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+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</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"/>
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+				<port id="0" precision="FP16">
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+					<dim>3</dim>
+					<dim>3</dim>
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+				<port id="2" precision="FP16">
+					<dim>1</dim>
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+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="313" name="data_add_2404124046" type="Const" version="opset1">
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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="FP16">
+					<dim>1</dim>
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+				<port id="1" precision="FP16">
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+					<dim>1</dim>
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+			</input>
+			<output>
+				<port id="2" names="bottleneck3_2/inner/dw1/conv" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="315" name="bottleneck3_2/inner/dw1/fn/weights3081640064" type="Const" version="opset1">
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+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="316" name="bottleneck3_2/inner/dw1/fn" type="PReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
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+				<port id="1" precision="FP32">
+					<dim>1</dim>
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+			</input>
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+				<port id="2" names="bottleneck3_2/inner/dw1/conv" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="317" name="bottleneck3_2/dim_inc/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f16" offset="107376" shape="128, 32, 1, 1" size="8192"/>
+			<output>
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+					<dim>128</dim>
+					<dim>32</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="FP16">
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+					<dim>1</dim>
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+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
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+			</output>
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+			<data element_type="f16" offset="115568" shape="1, 128, 1, 1" size="256"/>
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+					<dim>1</dim>
+					<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="FP16">
+					<dim>1</dim>
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+				<port id="1" precision="FP16">
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+					<dim>1</dim>
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+			<output>
+				<port id="2" names="bottleneck3_2/dim_inc/conv" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="321" name="bottleneck3_2/add" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
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+				<port id="1" precision="FP16">
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+					<dim>40</dim>
+					<dim>68</dim>
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+			</input>
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+				<port id="2" names="bottleneck3_2/add" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="322" name="bottleneck3_2/fn/weights3088840037" type="Const" version="opset1">
+			<data element_type="f32" offset="2332" shape="1" size="4"/>
+			<output>
+				<port id="0" precision="FP32">
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+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
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+					<dim>68</dim>
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+				<port id="1" precision="FP32">
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+			</input>
+			<output>
+				<port id="2" names="bottleneck3_2/add" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="324" name="bottleneck3_3/dim_red/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f16" offset="115824" shape="32, 128, 1, 1" size="8192"/>
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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="FP16">
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+					<dim>68</dim>
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+				<port id="1" precision="FP16">
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+					<dim>128</dim>
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+					<dim>1</dim>
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+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="326" name="data_add_2405724062" type="Const" version="opset1">
+			<data element_type="f16" offset="124016" shape="1, 32, 1, 1" size="64"/>
+			<output>
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+					<dim>32</dim>
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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="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
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+				<port id="1" precision="FP16">
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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="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="328" name="bottleneck3_3/dim_red/fn/weights3081240370" type="Const" version="opset1">
+			<data element_type="f32" offset="2332" shape="1" size="4"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="329" name="bottleneck3_3/dim_red/fn" type="PReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP16">
+					<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_3/dim_red/conv" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="330" name="bottleneck3_3/inner/dw1/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f16" offset="124080" shape="32, 1, 1, 3, 3" size="576"/>
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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="FP16">
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+					<dim>68</dim>
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+					<dim>3</dim>
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+				<port id="2" precision="FP16">
+					<dim>1</dim>
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+					<dim>40</dim>
+					<dim>68</dim>
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+					<dim>1</dim>
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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="FP16">
+					<dim>1</dim>
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+				<port id="1" precision="FP16">
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+					<dim>1</dim>
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+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
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+				<port id="0" precision="FP16">
+					<dim>1</dim>
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+				<port id="1" precision="FP32">
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+			<output>
+				<port id="2" names="bottleneck3_3/inner/dw1/conv" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
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+			<data element_type="f16" offset="124720" shape="128, 32, 1, 1" size="8192"/>
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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_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
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+					<dim>40</dim>
+					<dim>68</dim>
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+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="339" name="bottleneck3_3/dim_inc/bn/variance/Fused_Add_" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
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+					<dim>1</dim>
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+			</input>
+			<output>
+				<port id="2" names="bottleneck3_3/dim_inc/conv" precision="FP16">
+					<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="FP16">
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+				<port id="2" names="bottleneck3_3/add" precision="FP16">
+					<dim>1</dim>
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+				</port>
+			</output>
+		</layer>
+		<layer id="341" name="bottleneck3_3/fn/weights3096840265" type="Const" version="opset1">
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+			<output>
+				<port id="0" precision="FP32">
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+		</layer>
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+				<port id="0" precision="FP16">
+					<dim>1</dim>
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+					<dim>1</dim>
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+				<port id="2" names="bottleneck3_3/add" precision="FP16">
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+		</layer>
+		<layer id="343" name="bottleneck3_4/dim_red/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
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+		</layer>
+		<layer id="344" name="bottleneck3_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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+				<port id="0" precision="FP16">
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+					<dim>1</dim>
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+			<data auto_broadcast="numpy"/>
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+				<port id="0" precision="FP16">
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+					<dim>68</dim>
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+				<port id="0" precision="FP16">
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+			<output>
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+					<dim>68</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="FP16">
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+				<port id="0" precision="FP16">
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+				<port id="1" 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="FP16">
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+					<dim>68</dim>
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+				<port id="1" precision="FP32">
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+			<data auto_broadcast="numpy"/>
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+				<port id="0" precision="FP16">
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+			<data auto_broadcast="numpy"/>
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+		</layer>
+		<layer id="374" name="bottleneck3_5/dim_inc/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
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+		</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"/>
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+			<data auto_broadcast="numpy"/>
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+				<port id="0" precision="FP16">
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+				<port id="0" precision="FP16">
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+			<data auto_broadcast="numpy"/>
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+				<port id="0" precision="FP16">
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+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
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+					<dim>68</dim>
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+					<dim>68</dim>
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+			<data auto_broadcast="numpy"/>
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+				<port id="0" precision="FP16">
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+					<dim>68</dim>
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+				<port id="0" precision="FP16">
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+				<port id="0" precision="FP16">
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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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+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</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="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+				<port id="1" precision="FP16">
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+					<dim>1</dim>
+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" precision="FP16">
+					<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="f16" offset="194032" shape="1, 32, 1, 1" size="64"/>
+			<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="FP16">
+					<dim>1</dim>
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+					<dim>1</dim>
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+			</input>
+			<output>
+				<port id="2" names="bottleneck3_7/inner/dw1/conv" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
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+		<layer id="410" name="bottleneck3_7/inner/dw1/fn/weights3104039701" type="Const" version="opset1">
+			<data element_type="f32" offset="2332" 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="FP16">
+					<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/inner/dw1/conv" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<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="f16" offset="194096" shape="128, 32, 1, 1" size="8192"/>
+			<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="FP16">
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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="f16" offset="202288" shape="1, 128, 1, 1" size="256"/>
+			<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="FP16">
+					<dim>1</dim>
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+				<port id="1" precision="FP16">
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+					<dim>1</dim>
+					<dim>1</dim>
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+			</input>
+			<output>
+				<port id="2" names="bottleneck3_7/dim_inc/conv" precision="FP16">
+					<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="FP16">
+					<dim>1</dim>
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+				<port id="1" precision="FP16">
+					<dim>1</dim>
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+					<dim>68</dim>
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+			</input>
+			<output>
+				<port id="2" names="bottleneck3_7/add" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="417" name="bottleneck3_7/fn/weights3079640682" type="Const" version="opset1">
+			<data element_type="f32" offset="2332" shape="1" size="4"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+		</layer>
+		<layer id="418" name="bottleneck3_7/fn" type="PReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP16">
+					<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_7/add" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="419" name="bottleneck3_8/dim_red/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f16" offset="202544" shape="32, 128, 1, 1" size="8192"/>
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+					<dim>1</dim>
+					<dim>1</dim>
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+			</output>
+		</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="FP16">
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+					<dim>40</dim>
+					<dim>68</dim>
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+				<port id="1" precision="FP16">
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+					<dim>128</dim>
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+					<dim>1</dim>
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+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
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+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
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+			<data element_type="f16" offset="210736" shape="1, 32, 1, 1" size="64"/>
+			<output>
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+					<dim>1</dim>
+					<dim>1</dim>
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+			</output>
+		</layer>
+		<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="FP16">
+					<dim>1</dim>
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+					<dim>68</dim>
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+				<port id="1" precision="FP16">
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+					<dim>1</dim>
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+			</input>
+			<output>
+				<port id="2" names="bottleneck3_8/dim_red/conv" precision="FP16">
+					<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/weights3112439755" type="Const" version="opset1">
+			<data element_type="f32" offset="2332" shape="1" size="4"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="424" name="bottleneck3_8/dim_red/fn" type="PReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP16">
+					<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_8/dim_red/conv" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="425" name="bottleneck3_8/inner/dw1/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f16" offset="210800" shape="32, 1, 1, 3, 3" size="576"/>
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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="FP16">
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+					<dim>68</dim>
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+				<port id="1" precision="FP16">
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+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>3</dim>
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+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
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+			<data element_type="f16" offset="211376" shape="1, 32, 1, 1" size="64"/>
+			<output>
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+					<dim>1</dim>
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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="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
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+				<port id="1" precision="FP16">
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+					<dim>1</dim>
+					<dim>1</dim>
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+			</input>
+			<output>
+				<port id="2" names="bottleneck3_8/inner/dw1/conv" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="429" name="bottleneck3_8/inner/dw1/fn/weights3092839893" type="Const" version="opset1">
+			<data element_type="f32" offset="2332" 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="FP16">
+					<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="FP16">
+					<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="f16" offset="211440" shape="128, 32, 1, 1" size="8192"/>
+			<output>
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+					<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="FP16">
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+					<dim>40</dim>
+					<dim>68</dim>
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+				<port id="1" precision="FP16">
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+					<dim>32</dim>
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+					<dim>1</dim>
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+			<output>
+				<port id="2" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
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+			<data element_type="f16" offset="219632" shape="1, 128, 1, 1" size="256"/>
+			<output>
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+					<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="FP16">
+					<dim>1</dim>
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+					<dim>68</dim>
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+				<port id="1" precision="FP16">
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+					<dim>1</dim>
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+			</input>
+			<output>
+				<port id="2" names="bottleneck3_8/dim_inc/conv" precision="FP16">
+					<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="FP16">
+					<dim>1</dim>
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+					<dim>68</dim>
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+				<port id="1" precision="FP16">
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+					<dim>68</dim>
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+			</input>
+			<output>
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+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
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+			<data element_type="f32" offset="2332" shape="1" size="4"/>
+			<output>
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+			</output>
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+			<input>
+				<port id="0" precision="FP16">
+					<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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+			</input>
+			<output>
+				<port id="2" names="bottleneck3_8/add" precision="FP16">
+					<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="f16" offset="219888" shape="32, 128, 1, 1" size="8192"/>
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+					<dim>1</dim>
+					<dim>1</dim>
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+			</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="FP16">
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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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+			</output>
+		</layer>
+		<layer id="441" name="bottleneck3_9/dim_red/bn/variance/Fused_Add_" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
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+					<dim>1</dim>
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+			</input>
+			<output>
+				<port id="2" names="bottleneck3_9/dim_red/conv" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
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+					<dim>68</dim>
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+			</output>
+		</layer>
+		<layer id="442" name="bottleneck3_9/dim_red/fn/weights3083240112" type="Const" version="opset1">
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+				<port id="0" precision="FP16">
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+			<output>
+				<port id="2" names="bottleneck3_9/dim_red/conv" precision="FP16">
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+					<dim>40</dim>
+					<dim>68</dim>
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+			</output>
+		</layer>
+		<layer id="444" name="bottleneck3_9/inner/dw1/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
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+		</layer>
+		<layer id="445" name="bottleneck3_9/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="FP16">
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+		<layer id="448" name="bottleneck3_9/inner/dw1/fn/weights3104840376" type="Const" version="opset1">
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+					<dim>1</dim>
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+				<port id="1" precision="FP32">
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+		<layer id="450" name="bottleneck3_9/dim_inc/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
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+		<layer id="451" name="bottleneck3_9/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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+					<dim>1</dim>
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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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+		<layer id="453" name="bottleneck3_9/dim_inc/bn/variance/Fused_Add_" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
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+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
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+				<port id="0" precision="FP16">
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+		<layer id="455" name="bottleneck3_9/fn/weights3094040340" type="Const" version="opset1">
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+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
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+				<port id="1" precision="FP32">
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+			<output>
+				<port id="2" names="bottleneck3_9/add" precision="FP16">
+					<dim>1</dim>
+					<dim>128</dim>
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+					<dim>68</dim>
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+		<layer id="457" name="bottleneck3_10/dim_red/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f16" offset="237232" shape="32, 128, 1, 1" size="8192"/>
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+					<dim>1</dim>
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+		<layer id="458" name="bottleneck3_10/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>68</dim>
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+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
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+					<dim>1</dim>
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+			</input>
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+				<port id="2" names="bottleneck3_10/dim_red/conv" precision="FP16">
+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
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+		<layer id="461" name="bottleneck3_10/dim_red/fn/weights3082839743" type="Const" version="opset1">
+			<data element_type="f32" offset="2332" shape="1" size="4"/>
+			<output>
+				<port id="0" precision="FP32">
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+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
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+				<port id="1" precision="FP32">
+					<dim>1</dim>
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+			<output>
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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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+			<data element_type="f16" offset="245488" shape="32, 1, 1, 3, 3" size="576"/>
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+					<dim>3</dim>
+					<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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+				<port id="0" precision="FP16">
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+					<dim>68</dim>
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+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
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+					<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="FP16">
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+				<port id="1" precision="FP32">
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+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
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+		<layer id="469" name="bottleneck3_10/dim_inc/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
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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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+					<dim>40</dim>
+					<dim>68</dim>
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+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
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+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
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+			</output>
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+			<data auto_broadcast="numpy"/>
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+				<port id="0" precision="FP16">
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+					<dim>40</dim>
+					<dim>68</dim>
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+			</output>
+		</layer>
+		<layer id="474" name="bottleneck3_10/fn/weights3077239977" type="Const" version="opset1">
+			<data element_type="f32" offset="2332" shape="1" size="4"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="475" name="bottleneck3_10/fn" type="PReLU" version="opset1">
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+				<port id="0" precision="FP16">
+					<dim>1</dim>
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+				<port id="1" precision="FP32">
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+				<port id="2" names="bottleneck3_10/add" precision="FP16">
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+			</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"/>
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+			</output>
+		</layer>
+		<layer id="477" name="bottleneck4_0/skip/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f16" offset="254576" shape="256, 128, 1, 1" size="65536"/>
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+					<dim>1</dim>
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+		</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"/>
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+				<port id="0" precision="FP16">
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+			</output>
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+					<dim>1</dim>
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+		<layer id="480" name="bottleneck4_0/skip/bn/variance/Fused_Add_" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
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+					<dim>1</dim>
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+					<dim>1</dim>
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+			</output>
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+		<layer id="481" name="bottleneck4_0/dim_red/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f16" offset="320624" shape="64, 128, 1, 1" size="16384"/>
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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"/>
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+		<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="FP16">
+					<dim>1</dim>
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+					<dim>1</dim>
+					<dim>64</dim>
+					<dim>40</dim>
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+		<layer id="485" name="bottleneck4_0/dim_red/fn/weights3101639809" type="Const" version="opset1">
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+			<output>
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+		<layer id="486" name="bottleneck4_0/dim_red/fn" type="PReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP16">
+					<dim>1</dim>
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+				<port id="1" precision="FP32">
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+			<output>
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+					<dim>1</dim>
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+					<dim>40</dim>
+					<dim>68</dim>
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+			<data element_type="f16" offset="337136" shape="64, 1, 1, 3, 3" size="1152"/>
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+					<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"/>
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+				<port id="0" precision="FP16">
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+		<layer id="489" name="data_add_2426524270" type="Const" version="opset1">
+			<data element_type="f16" offset="338288" shape="1, 64, 1, 1" size="128"/>
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+		<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="FP16">
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+					<dim>1</dim>
+					<dim>64</dim>
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+		<layer id="491" name="bottleneck4_0/inner/dw1/fn/weights3082440412" type="Const" version="opset1">
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+				<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="FP16">
+					<dim>1</dim>
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+				<port id="1" precision="FP32">
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+					<dim>64</dim>
+					<dim>20</dim>
+					<dim>34</dim>
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+			</output>
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+		<layer id="493" name="bottleneck4_0/dim_inc/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f16" offset="338416" shape="256, 64, 1, 1" size="32768"/>
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+					<dim>1</dim>
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+		</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"/>
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+				<port id="0" precision="FP16">
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+					<dim>1</dim>
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+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>20</dim>
+					<dim>34</dim>
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+			<data element_type="f16" offset="371184" shape="1, 256, 1, 1" size="512"/>
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+					<dim>1</dim>
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+			</output>
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+		<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="FP16">
+					<dim>1</dim>
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+					<dim>1</dim>
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+			<output>
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+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>20</dim>
+					<dim>34</dim>
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+			</output>
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+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
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+			<output>
+				<port id="2" names="bottleneck4_0/add" precision="FP16">
+					<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/weights3103639980" type="Const" version="opset1">
+			<data element_type="f32" offset="2332" shape="1" size="4"/>
+			<output>
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+				<port id="0" precision="FP16">
+					<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/add" precision="FP16">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>20</dim>
+					<dim>34</dim>
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+			</output>
+		</layer>
+		<layer id="500" name="bottleneck4_1/dim_red/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f16" offset="371696" shape="64, 256, 1, 1" size="32768"/>
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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"/>
+			<input>
+				<port id="0" precision="FP16">
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+					<dim>256</dim>
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+					<dim>1</dim>
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+					<dim>20</dim>
+					<dim>34</dim>
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+			</output>
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+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP16">
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+					<dim>1</dim>
+					<dim>64</dim>
+					<dim>20</dim>
+					<dim>34</dim>
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+			<input>
+				<port id="0" precision="FP16">
+					<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>64</dim>
+					<dim>20</dim>
+					<dim>34</dim>
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+			</output>
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+			<data element_type="f16" offset="404592" shape="64, 1, 1, 3, 3" size="1152"/>
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+					<dim>3</dim>
+					<dim>3</dim>
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+			</output>
+		</layer>
+		<layer id="507" name="bottleneck4_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="FP16">
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+					<dim>1</dim>
+					<dim>64</dim>
+					<dim>20</dim>
+					<dim>34</dim>
+				</port>
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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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+		</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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+			<data auto_broadcast="numpy"/>
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+			<data auto_broadcast="numpy"/>
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+		<edge from-layer="743" from-port="3" to-layer="744" to-port="0"/>
+	</edges>
+	<meta_data>
+		<MO_version value="2021.4.0-3827-c5b65f2cb1d-releases/2021/4"/>
+		<cli_parameters>
+			<caffe_parser_path value="DIR"/>
+			<data_type value="FP16"/>
+			<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>
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>
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+					<dim>1</dim>
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+			</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">
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+			</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"/>
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+		</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>
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+					<dim>1</dim>
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+			</input>
+			<output>
+				<port id="2" names="data/norm/bn" precision="FP32">
+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>320</dim>
+					<dim>544</dim>
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+			</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"/>
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+					<dim>3</dim>
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+		</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"/>
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+					<dim>3</dim>
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+				<port id="2" precision="FP32">
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+					<dim>160</dim>
+					<dim>272</dim>
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+			</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"/>
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+					<dim>1</dim>
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+		</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">
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+					<dim>1</dim>
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+					<dim>160</dim>
+					<dim>272</dim>
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+			</output>
+		</layer>
+		<layer id="9" name="init_block1/dim_inc/fn" type="ReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+					<dim>272</dim>
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+			<output>
+				<port id="1" names="init_block1/dim_inc/conv" precision="FP32">
+					<dim>1</dim>
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+					<dim>160</dim>
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+			</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"/>
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+					<dim>1</dim>
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+		</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">
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+					<dim>1</dim>
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+					<dim>272</dim>
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+		</layer>
+		<layer id="12" name="data_add_2365723662" type="Const" version="opset1">
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+					<dim>1</dim>
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+		</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>
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+			<output>
+				<port id="2" names="bottleneck1_1/dim_red/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>8</dim>
+					<dim>160</dim>
+					<dim>272</dim>
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+			</output>
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+			<output>
+				<port id="0" precision="FP32">
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+		</layer>
+		<layer id="15" name="bottleneck1_1/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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+			<output>
+				<port id="2" names="bottleneck1_1/dim_red/conv" precision="FP32">
+					<dim>1</dim>
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+					<dim>160</dim>
+					<dim>272</dim>
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+			</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"/>
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+					<dim>3</dim>
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+		</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"/>
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+				<port id="0" precision="FP32">
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+				<port id="2" precision="FP32">
+					<dim>1</dim>
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+					<dim>272</dim>
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+		<layer id="18" name="data_add_2366523670" type="Const" version="opset1">
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+			<output>
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+		<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>
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+					<dim>1</dim>
+					<dim>8</dim>
+					<dim>160</dim>
+					<dim>272</dim>
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+		<layer id="20" name="bottleneck1_1/inner/dw1/fn/weights3102439659" type="Const" version="opset1">
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+		<layer id="21" name="bottleneck1_1/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="bottleneck1_1/inner/dw1/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>8</dim>
+					<dim>160</dim>
+					<dim>272</dim>
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+		</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"/>
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+					<dim>1</dim>
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+		</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">
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+					<dim>1</dim>
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+					<dim>272</dim>
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+					<dim>1</dim>
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+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+			</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>
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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>
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+		<layer id="27" name="bottleneck1_1/fn/weights3115239677" type="Const" version="opset1">
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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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+			<output>
+				<port id="2" names="bottleneck1_1/add" precision="FP32">
+					<dim>1</dim>
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+					<dim>272</dim>
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+		<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"/>
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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"/>
+			<input>
+				<port id="0" precision="FP32">
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+					<dim>272</dim>
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+				<port id="0" precision="FP32">
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+					<dim>8</dim>
+					<dim>160</dim>
+					<dim>272</dim>
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+			</output>
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+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</output>
+		</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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+				<port id="2" precision="FP32">
+					<dim>1</dim>
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+					<dim>272</dim>
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+			</output>
+		</layer>
+		<layer id="37" name="data_add_2368923694" type="Const" version="opset1">
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+			</output>
+		</layer>
+		<layer id="38" name="bottleneck1_2/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>
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+				<port id="2" names="bottleneck1_2/inner/dw1/conv" precision="FP32">
+					<dim>1</dim>
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+		<layer id="39" name="bottleneck1_2/inner/dw1/fn/weights3087240085" type="Const" version="opset1">
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+				<port id="0" precision="FP32">
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+			</output>
+		</layer>
+		<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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+			</output>
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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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+					<dim>1</dim>
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+		<layer id="48" name="bottleneck1_3/dim_red/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f32" offset="8668" shape="8, 32, 1, 1" size="1024"/>
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+		</layer>
+		<layer id="49" name="bottleneck1_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"/>
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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">
+					<dim>1</dim>
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+					<dim>1</dim>
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+					<dim>160</dim>
+					<dim>272</dim>
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+		<layer id="52" name="bottleneck1_3/dim_red/fn/weights3092840502" type="Const" version="opset1">
+			<data element_type="f32" offset="4664" shape="1" size="4"/>
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+		<layer id="53" name="bottleneck1_3/dim_red/fn" type="PReLU" version="opset1">
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+				<port id="0" precision="FP32">
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+			<output>
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+					<dim>160</dim>
+					<dim>272</dim>
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+			<data element_type="f32" offset="9724" shape="8, 1, 1, 3, 3" size="288"/>
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+		</layer>
+		<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>
+					<dim>160</dim>
+					<dim>272</dim>
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+			</output>
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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>1</dim>
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+					<dim>160</dim>
+					<dim>272</dim>
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+			</output>
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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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+			</input>
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+				<port id="2" names="bottleneck1_4/dim_red/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>8</dim>
+					<dim>160</dim>
+					<dim>272</dim>
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+			</output>
+		</layer>
+		<layer id="71" name="bottleneck1_4/dim_red/fn/weights3114840622" type="Const" version="opset1">
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+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+		</layer>
+		<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>1</dim>
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+					<dim>160</dim>
+					<dim>272</dim>
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+			</output>
+		</layer>
+		<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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+			<data auto_broadcast="numpy"/>
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+				<port id="0" precision="FP32">
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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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+			<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="84" name="bottleneck1_4/fn/weights3087639962" type="Const" version="opset1">
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+				<port id="0" precision="FP32">
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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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+					<dim>80</dim>
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+		<layer id="87" name="bottleneck2_0/skip/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
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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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+		<layer id="90" name="bottleneck2_0/skip/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_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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+				<port id="1" precision="FP32">
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+					<dim>160</dim>
+					<dim>272</dim>
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+			<data auto_pad="explicit" dilations="1, 1" pads_begin="1, 1" pads_end="1, 1" strides="2, 2"/>
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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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+					<dim>136</dim>
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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"/>
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+				<port id="2" precision="FP32">
+					<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>
+				</port>
+			</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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+		</layer>
+		<layer id="108" name="bottleneck2_0/fn/weights3086440226" type="Const" version="opset1">
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+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+		<layer id="109" name="bottleneck2_0/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">
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+			<output>
+				<port id="2" names="bottleneck2_0/add" precision="FP32">
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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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+				<port id="2" precision="FP32">
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+					<dim>136</dim>
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+			</output>
+		</layer>
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+					<dim>1</dim>
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+		</layer>
+		<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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+			</output>
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+		<layer id="114" name="bottleneck2_1/dim_red/fn/weights3091240172" 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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+				<port id="0" precision="FP32">
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+				<port id="1" precision="FP32">
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+				<port id="2" names="bottleneck2_1/dim_red/conv" precision="FP32">
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+					<dim>80</dim>
+					<dim>136</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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+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
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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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+				<port id="1" precision="FP32">
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+					<dim>136</dim>
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+		<layer id="122" name="bottleneck2_1/dim_inc/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<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"/>
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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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+					<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>
+		</layer>
+		<layer id="126" name="bottleneck2_1/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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+				<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>
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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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+			<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>
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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_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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+			<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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+		</layer>
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+			<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>136</dim>
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+			</output>
+		</layer>
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+			</output>
+		</layer>
+		<layer id="208" name="bottleneck2_6/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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+					<dim>16</dim>
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+			</output>
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+		<layer id="209" name="bottleneck2_6/dim_red/fn/weights3097240064" type="Const" version="opset1">
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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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+			<data auto_broadcast="numpy"/>
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+				<port id="0" precision="FP32">
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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"/>
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+				<port id="0" precision="FP32">
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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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+					<dim>16</dim>
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+					<dim>136</dim>
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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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+			</input>
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+				<port id="2" names="bottleneck2_7/dim_inc/conv" precision="FP32">
+					<dim>1</dim>
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+					<dim>80</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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+		<layer id="241" name="bottleneck2_7/fn/weights3106040265" type="Const" version="opset1">
+			<data element_type="f32" offset="4664" shape="1" size="4"/>
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+				<port id="0" precision="FP32">
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+		<layer id="244" name="bottleneck2_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"/>
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+			<data auto_broadcast="numpy"/>
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+		<layer id="247" name="bottleneck2_8/dim_red/fn/weights3100840364" type="Const" version="opset1">
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+				<port id="0" precision="FP32">
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+			<data auto_broadcast="numpy"/>
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+		<layer id="255" name="bottleneck2_8/dim_inc/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f32" offset="98140" shape="64, 16, 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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+				<port id="1" precision="FP32">
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+					<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"/>
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+					<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">
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+					<dim>136</dim>
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+					<dim>3</dim>
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+				<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"/>
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+					<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>
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+					<dim>1</dim>
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+			</input>
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+				<port id="2" names="bottleneck3_0/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="277" name="bottleneck3_0/inner/dw1/fn/weights3079640607" type="Const" version="opset1">
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+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+			</output>
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+		<layer id="278" name="bottleneck3_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">
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+				<port id="2" names="bottleneck3_0/inner/dw1/conv" precision="FP32">
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+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
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+			</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"/>
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+					<dim>1</dim>
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+			</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"/>
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+				<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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+		</layer>
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+			<data element_type="f32" offset="161756" shape="1, 128, 1, 1" size="512"/>
+			<output>
+				<port id="0" precision="FP32">
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+					<dim>1</dim>
+					<dim>1</dim>
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+		<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>
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+					<dim>1</dim>
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+			<output>
+				<port id="2" names="bottleneck3_0/dim_inc/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
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+			</output>
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+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
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+				<port id="2" names="bottleneck3_0/add" precision="FP32">
+					<dim>1</dim>
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+					<dim>68</dim>
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+		<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">
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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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+				<port id="2" names="bottleneck3_0/add" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
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+					<dim>68</dim>
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+		<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"/>
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+					<dim>1</dim>
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+		</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"/>
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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">
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+					<dim>40</dim>
+					<dim>68</dim>
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+		</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>
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+					<dim>1</dim>
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+		<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>
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+				<port id="1" precision="FP32">
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+			<output>
+				<port id="2" names="bottleneck3_1/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="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>
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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">
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+			<output>
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+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
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+			<data element_type="f32" offset="178780" shape="32, 1, 1, 3, 3" size="1152"/>
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+					<dim>3</dim>
+					<dim>3</dim>
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+		</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">
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+				<port id="2" precision="FP32">
+					<dim>1</dim>
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+					<dim>68</dim>
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+		<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>
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+					<dim>1</dim>
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+		<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>
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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_1/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="296" name="bottleneck3_1/inner/dw1/fn/weights3082440517" type="Const" version="opset1">
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+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+		<layer id="297" name="bottleneck3_1/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_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>
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+					<dim>32</dim>
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+					<dim>1</dim>
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+			</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">
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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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+			<data element_type="f32" offset="196444" shape="1, 128, 1, 1" size="512"/>
+			<output>
+				<port id="0" precision="FP32">
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+					<dim>1</dim>
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+			</output>
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+		<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>
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+					<dim>40</dim>
+					<dim>68</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" names="bottleneck3_1/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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+					<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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+			<output>
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+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</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">
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+			</output>
+		</layer>
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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">
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+			</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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+					<dim>128</dim>
+					<dim>1</dim>
+					<dim>1</dim>
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+			</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"/>
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+				<port id="0" precision="FP32">
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+					<dim>1</dim>
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+			<output>
+				<port id="2" precision="FP32">
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+				</port>
+			</output>
+		</layer>
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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">
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+					<dim>68</dim>
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+			</output>
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+				<port id="0" precision="FP32">
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+					<dim>68</dim>
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+			</output>
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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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+			<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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+				<port id="0" precision="FP32">
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+			<data element_type="f32" offset="231644" shape="32, 128, 1, 1" size="16384"/>
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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"/>
+			<input>
+				<port id="0" precision="FP32">
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+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</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>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
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+			<data element_type="f32" offset="248156" shape="32, 1, 1, 3, 3" size="1152"/>
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+					<dim>3</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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+				<port id="0" precision="FP32">
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+					<dim>1</dim>
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+					<dim>68</dim>
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+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
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+					<dim>32</dim>
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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_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>40</dim>
+					<dim>68</dim>
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+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="341" name="bottleneck3_3/fn/weights3083640325" type="Const" version="opset1">
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+			<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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+					<dim>1</dim>
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+				<port id="2" names="bottleneck3_3/add" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="343" name="bottleneck3_4/dim_red/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f32" offset="266332" shape="32, 128, 1, 1" size="16384"/>
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+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="344" name="bottleneck3_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"/>
+			<input>
+				<port id="0" precision="FP32">
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+					<dim>1</dim>
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+		</layer>
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+		<layer id="346" name="bottleneck3_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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+					<dim>68</dim>
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+				<port id="0" precision="FP32">
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+			<output>
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+					<dim>1</dim>
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+					<dim>68</dim>
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+			<data element_type="f32" offset="282844" shape="32, 1, 1, 3, 3" size="1152"/>
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+					<dim>3</dim>
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+		</layer>
+		<layer id="350" name="bottleneck3_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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+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
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+					<dim>1</dim>
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+					<dim>68</dim>
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+				<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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+				<port id="2" names="bottleneck3_4/inner/dw1/conv" precision="FP32">
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+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
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+		<layer id="355" name="bottleneck3_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="356" name="bottleneck3_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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+					<dim>1</dim>
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+			<data auto_broadcast="numpy"/>
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+				<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>
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+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
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+		<layer id="360" name="bottleneck3_4/fn/weights3105640382" 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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+			<data auto_pad="explicit" dilations="1, 1" pads_begin="0, 0" pads_end="0, 0" strides="1, 1"/>
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+					<dim>1</dim>
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+					<dim>68</dim>
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+					<dim>1</dim>
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+		<layer id="365" name="bottleneck3_5/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>
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+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
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+				<port id="0" precision="FP32">
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+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
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+			<data element_type="f32" offset="317532" shape="32, 1, 1, 3, 3" size="1152"/>
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+			<data auto_broadcast="numpy"/>
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+				<port id="0" precision="FP32">
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+					<dim>32</dim>
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+					<dim>68</dim>
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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>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
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+			</output>
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+		<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"/>
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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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+				<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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+					<dim>68</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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+				<port id="0" precision="FP32">
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+					<dim>68</dim>
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+			<data element_type="f32" offset="353500" shape="128, 32, 1, 1" size="16384"/>
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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>128</dim>
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+					<dim>68</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 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_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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+			</output>
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+				<port id="0" precision="FP32">
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+					<dim>68</dim>
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+			</output>
+		</layer>
+		<layer id="412" name="bottleneck3_7/dim_inc/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
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+					<dim>1</dim>
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+		</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"/>
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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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+		<layer id="417" name="bottleneck3_7/fn/weights3102840676" type="Const" version="opset1">
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+				<port id="0" precision="FP32">
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+				<port id="2" names="bottleneck3_7/add" precision="FP32">
+					<dim>1</dim>
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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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+					<dim>68</dim>
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+					<dim>1</dim>
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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">
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+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
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+				<port id="0" precision="FP32">
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+			<output>
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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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+			<data element_type="f32" offset="421596" shape="32, 1, 1, 3, 3" size="1152"/>
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+					<dim>3</dim>
+					<dim>3</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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+					<dim>68</dim>
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+			<data auto_broadcast="numpy"/>
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+				<port id="0" precision="FP32">
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+					<dim>32</dim>
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+					<dim>68</dim>
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+				<port id="0" precision="FP32">
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+					<dim>40</dim>
+					<dim>68</dim>
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+			<data element_type="f32" offset="422876" shape="128, 32, 1, 1" size="16384"/>
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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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+					<dim>1</dim>
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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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+			<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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+					<dim>1</dim>
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+			<output>
+				<port id="2" names="bottleneck3_8/dim_inc/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</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">
+					<dim>1</dim>
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+					<dim>68</dim>
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+				<port id="1" precision="FP32">
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+			<output>
+				<port id="2" names="bottleneck3_8/add" precision="FP32">
+					<dim>1</dim>
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+					<dim>68</dim>
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+					<dim>1</dim>
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+		<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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+					<dim>68</dim>
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+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
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+			<output>
+				<port id="2" names="bottleneck3_9/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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+				<port id="0" precision="FP32">
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+				<port id="1" precision="FP32">
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+			<output>
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+			</output>
+		</layer>
+		<layer id="444" name="bottleneck3_9/inner/dw1/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f32" offset="456284" shape="32, 1, 1, 3, 3" size="1152"/>
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+					<dim>3</dim>
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+			</output>
+		</layer>
+		<layer id="445" name="bottleneck3_9/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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+					<dim>68</dim>
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+			</output>
+		</layer>
+		<layer id="446" name="data_add_2420924214" type="Const" version="opset1">
+			<data element_type="f32" offset="457436" shape="1, 32, 1, 1" size="128"/>
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+					<dim>1</dim>
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+		<layer id="447" name="bottleneck3_9/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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+			</output>
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+		<layer id="448" name="bottleneck3_9/inner/dw1/fn/weights3112040592" type="Const" version="opset1">
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+					<dim>1</dim>
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+				<port id="1" precision="FP32">
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+		<layer id="450" name="bottleneck3_9/dim_inc/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f32" offset="457564" shape="128, 32, 1, 1" size="16384"/>
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+		</layer>
+		<layer id="451" name="bottleneck3_9/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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+					<dim>1</dim>
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+			<data element_type="f32" offset="473948" shape="1, 128, 1, 1" size="512"/>
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+					<dim>1</dim>
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+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
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+			<output>
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+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
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+			</output>
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+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
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+		<layer id="455" name="bottleneck3_9/fn/weights3116840514" 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>
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+				<port id="1" precision="FP32">
+					<dim>1</dim>
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+			<output>
+				<port id="2" names="bottleneck3_9/add" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
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+					<dim>68</dim>
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+			</output>
+		</layer>
+		<layer id="457" name="bottleneck3_10/dim_red/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f32" offset="474460" shape="32, 128, 1, 1" size="16384"/>
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+					<dim>1</dim>
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+		</layer>
+		<layer id="458" name="bottleneck3_10/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>1</dim>
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+					<dim>68</dim>
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+			<data element_type="f32" offset="490844" shape="1, 32, 1, 1" size="128"/>
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+					<dim>1</dim>
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+			</output>
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+		<layer id="460" name="bottleneck3_10/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>
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+			<output>
+				<port id="2" names="bottleneck3_10/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="461" name="bottleneck3_10/dim_red/fn/weights3100040610" 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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+			<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>
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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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+			<data element_type="f32" offset="490972" shape="32, 1, 1, 3, 3" size="1152"/>
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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>68</dim>
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+		<layer id="465" name="data_add_2423324238" type="Const" version="opset1">
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+		<layer id="466" name="bottleneck3_10/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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+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
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+			</output>
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+		<layer id="467" name="bottleneck3_10/inner/dw1/fn/weights3116040688" type="Const" version="opset1">
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+		<layer id="468" name="bottleneck3_10/inner/dw1/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>
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+					<dim>1</dim>
+					<dim>32</dim>
+					<dim>40</dim>
+					<dim>68</dim>
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+			</output>
+		</layer>
+		<layer id="469" name="bottleneck3_10/dim_inc/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f32" offset="492252" shape="128, 32, 1, 1" size="16384"/>
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+					<dim>1</dim>
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+		<layer id="470" name="bottleneck3_10/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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+					<dim>1</dim>
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+					<dim>40</dim>
+					<dim>68</dim>
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+					<dim>1</dim>
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+		<layer id="472" name="bottleneck3_10/dim_inc/bn/variance/Fused_Add_" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<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>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
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+			</output>
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+		<layer id="473" name="bottleneck3_10/add" type="Add" version="opset1">
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+			<input>
+				<port id="0" precision="FP32">
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+					<dim>40</dim>
+					<dim>68</dim>
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+				<port id="0" precision="FP32">
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+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
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+					<dim>68</dim>
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+				<port id="1" precision="FP32">
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+			<output>
+				<port id="2" names="bottleneck3_10/add" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>40</dim>
+					<dim>68</dim>
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+			</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>40</dim>
+					<dim>68</dim>
+				</port>
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+			<output>
+				<port id="1" names="bottleneck4_0/skip/pooling" precision="FP32">
+					<dim>1</dim>
+					<dim>128</dim>
+					<dim>20</dim>
+					<dim>34</dim>
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+			</output>
+		</layer>
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+			<data element_type="f32" offset="509148" shape="256, 128, 1, 1" size="131072"/>
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+					<dim>1</dim>
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+		</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"/>
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+			</output>
+		</layer>
+		<layer id="479" name="data_add_2424924254" type="Const" version="opset1">
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+		</layer>
+		<layer id="480" name="bottleneck4_0/skip/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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+		</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"/>
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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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+			<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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+			<data auto_broadcast="numpy"/>
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+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>64</dim>
+					<dim>20</dim>
+					<dim>34</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="bottleneck4_1/inner/dw1/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>64</dim>
+					<dim>20</dim>
+					<dim>34</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="512" name="bottleneck4_1/dim_inc/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f32" offset="811740" shape="256, 64, 1, 1" size="65536"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>256</dim>
+					<dim>64</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="513" name="bottleneck4_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>64</dim>
+					<dim>20</dim>
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+				</port>
+				<port id="1" precision="FP32">
+					<dim>256</dim>
+					<dim>64</dim>
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+					<dim>1</dim>
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+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>20</dim>
+					<dim>34</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="514" name="data_add_2429724302" type="Const" version="opset1">
+			<data element_type="f32" offset="877276" shape="1, 256, 1, 1" size="1024"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>1</dim>
+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="515" name="bottleneck4_1/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="bottleneck4_1/dim_inc/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>20</dim>
+					<dim>34</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="516" name="bottleneck4_1/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">
+					<dim>1</dim>
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+			</input>
+			<output>
+				<port id="2" names="bottleneck4_1/add" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>20</dim>
+					<dim>34</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="517" name="bottleneck4_1/fn/weights3078840664" 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="518" name="bottleneck4_1/fn" type="PReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
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+				<port id="1" precision="FP32">
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="bottleneck4_1/add" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>20</dim>
+					<dim>34</dim>
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+			</output>
+		</layer>
+		<layer id="519" name="bottleneck4_2/dim_red/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f32" offset="878300" shape="64, 256, 1, 1" size="65536"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>64</dim>
+					<dim>256</dim>
+					<dim>1</dim>
+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="520" name="bottleneck4_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>256</dim>
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+				<port id="1" precision="FP32">
+					<dim>64</dim>
+					<dim>256</dim>
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+					<dim>1</dim>
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+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>64</dim>
+					<dim>20</dim>
+					<dim>34</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="521" name="data_add_2430524310" type="Const" version="opset1">
+			<data element_type="f32" offset="943836" shape="1, 64, 1, 1" size="256"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>64</dim>
+					<dim>1</dim>
+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="522" name="bottleneck4_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>
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+			</input>
+			<output>
+				<port id="2" names="bottleneck4_2/dim_red/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>64</dim>
+					<dim>20</dim>
+					<dim>34</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="523" name="bottleneck4_2/dim_red/fn/weights3080440121" 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="524" name="bottleneck4_2/dim_red/fn" type="PReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>64</dim>
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+				<port id="1" precision="FP32">
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="bottleneck4_2/dim_red/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>64</dim>
+					<dim>20</dim>
+					<dim>34</dim>
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+			</output>
+		</layer>
+		<layer id="525" name="bottleneck4_2/inner/dw1/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f32" offset="944092" shape="64, 1, 1, 3, 3" size="2304"/>
+			<output>
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+					<dim>3</dim>
+					<dim>3</dim>
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+			</output>
+		</layer>
+		<layer id="526" name="bottleneck4_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">
+					<dim>1</dim>
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+				<port id="1" precision="FP32">
+					<dim>64</dim>
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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>20</dim>
+					<dim>34</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="527" name="data_add_2431324318" type="Const" version="opset1">
+			<data element_type="f32" offset="946396" shape="1, 64, 1, 1" size="256"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>64</dim>
+					<dim>1</dim>
+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="528" name="bottleneck4_2/inner/dw1/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">
+					<dim>1</dim>
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+					<dim>1</dim>
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+			</input>
+			<output>
+				<port id="2" names="bottleneck4_2/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="529" name="bottleneck4_2/inner/dw1/fn/weights3078440649" 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="530" name="bottleneck4_2/inner/dw1/fn" type="PReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>64</dim>
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+				<port id="1" precision="FP32">
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="bottleneck4_2/inner/dw1/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>64</dim>
+					<dim>20</dim>
+					<dim>34</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="531" name="bottleneck4_2/dim_inc/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f32" offset="946652" shape="256, 64, 1, 1" size="65536"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>256</dim>
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+					<dim>1</dim>
+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="532" name="bottleneck4_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">
+					<dim>1</dim>
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+					<dim>20</dim>
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+					<dim>256</dim>
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+					<dim>1</dim>
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+			</input>
+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>20</dim>
+					<dim>34</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="533" name="data_add_2432124326" type="Const" version="opset1">
+			<data element_type="f32" offset="1012188" shape="1, 256, 1, 1" size="1024"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>1</dim>
+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="534" name="bottleneck4_2/dim_inc/bn/variance/Fused_Add_" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<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>
+					<dim>256</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="bottleneck4_2/dim_inc/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>20</dim>
+					<dim>34</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="535" name="bottleneck4_2/add" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>20</dim>
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+				<port id="1" precision="FP32">
+					<dim>1</dim>
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+					<dim>20</dim>
+					<dim>34</dim>
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+			</input>
+			<output>
+				<port id="2" names="bottleneck4_2/add" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>20</dim>
+					<dim>34</dim>
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+			</output>
+		</layer>
+		<layer id="536" name="bottleneck4_2/fn/weights3084440229" 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="537" name="bottleneck4_2/fn" type="PReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>20</dim>
+					<dim>34</dim>
+				</port>
+				<port id="1" precision="FP32">
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="bottleneck4_2/add" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>20</dim>
+					<dim>34</dim>
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+			</output>
+		</layer>
+		<layer id="538" name="bottleneck4_3/dim_red/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f32" offset="1013212" shape="64, 256, 1, 1" size="65536"/>
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+					<dim>64</dim>
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+					<dim>1</dim>
+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="539" name="bottleneck4_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="1" precision="FP32">
+					<dim>64</dim>
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+					<dim>1</dim>
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+			<output>
+				<port id="2" precision="FP32">
+					<dim>1</dim>
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+					<dim>20</dim>
+					<dim>34</dim>
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+			</output>
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+		<layer id="540" name="data_add_2432924334" type="Const" version="opset1">
+			<data element_type="f32" offset="1078748" shape="1, 64, 1, 1" size="256"/>
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+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="541" name="bottleneck4_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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+				<port id="1" precision="FP32">
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+					<dim>1</dim>
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+			</input>
+			<output>
+				<port id="2" names="bottleneck4_3/dim_red/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>64</dim>
+					<dim>20</dim>
+					<dim>34</dim>
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+			</output>
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+		<layer id="542" name="bottleneck4_3/dim_red/fn/weights3112440511" 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="543" name="bottleneck4_3/dim_red/fn" type="PReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
+					<dim>64</dim>
+					<dim>20</dim>
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+				<port id="1" precision="FP32">
+					<dim>1</dim>
+				</port>
+			</input>
+			<output>
+				<port id="2" names="bottleneck4_3/dim_red/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>64</dim>
+					<dim>20</dim>
+					<dim>34</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="544" name="bottleneck4_3/inner/dw1/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f32" offset="1079004" shape="64, 1, 1, 3, 3" size="2304"/>
+			<output>
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+					<dim>1</dim>
+					<dim>3</dim>
+					<dim>3</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="545" name="bottleneck4_3/inner/dw1/conv" type="GroupConvolution" version="opset1">
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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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+			<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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+			<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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+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="613" name="bottleneck4_6/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="bottleneck4_6/add" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>20</dim>
+					<dim>34</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="614" name="bottleneck4_7/dim_red/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f32" offset="1552860" shape="64, 256, 1, 1" size="65536"/>
+			<output>
+				<port id="0" precision="FP32">
+					<dim>64</dim>
+					<dim>256</dim>
+					<dim>1</dim>
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<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"/>
+			<input>
+				<port id="0" precision="FP32">
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+				<port id="1" precision="FP32">
+					<dim>64</dim>
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+					<dim>1</dim>
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+			</input>
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+				<port id="2" precision="FP32">
+					<dim>1</dim>
+					<dim>64</dim>
+					<dim>20</dim>
+					<dim>34</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="616" name="data_add_2442524430" type="Const" version="opset1">
+			<data element_type="f32" offset="1618396" shape="1, 64, 1, 1" size="256"/>
+			<output>
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+					<dim>1</dim>
+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="617" name="bottleneck4_7/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>
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+			<output>
+				<port id="2" names="bottleneck4_7/dim_red/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>64</dim>
+					<dim>20</dim>
+					<dim>34</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="618" name="bottleneck4_7/dim_red/fn/weights3095240595" type="Const" version="opset1">
+			<data element_type="f32" offset="4664" shape="1" size="4"/>
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+				<port id="0" precision="FP32">
+					<dim>1</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="619" name="bottleneck4_7/dim_red/fn" type="PReLU" version="opset1">
+			<input>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+			<output>
+				<port id="2" names="bottleneck4_7/dim_red/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>64</dim>
+					<dim>20</dim>
+					<dim>34</dim>
+				</port>
+			</output>
+		</layer>
+		<layer id="620" name="bottleneck4_7/inner/dw1/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f32" offset="1618652" shape="64, 1, 1, 3, 3" size="2304"/>
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+					<dim>3</dim>
+					<dim>3</dim>
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+			</output>
+		</layer>
+		<layer id="621" name="bottleneck4_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">
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+				<port id="2" precision="FP32">
+					<dim>1</dim>
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+		<layer id="622" name="data_add_2443324438" type="Const" version="opset1">
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+					<dim>1</dim>
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+		<layer id="623" name="bottleneck4_7/inner/dw1/bn/variance/Fused_Add_" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
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+				<port id="2" names="bottleneck4_7/inner/dw1/conv" precision="FP32">
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+					<dim>64</dim>
+					<dim>20</dim>
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+		<layer id="624" name="bottleneck4_7/inner/dw1/fn/weights3101240547" type="Const" version="opset1">
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+			<output>
+				<port id="0" precision="FP32">
+					<dim>1</dim>
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+			</output>
+		</layer>
+		<layer id="625" name="bottleneck4_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">
+					<dim>1</dim>
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+			</input>
+			<output>
+				<port id="2" names="bottleneck4_7/inner/dw1/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>64</dim>
+					<dim>20</dim>
+					<dim>34</dim>
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+			</output>
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+		<layer id="626" name="bottleneck4_7/dim_inc/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f32" offset="1621212" shape="256, 64, 1, 1" size="65536"/>
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+					<dim>1</dim>
+					<dim>1</dim>
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+		</layer>
+		<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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+				<port id="0" precision="FP32">
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+					<dim>1</dim>
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+					<dim>1</dim>
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+					<dim>34</dim>
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+		<layer id="628" name="data_add_2444124446" type="Const" version="opset1">
+			<data element_type="f32" offset="1686748" shape="1, 256, 1, 1" size="1024"/>
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+					<dim>1</dim>
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+		<layer id="629" name="bottleneck4_7/dim_inc/bn/variance/Fused_Add_" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
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+			<output>
+				<port id="2" names="bottleneck4_7/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="630" name="bottleneck4_7/add" type="Add" version="opset1">
+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
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+				<port id="2" names="bottleneck4_7/add" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>20</dim>
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+			<data element_type="f32" offset="4664" shape="1" size="4"/>
+			<output>
+				<port id="0" precision="FP32">
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+		<layer id="632" name="bottleneck4_7/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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+			<output>
+				<port id="2" names="bottleneck4_7/add" precision="FP32">
+					<dim>1</dim>
+					<dim>256</dim>
+					<dim>20</dim>
+					<dim>34</dim>
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+		<layer id="633" name="bottleneck4_8/dim_red/bn/mean/Fused_Mul__copy" type="Const" version="opset1">
+			<data element_type="f32" offset="1687772" shape="64, 256, 1, 1" size="65536"/>
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+		<layer id="634" name="bottleneck4_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"/>
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+				<port id="2" precision="FP32">
+					<dim>1</dim>
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+					<dim>34</dim>
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+		<layer id="635" name="data_add_2444924454" type="Const" version="opset1">
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+					<dim>1</dim>
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+			</output>
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+		<layer id="636" name="bottleneck4_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>1</dim>
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+			</input>
+			<output>
+				<port id="2" names="bottleneck4_8/dim_red/conv" precision="FP32">
+					<dim>1</dim>
+					<dim>64</dim>
+					<dim>20</dim>
+					<dim>34</dim>
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+		<layer id="637" name="bottleneck4_8/dim_red/fn/weights3091640496" 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 id="638" name="bottleneck4_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="bottleneck4_8/dim_red/conv" precision="FP32">
+					<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="1753564" shape="64, 1, 1, 3, 3" size="2304"/>
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+					<dim>3</dim>
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+		</layer>
+		<layer id="640" name="bottleneck4_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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+					<dim>1</dim>
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+			<data auto_broadcast="numpy"/>
+			<input>
+				<port id="0" precision="FP32">
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+					<dim>34</dim>
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+				<port id="0" precision="FP32">
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+				<port id="1" precision="FP32">
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+					<dim>64</dim>
+					<dim>20</dim>
+					<dim>34</dim>
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+			<data element_type="f32" offset="1756124" shape="256, 64, 1, 1" size="65536"/>
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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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+				<port id="0" precision="FP32">
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+		<layer id="684" name="bottleneck4_10/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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+	</edges>
+	<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>
diff --git a/resources/run.mp4 b/resources/run.mp4
new file mode 100644
index 0000000..dd5f90e
--- /dev/null
+++ b/resources/run.mp4
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