OpenVINO on Raspberry Pi 4 with Movidius Neural Compute Stick II

Posted on Edited on In ,

Today is Easter Sunday, Prime minister of United Kindom Boris Johnson recovered from COVID-19. Canada has been suffereing COVID-19 for a month already. I'm re-writing this blog FIRSTLY written in September 2019, and UPDATED ONCE in December 2019.

Merry Christmas and happy new year everybody. I’ve been back to Vancouver for several days. These days, I’m updating this blog FIRSTLY written in September 2019. 2020 is coming, and we’re getting 1 year older. A kind of sad hum?

😝

Okay… No matter what, let’s enjoy the song first: WE ARE YOUNG. Today, I joined Free Software Foundation and start my journey of supporting Open Source Software BY CASH. For me, it’s not about poverity or richness. It’s ALL about FAITH.

To write something about Raspberry Pi is to say GOOD BYE to my Raspberry Pi 3B, and WELCOME Raspberry Pi 4 at the same time. Our target today is to build an AI edge computing end as the following Youtube video:

1. About Raspberry Pi 4

1.1 Raspberry Pi 4 vs. Raspberry Pi 3B+

Before we start, let’s carry out a simple comparison between Raspberry Pi 4 and Raspberry Pi 3B+.

1.2 Raspbian Installation

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➜  raspbian sudo dd bs=4M if=2020-02-13-raspbian-buster.img of=/dev/mmcblk0 conv=fsync 
[sudo] password for longervision: 
903+0 records in
903+0 records out
3787456512 bytes (3.8 GB, 3.5 GiB) copied, 198.861 s, 19.0 MB/s

1.3 BCM2711 is detected as BCM2835

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➜  ~ cat /proc/cpuinfo
processor	: 0
model name	: ARMv7 Processor rev 3 (v7l)
BogoMIPS	: 108.00
Features	: half thumb fastmult vfp edsp neon vfpv3 tls vfpv4 idiva idivt vfpd32 lpae evtstrm crc32 
CPU implementer	: 0x41
CPU architecture: 7
CPU variant	: 0x0
CPU part	: 0xd08
CPU revision	: 3

processor	: 1
model name	: ARMv7 Processor rev 3 (v7l)
BogoMIPS	: 108.00
Features	: half thumb fastmult vfp edsp neon vfpv3 tls vfpv4 idiva idivt vfpd32 lpae evtstrm crc32 
CPU implementer	: 0x41
CPU architecture: 7
CPU variant	: 0x0
CPU part	: 0xd08
CPU revision	: 3

processor	: 2
model name	: ARMv7 Processor rev 3 (v7l)
BogoMIPS	: 108.00
Features	: half thumb fastmult vfp edsp neon vfpv3 tls vfpv4 idiva idivt vfpd32 lpae evtstrm crc32 
CPU implementer	: 0x41
CPU architecture: 7
CPU variant	: 0x0
CPU part	: 0xd08
CPU revision	: 3

processor	: 3
model name	: ARMv7 Processor rev 3 (v7l)
BogoMIPS	: 108.00
Features	: half thumb fastmult vfp edsp neon vfpv3 tls vfpv4 idiva idivt vfpd32 lpae evtstrm crc32 
CPU implementer	: 0x41
CPU architecture: 7
CPU variant	: 0x0
CPU part	: 0xd08
CPU revision	: 3

Hardware	: BCM2835
Revision	: c03111
Serial		: 100000006c0c9b01
Model		: Raspberry Pi 4 Model B Rev 1.1

This issue seems to be a well-known bug. Raspberry Pi 4’s specification can be retrieved from The MagPi Magazine. More details about the development history of Raspberry Pi can be found on Wikipedia.

2. Movidius Neural Compute Stick on Raspberry Pi 4

Then, we just follow the following 2 blogs Run NCS Applications on Raspberry Pi and Adding AI to the Raspberry Pi with the Movidius Neural Compute Stick to test out Intel Movidius Neural Compute Stick 2:

Intel Movidius Neural Compute Stick 2 Intel Movidius Neural Compute Stick 1
Intel Movidius Neural Compute Stick 2 Intel Movidius Neural Compute Stick 1

Intel Movidius Neural Compute Stick 1 is NOT listed on Intel’s official website any more. But github support for Intel Movidius Neural Compute Stick 1 can be found at https://github.com/movidius/ncsdk.

2.1 NCSDK Installation

We FIRST need to have ncsdk installed. Yup, here, as described in Run NCS Applications on Raspberry Pi, we carry out the installation directly under folder ...../ncsdk/api/src.

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➜  src make
cc -I/home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/components/XLink/shared -I/home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/components/XLink/pc -I/home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/components/XLinkConsole/pc -I /home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/swCommon/include/ -I /home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/shared/include/ -D__PC__ -DUSE_USB_VSC -DVERSION_NAME="\"`cat ./version.txt`\"" -DDEVICE_SHELL_ENABLED -DEXCLUDE_HIGHCLASS -O2 -Wall -pthread -fPIC -MMD -MP -I. -I../include -I/usr/include/libusb-1.0  -c mvnc_api.c -o obj-armv7l/mvnc_api.o
mvnc_api.c:457:12: warning: ‘deviceGetNumberOfDevices’ defined but not used [-Wunused-function]
 static int deviceGetNumberOfDevices()
            ^~~~~~~~~~~~~~~~~~~~~~~~
mvnc_api.c: In function ‘ncGraphCreate’:
mvnc_api.c:898:5: warning: ‘strncpy’ specified bound 28 equals destination size [-Wstringop-truncation]
     strncpy(g->name, name, NC_MAX_NAME_SIZE);
     ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
mvnc_api.c: In function ‘ncFifoCreate’:
mvnc_api.c:2210:5: warning: ‘strncpy’ specified bound 28 equals destination size [-Wstringop-truncation]
     strncpy(handle->name, name, NC_MAX_NAME_SIZE);
     ^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
cc -I/home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/components/XLink/shared -I/home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/components/XLink/pc -I/home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/components/XLinkConsole/pc -I /home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/swCommon/include/ -I /home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/shared/include/ -D__PC__ -DUSE_USB_VSC -DVERSION_NAME="\"`cat ./version.txt`\"" -DDEVICE_SHELL_ENABLED -DEXCLUDE_HIGHCLASS -O2 -Wall -pthread -fPIC -MMD -MP -I. -I../include -I/usr/include/libusb-1.0  -c fp16.c -o obj-armv7l/fp16.o
cc -I/home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/components/XLink/shared -I/home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/components/XLink/pc -I/home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/components/XLinkConsole/pc -I /home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/swCommon/include/ -I /home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/shared/include/ -D__PC__ -DUSE_USB_VSC -DVERSION_NAME="\"`cat ./version.txt`\"" -DDEVICE_SHELL_ENABLED -DEXCLUDE_HIGHCLASS -O2 -Wall -pthread -fPIC -MMD -MP -I. -I../include -I/usr/include/libusb-1.0  -c /home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/components/XLink/pc/usb_boot.c -o obj-armv7l//home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/components/XLink/pc/usb_boot.o
cc -I/home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/components/XLink/shared -I/home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/components/XLink/pc -I/home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/components/XLinkConsole/pc -I /home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/swCommon/include/ -I /home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/shared/include/ -D__PC__ -DUSE_USB_VSC -DVERSION_NAME="\"`cat ./version.txt`\"" -DDEVICE_SHELL_ENABLED -DEXCLUDE_HIGHCLASS -O2 -Wall -pthread -fPIC -MMD -MP -I. -I../include -I/usr/include/libusb-1.0  -c /home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/components/XLink/pc/pcie_host.c -o obj-armv7l//home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/components/XLink/pc/pcie_host.o
cc -I/home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/components/XLink/shared -I/home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/components/XLink/pc -I/home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/components/XLinkConsole/pc -I /home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/swCommon/include/ -I /home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/shared/include/ -D__PC__ -DUSE_USB_VSC -DVERSION_NAME="\"`cat ./version.txt`\"" -DDEVICE_SHELL_ENABLED -DEXCLUDE_HIGHCLASS -O2 -Wall -pthread -fPIC -MMD -MP -I. -I../include -I/usr/include/libusb-1.0  -c /home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/components/XLink/shared/XLink.c -o obj-armv7l//home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/components/XLink/shared/XLink.o
cc -I/home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/components/XLink/shared -I/home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/components/XLink/pc -I/home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/components/XLinkConsole/pc -I /home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/swCommon/include/ -I /home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/shared/include/ -D__PC__ -DUSE_USB_VSC -DVERSION_NAME="\"`cat ./version.txt`\"" -DDEVICE_SHELL_ENABLED -DEXCLUDE_HIGHCLASS -O2 -Wall -pthread -fPIC -MMD -MP -I. -I../include -I/usr/include/libusb-1.0  -c /home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/components/XLink/shared/XLinkDispatcher.c -o obj-armv7l//home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/components/XLink/shared/XLinkDispatcher.o
cc -I/home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/components/XLink/shared -I/home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/components/XLink/pc -I/home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/components/XLinkConsole/pc -I /home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/swCommon/include/ -I /home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/shared/include/ -D__PC__ -DUSE_USB_VSC -DVERSION_NAME="\"`cat ./version.txt`\"" -DDEVICE_SHELL_ENABLED -DEXCLUDE_HIGHCLASS -O2 -Wall -pthread -fPIC -MMD -MP -I. -I../include -I/usr/include/libusb-1.0  -c /home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/components/XLinkConsole/pc/XLinkConsole.c -o obj-armv7l//home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/components/XLinkConsole/pc/XLinkConsole.o
/home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/components/XLinkConsole/pc/XLinkConsole.c: In function ‘shellThreadWriter’:
/home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/components/XLinkConsole/pc/XLinkConsole.c:44:33: warning: pointer targets in passing argument 2 of ‘XLinkWriteData’ differ in signedness [-Wpointer-sign]
             XLinkWriteData(cId, str, bytes);
                                 ^~~
In file included from /home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/components/XLinkConsole/pc/XLinkConsole.c:18:
/home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/components/XLink/shared/XLink.h:54:14: note: expected ‘const uint8_t *’ {aka ‘const unsigned char *’} but argument is of type ‘char *’
 XLinkError_t XLinkWriteData(streamId_t streamId, const uint8_t* buffer, int size);
              ^~~~~~~~~~~~~~
/home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/components/XLinkConsole/pc/XLinkConsole.c: In function ‘shellThreadReader’:
/home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/components/XLinkConsole/pc/XLinkConsole.c:81:5: warning: implicit declaration of function ‘pthread_create’ [-Wimplicit-function-declaration]
     pthread_create(&shellWriter, NULL, shellThreadWriter, (void*) context);
     ^~~~~~~~~~~~~~
cc -I/home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/components/XLink/shared -I/home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/components/XLink/pc -I/home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/components/XLinkConsole/pc -I /home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/swCommon/include/ -I /home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/shared/include/ -D__PC__ -DUSE_USB_VSC -DVERSION_NAME="\"`cat ./version.txt`\"" -DDEVICE_SHELL_ENABLED -DEXCLUDE_HIGHCLASS -O2 -Wall -pthread -fPIC -MMD -MP -I. -I../include -I/usr/include/libusb-1.0  -c /home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/components/XLink/pc/XLinkPlatform.c -o obj-armv7l//home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/components/XLink/pc/XLinkPlatform.o
if [ ! -e ./version.txt ] ; then echo "missing version.txt file"; exit 1; fi;
cc -shared obj-armv7l/mvnc_api.o obj-armv7l/fp16.o obj-armv7l//home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/components/XLink/pc/usb_boot.o obj-armv7l//home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/components/XLink/pc/pcie_host.o obj-armv7l//home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/components/XLink/shared/XLink.o obj-armv7l//home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/components/XLink/shared/XLinkDispatcher.o obj-armv7l//home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/components/XLinkConsole/pc/XLinkConsole.o obj-armv7l//home/pi/Downloads/ncsdk-2.10.01.01/api/src/common/components/XLink/pc/XLinkPlatform.o -o obj-armv7l/libmvnc.so.0 -lpthread -lusb-1.0 -ldl
ln -fs obj-armv7l/libmvnc.so.0 libmvnc.so
ln -fs obj-armv7l/libmvnc.so.0 libmvnc.so.0
NCSDK FW successfully installed
➜  src sudo make install
NCSDK FW successfully installed
mkdir -p /usr/local/include/
mkdir -p /usr/local/include/mvnc2
mkdir -p /usr/local/lib/
cp obj-armv7l/libmvnc.so.0 /usr/local/lib/
ln -fs libmvnc.so.0 /usr/local/lib/libmvnc.so
cp ../include/mvnc.h /usr/local/include/mvnc2
ln -fs /usr/local/include/mvnc2/mvnc.h /usr/local/include/mvnc.h 
mkdir -p /usr/local/lib/mvnc
cp mvnc/MvNCAPI-*.mvcmd /usr/local/lib/mvnc/
mkdir -p /etc/udev/rules.d/
cp 97-usbboot.rules /etc/udev/rules.d/
mkdir -p /usr/local/lib/python3.7/dist-packages
mkdir -p /usr/local/lib/python3.7/dist-packages
cp -r ../python/mvnc /usr/local/lib/python3.7/dist-packages/
cp -r ../python/mvnc /usr/local/lib/python3.7/dist-packages/
udevadm control --reload-rules
udevadm trigger
ldconfig
➜  src

2.2 Test NCSDK Example Apps

2.2.1 For Movidius NCS 1

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➜  hello_ncs_py lsusb
......
Bus 001 Device 004: ID 03e7:2150 Intel Myriad VPU [Movidius Neural Compute Stick]
......
➜  hello_ncs_py python hello_ncs.py
D: [         0] ncDeviceCreate:307	ncDeviceCreate index 0
D: [         0] ncDeviceCreate:307	ncDeviceCreate index 1
D: [         0] ncDeviceOpen:523	File path /usr/local/lib/mvnc/MvNCAPI-ma2450.mvcmd
I: [         0] ncDeviceOpen:529	ncDeviceOpen() XLinkBootRemote returned success 0
I: [         0] ncDeviceOpen:567	XLinkConnect done - link Id 0
D: [         0] ncDeviceOpen:581	done
I: [         0] ncDeviceOpen:583	Booted 1.1-ma2450 -> VSC
I: [         0] getDevAttributes:382	Device attributes
I: [         0] getDevAttributes:385	Device FW version: 2.a.2450.8a
I: [         0] getDevAttributes:387	mvTensorVersion 2.10
I: [         0] getDevAttributes:388	Maximum graphs: 10
I: [         0] getDevAttributes:389	Maximum fifos: 20
I: [         0] getDevAttributes:391	Maximum graph option class: 1
I: [         0] getDevAttributes:393	Maximum device option class: 1
I: [         0] getDevAttributes:394	Device memory capacity: 522047856
Hello NCS! Device opened normally.
I: [         0] ncDeviceClose:775	closing device
Goodbye NCS! Device closed normally.
NCS device working.

2.2.2 For Movidius NCS 2

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➜  hello_ncs_py lsusb
......
Bus 001 Device 007: ID 03e7:2485 Intel Movidius MyriadX
......
➜  hello_ncs_py python hello_ncs.py 
D: [         0] ncDeviceCreate:307	ncDeviceCreate index 0
D: [         0] ncDeviceCreate:307	ncDeviceCreate index 1
D: [         0] ncDeviceOpen:523	File path /usr/local/lib/mvnc/MvNCAPI-ma2480.mvcmd
W: [         0] ncDeviceOpen:527	ncDeviceOpen() XLinkBootRemote returned error 3
Error - Could not open NCS device.

The above bug has ALREADY been expalined in main online resource:

All these hint that OpenVINO should be utilized instead of NCSDK2.

2.3 mvnc Python Package

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➜  ~ python
Python 3.7.3 (default, Dec 20 2019, 18:57:59) 
[GCC 8.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> import mvnc
>>> mvnc.__file__
'/usr/local/lib/python3.7/dist-packages/mvnc/__init__.py'
>>>

3. Transitioning from Intel Movidius Neural Compute SDK to Intel OpenVINO

By following Intel’s official documentation Transitioning from Intel® Movidius™ Neural Compute SDK to Intel® Distribution of OpenVINO™ toolkit, we are transitioning to OpenVINO, which supports both Intel NCS 2 and Intel NCS 1.

3.1 Install OpenVINO for Raspbian

For the installation details of OpenVINO, please refer to the following 2 documentations:

We now extract the MOST up-to-date l_openvino_toolkit_runtime_raspbian_p_2020.3.220.tgz under folder /opt/intel/openvino. Let’s take a brief look at this folder:

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➜  openvino pwd
/opt/intel/openvino
➜  openvino ll
total 28K
drwxr-xr-x 2 pi pi 4.0K Jan 27 08:06 bin
drwxr-xr-x 4 pi pi 4.0K Jan 27 08:06 deployment_tools
drwxr-xr-x 2 pi pi 4.0K Jan 27 08:08 documentation
lrwxrwxrwx 1 pi pi   33 Jan 27 08:04 inference_engine -> deployment_tools/inference_engine
drwxr-xr-x 2 pi pi 4.0K Jan 27 08:08 install_dependencies
drwxr-xr-x 5 pi pi 4.0K Jan 27 08:08 licensing
drwxr-xr-x 8 pi pi 4.0K Jan 27 08:09 opencv
drwxr-xr-x 6 pi pi 4.0K Jan 27 08:09 python
➜  openvino ls inference_engine 
external  include  lib  samples  share  version.txt

Clearly, by comparing with OpenVINO™ Toolkit - Deep Learning Deployment Toolkit repository, we know that the open source version of deployment_tools contains some more content than the trimmed version for Raspbian. We’ll use model-optimizer for sure. Therefore, we checked out dldt, and put it under folder /opt/intel.

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➜  intel pwd
/opt/intel
➜  intel ll
total 12K
drwxr-xr-x 8 pi   pi   4.0K Apr  7 06:37 dldt
drwxr-xr-x 9 pi   pi   4.0K Apr  7 14:04 l_openvino_toolkit_runtime_raspbian_p_2020.1.023
lrwxrwxrwx 1 root root   48 Apr  7 06:29 openvino -> l_openvino_toolkit_runtime_raspbian_p_2020.1.023

3.2 Build OpenVINO Samples

Before start building OpenVINO samples, please have OpenCV built from source and installed. You can of course directly run the command build_samples.sh to build ALL samples. However, I personally would like you to build and install OpenCV from source FIRST.

Note: Be sure to enable -DCMAKE_CXX_FLAGS=’-march=armv7-a’ while building dldt/samples, which is exactly the same as the source under l_openvino_toolkit_runtime_raspbian_p_2020.1.023/inference_engine/samples/cpp. In fact, to build l_openvino_toolkit_runtime_raspbian_p_2020.1.023/inference_engine/samples/c, -DCMAKE_CXX_FLAGS=’-march=armv7-a’ also needs to be enabled.

After having successfully built C/C++ samples, let's enter folder /opt/intel/openvino/inference_engine/samples.

3.3 Device Query

3.2.1 C

There is NO such an exe file hello_query_device_c.

3.2.2 C++

For NCS 2

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➜  samples $ ./cpp/build/armv7l/Release/hello_query_device
Available devices: 
	Device: MYRIAD
	Metrics: 
		DEVICE_THERMAL : UNSUPPORTED TYPE
		RANGE_FOR_ASYNC_INFER_REQUESTS : { 3, 6, 1 }
		SUPPORTED_CONFIG_KEYS : [ CONFIG_FILE PERF_COUNT EXCLUSIVE_ASYNC_REQUESTS DEVICE_ID VPU_MYRIAD_PLATFORM VPU_IGNORE_IR_STATISTIC VPU_CUSTOM_LAYERS VPU_MYRIAD_FORCE_RESET VPU_PRINT_RECEIVE_TENSOR_TIME LOG_LEVEL VPU_HW_STAGES_OPTIMIZATION ]
		SUPPORTED_METRICS : [ DEVICE_THERMAL RANGE_FOR_ASYNC_INFER_REQUESTS SUPPORTED_CONFIG_KEYS SUPPORTED_METRICS OPTIMIZATION_CAPABILITIES FULL_DEVICE_NAME AVAILABLE_DEVICES ]
		OPTIMIZATION_CAPABILITIES : [ FP16 ]
		FULL_DEVICE_NAME : Intel Movidius Myriad X VPU
	Default values for device configuration keys: 
		CONFIG_FILE : ""
		PERF_COUNT : OFF
		EXCLUSIVE_ASYNC_REQUESTS : OFF
		DEVICE_ID : ""
		VPU_MYRIAD_PLATFORM : ""
		VPU_IGNORE_IR_STATISTIC : OFF
		VPU_CUSTOM_LAYERS : ""
		VPU_MYRIAD_FORCE_RESET : OFF
		VPU_PRINT_RECEIVE_TENSOR_TIME : OFF
		LOG_LEVEL : LOG_NONE
		VPU_HW_STAGES_OPTIMIZATION : ON

For NCS 1

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➜  samples ./cpp/build/armv7l/Release/hello_query_device
Available devices: 
	Device: MYRIAD
	Metrics: 
		DEVICE_THERMAL : UNSUPPORTED TYPE
		RANGE_FOR_ASYNC_INFER_REQUESTS : { 3, 6, 1 }
		SUPPORTED_CONFIG_KEYS : [ CONFIG_FILE PERF_COUNT EXCLUSIVE_ASYNC_REQUESTS DEVICE_ID VPU_MYRIAD_PLATFORM VPU_IGNORE_IR_STATISTIC VPU_CUSTOM_LAYERS VPU_MYRIAD_FORCE_RESET VPU_PRINT_RECEIVE_TENSOR_TIME LOG_LEVEL VPU_HW_STAGES_OPTIMIZATION ]
		SUPPORTED_METRICS : [ DEVICE_THERMAL RANGE_FOR_ASYNC_INFER_REQUESTS SUPPORTED_CONFIG_KEYS SUPPORTED_METRICS OPTIMIZATION_CAPABILITIES FULL_DEVICE_NAME AVAILABLE_DEVICES ]
		OPTIMIZATION_CAPABILITIES : [ FP16 ]
		FULL_DEVICE_NAME : Intel Movidius Myriad 2 VPU
	Default values for device configuration keys: 
		CONFIG_FILE : ""
		PERF_COUNT : OFF
		EXCLUSIVE_ASYNC_REQUESTS : OFF
		DEVICE_ID : ""
		VPU_MYRIAD_PLATFORM : ""
		VPU_IGNORE_IR_STATISTIC : OFF
		VPU_CUSTOM_LAYERS : ""
		VPU_MYRIAD_FORCE_RESET : OFF
		VPU_PRINT_RECEIVE_TENSOR_TIME : OFF
		LOG_LEVEL : LOG_NONE
		VPU_HW_STAGES_OPTIMIZATION : ON

3.2.3 Python

For NCS 2

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➜  samples python ./python/hello_query_device/hello_query_device.py
Available devices:
	Device: MYRIAD
	Metrics:
		DEVICE_THERMAL: UNSUPPORTED TYPE
		RANGE_FOR_ASYNC_INFER_REQUESTS: 3, 6, 1
		SUPPORTED_CONFIG_KEYS: CONFIG_FILE, PERF_COUNT, EXCLUSIVE_ASYNC_REQUESTS, DEVICE_ID, VPU_MYRIAD_PLATFORM, VPU_IGNORE_IR_STATISTIC, VPU_CUSTOM_LAYERS, VPU_MYRIAD_FORCE_RESET, VPU_PRINT_RECEIVE_TENSOR_TIME, LOG_LEVEL, VPU_HW_STAGES_OPTIMIZATION
		SUPPORTED_METRICS: DEVICE_THERMAL, RANGE_FOR_ASYNC_INFER_REQUESTS, SUPPORTED_CONFIG_KEYS, SUPPORTED_METRICS, OPTIMIZATION_CAPABILITIES, FULL_DEVICE_NAME, AVAILABLE_DEVICES
		OPTIMIZATION_CAPABILITIES: FP16
		FULL_DEVICE_NAME: Intel Movidius Myriad X VPU
		AVAILABLE_DEVICES: 1.1.1-ma2480

	Default values for device configuration keys:
		CONFIG_FILE: 
		PERF_COUNT: OFF
		EXCLUSIVE_ASYNC_REQUESTS: OFF
		DEVICE_ID: 
		VPU_MYRIAD_PLATFORM: 
		VPU_IGNORE_IR_STATISTIC: OFF
		VPU_CUSTOM_LAYERS: 
		VPU_MYRIAD_FORCE_RESET: OFF
		VPU_PRINT_RECEIVE_TENSOR_TIME: OFF
		LOG_LEVEL: LOG_NONE
		VPU_HW_STAGES_OPTIMIZATION: ON

For NCS 1

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➜  samples python ./python/hello_query_device/hello_query_device.py
Available devices:
	Device: MYRIAD
	Metrics:
		DEVICE_THERMAL: UNSUPPORTED TYPE
		RANGE_FOR_ASYNC_INFER_REQUESTS: 3, 6, 1
		SUPPORTED_CONFIG_KEYS: CONFIG_FILE, PERF_COUNT, EXCLUSIVE_ASYNC_REQUESTS, DEVICE_ID, VPU_MYRIAD_PLATFORM, VPU_IGNORE_IR_STATISTIC, VPU_CUSTOM_LAYERS, VPU_MYRIAD_FORCE_RESET, VPU_PRINT_RECEIVE_TENSOR_TIME, LOG_LEVEL, VPU_HW_STAGES_OPTIMIZATION
		SUPPORTED_METRICS: DEVICE_THERMAL, RANGE_FOR_ASYNC_INFER_REQUESTS, SUPPORTED_CONFIG_KEYS, SUPPORTED_METRICS, OPTIMIZATION_CAPABILITIES, FULL_DEVICE_NAME, AVAILABLE_DEVICES
		OPTIMIZATION_CAPABILITIES: FP16
		FULL_DEVICE_NAME: Intel Movidius Myriad 2 VPU
		AVAILABLE_DEVICES: 1.1.1-ma2450

	Default values for device configuration keys:
		CONFIG_FILE: 
		PERF_COUNT: OFF
		EXCLUSIVE_ASYNC_REQUESTS: OFF
		DEVICE_ID: 
		VPU_MYRIAD_PLATFORM: 
		VPU_IGNORE_IR_STATISTIC: OFF
		VPU_CUSTOM_LAYERS: 
		VPU_MYRIAD_FORCE_RESET: OFF
		VPU_PRINT_RECEIVE_TENSOR_TIME: OFF
		LOG_LEVEL: LOG_NONE
		VPU_HW_STAGES_OPTIMIZATION: ON

3.3 Object Detection

Please refer to Device-specific Plugin Libraries for ALL possible device types.

We then download two model files as given in the blog Install OpenVINO™ toolkit for Raspbian* OS.

Please note that:

Afterwards, start running Object Detection for 2 images: me.jpg and parents.jpg:

3.3.1 C

For NCS 2

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➜  samples ./c/build/armv7l/Release/object_detection_sample_ssd_c -m face-detection-adas-0001.xml -d MYRIAD -i parents.jpg
[ INFO ] InferenceEngine: 
2.1.2020.3.0-3467-15f2c61a-releases/2020/3
[ INFO ] Parsing input parameters
[ INFO ] Files were added: 1
[ INFO ]     parents.jpg
[ INFO ] Loading Inference Engine
[ INFO ] Device info: 
         MYRIAD
         myriadPlugin version ......... 2.1
         Build ......... 2020.3.0-3467-15f2c61a-releases/2020/3
[ INFO ] Loading network files:
	face-detection-adas-0001.xml
	face-detection-adas-0001.bin
[ INFO ] Preparing input blobs
[ WARNING ] Image is resized from (1280, 960) to (672, 384)
[ INFO ] Batch size is 1
[ INFO ] Preparing output blobs
[ INFO ] Loading model to the device
[ INFO ] Create infer request
[ INFO ] Start inference
[ INFO ] Processing output blobs
[0, 1] element, prob = 1.000000    (826, 366)-(1026, 644) batch id : 0 WILL BE PRINTED!
[1, 1] element, prob = 0.996582    (539, 173)-(693, 429) batch id : 0 WILL BE PRINTED!
[2, 1] element, prob = 0.539062    (1094, 47)-(1135, 96) batch id : 0 WILL BE PRINTED!
[3, 1] element, prob = 0.212402    (848, 22)-(886, 70) batch id : 0
[4, 1] element, prob = 0.052734    (7, 783)-(151, 957) batch id : 0
[5, 1] element, prob = 0.039551    (1033, 78)-(1070, 122) batch id : 0
[6, 1] element, prob = 0.034668    (1123, 75)-(1158, 122) batch id : 0
[7, 1] element, prob = 0.032227    (1091, 109)-(1130, 171) batch id : 0
[8, 1] element, prob = 0.032227    (1086, 14)-(1138, 85) batch id : 0
[9, 1] element, prob = 0.030273    (1109, 43)-(1150, 93) batch id : 0
[10, 1] element, prob = 0.029297    (1151, 83)-(1190, 131) batch id : 0
[11, 1] element, prob = 0.027344    (1030, 2)-(1073, 43) batch id : 0
[12, 1] element, prob = 0.027344    (1091, 6)-(1133, 55) batch id : 0
[13, 1] element, prob = 0.027344    (1064, 73)-(1098, 121) batch id : 0
[14, 1] element, prob = 0.027344    (1058, 113)-(1096, 168) batch id : 0
[15, 1] element, prob = 0.027344    (1143, 74)-(1211, 165) batch id : 0
[16, 1] element, prob = 0.026855    (876, 73)-(915, 118) batch id : 0
[17, 1] element, prob = 0.026855    (1047, -3)-(1110, 59) batch id : 0
[18, 1] element, prob = 0.026855    (849, 13)-(1030, 236) batch id : 0
[19, 1] element, prob = 0.025879    (941, 22)-(981, 74) batch id : 0
[20, 1] element, prob = 0.025879    (1030, 35)-(1074, 89) batch id : 0
[21, 1] element, prob = 0.025879    (845, 112)-(886, 167) batch id : 0
[22, 1] element, prob = 0.025879    (1036, 111)-(1076, 171) batch id : 0
[23, 1] element, prob = 0.025879    (1113, -1)-(1175, 59) batch id : 0
[24, 1] element, prob = 0.025879    (1002, 40)-(1093, 151) batch id : 0
[25, 1] element, prob = 0.024902    (1059, 0)-(1098, 43) batch id : 0
[26, 1] element, prob = 0.024902    (876, 29)-(918, 79) batch id : 0
[27, 1] element, prob = 0.024902    (1002, 69)-(1048, 123) batch id : 0
[28, 1] element, prob = 0.024902    (1133, 104)-(1170, 163) batch id : 0
[29, 1] element, prob = 0.024902    (1056, 145)-(1094, 208) batch id : 0
[30, 1] element, prob = 0.024902    (861, 2)-(918, 63) batch id : 0
[31, 1] element, prob = 0.023926    (966, 1)-(1018, 45) batch id : 0
[32, 1] element, prob = 0.023926    (1098, 63)-(1131, 109) batch id : 0
[33, 1] element, prob = 0.023926    (1010, 84)-(1092, 204) batch id : 0
[34, 1] element, prob = 0.023926    (1022, 139)-(1076, 224) batch id : 0
[35, 1] element, prob = 0.023926    (943, 34)-(1073, 208) batch id : 0
[36, 1] element, prob = 0.022949    (811, 3)-(851, 48) batch id : 0
[37, 1] element, prob = 0.022949    (1154, 27)-(1198, 79) batch id : 0
[38, 1] element, prob = 0.022949    (1000, 155)-(1046, 226) batch id : 0
[39, 1] element, prob = 0.022949    (1086, 145)-(1125, 208) batch id : 0
[40, 1] element, prob = 0.022949    (958, 20)-(1030, 84) batch id : 0
[41, 1] element, prob = 0.022949    (934, 65)-(986, 155) batch id : 0
[42, 1] element, prob = 0.022949    (1045, 99)-(1106, 178) batch id : 0
[43, 1] element, prob = 0.022949    (983, -9)-(1141, 105) batch id : 0
[44, 1] element, prob = 0.022949    (1065, -16)-(1192, 124) batch id : 0
[45, 1] element, prob = 0.022949    (922, 81)-(1087, 304) batch id : 0
[46, 1] element, prob = 0.021973    (796, 3)-(834, 47) batch id : 0
[47, 1] element, prob = 0.021973    (911, 4)-(953, 55) batch id : 0
[48, 1] element, prob = 0.021973    (797, 29)-(835, 79) batch id : 0
[49, 1] element, prob = 0.021973    (821, 34)-(855, 81) batch id : 0
[50, 1] element, prob = 0.021973    (910, 28)-(945, 78) batch id : 0
[51, 1] element, prob = 0.021973    (755, 77)-(790, 125) batch id : 0
[52, 1] element, prob = 0.021973    (969, 80)-(1008, 128) batch id : 0
[53, 1] element, prob = 0.021973    (938, 108)-(980, 173) batch id : 0
[54, 1] element, prob = 0.021973    (1123, 186)-(1161, 252) batch id : 0
[55, 1] element, prob = 0.021973    (985, 28)-(1056, 105) batch id : 0
[56, 1] element, prob = 0.021973    (845, 56)-(898, 135) batch id : 0
[57, 1] element, prob = 0.021973    (1023, 62)-(1080, 131) batch id : 0
[58, 1] element, prob = 0.021973    (1041, 40)-(1125, 143) batch id : 0
[59, 1] element, prob = 0.021973    (1035, 116)-(1110, 242) batch id : 0
[60, 1] element, prob = 0.021973    (990, 166)-(1054, 269) batch id : 0
[61, 1] element, prob = 0.021973    (1078, 169)-(1130, 261) batch id : 0
[62, 1] element, prob = 0.021973    (971, 13)-(1140, 235) batch id : 0
[63, 1] element, prob = 0.021973    (980, 125)-(1108, 286) batch id : 0
[64, 1] element, prob = 0.020996    (940, 5)-(987, 55) batch id : 0
[65, 1] element, prob = 0.020996    (991, 0)-(1043, 41) batch id : 0
[66, 1] element, prob = 0.020996    (1126, 0)-(1165, 42) batch id : 0
[67, 1] element, prob = 0.020996    (998, 41)-(1039, 86) batch id : 0
[68, 1] element, prob = 0.020996    (1068, 40)-(1103, 86) batch id : 0
[69, 1] element, prob = 0.020996    (821, 77)-(856, 123) batch id : 0
[70, 1] element, prob = 0.020996    (913, 80)-(949, 128) batch id : 0
[71, 1] element, prob = 0.020996    (821, 112)-(861, 164) batch id : 0
[72, 1] element, prob = 0.020996    (878, 113)-(919, 175) batch id : 0
[73, 1] element, prob = 0.020996    (910, 110)-(950, 170) batch id : 0
[74, 1] element, prob = 0.020996    (1149, 111)-(1189, 163) batch id : 0
[75, 1] element, prob = 0.020996    (870, 149)-(918, 215) batch id : 0
[76, 1] element, prob = 0.020996    (1033, 152)-(1070, 209) batch id : 0
[77, 1] element, prob = 0.020996    (1128, 141)-(1165, 204) batch id : 0
[78, 1] element, prob = 0.020996    (1147, 135)-(1188, 199) batch id : 0
[79, 1] element, prob = 0.020996    (1064, 188)-(1099, 246) batch id : 0
[80, 1] element, prob = 0.020996    (1238, 903)-(1279, 963) batch id : 0
[81, 1] element, prob = 0.020996    (1146, 19)-(1208, 109) batch id : 0
[82, 1] element, prob = 0.020996    (1054, 58)-(1110, 126) batch id : 0
[83, 1] element, prob = 0.020996    (1111, 99)-(1190, 228) batch id : 0
[84, 1] element, prob = 0.020996    (1052, 161)-(1105, 265) batch id : 0
[85, 1] element, prob = 0.020996    (1060, 145)-(1139, 284) batch id : 0
[86, 1] element, prob = 0.020996    (-16, 860)-(79, 985) batch id : 0
[87, 1] element, prob = 0.020996    (1174, -17)-(1281, 136) batch id : 0
[88, 1] element, prob = 0.020996    (1073, 32)-(1198, 190) batch id : 0
[89, 1] element, prob = 0.020996    (950, 188)-(1072, 366) batch id : 0
[90, 1] element, prob = 0.020020    (943, 81)-(978, 130) batch id : 0
[91, 1] element, prob = 0.020020    (968, 104)-(1008, 159) batch id : 0
[92, 1] element, prob = 0.020020    (930, 148)-(973, 211) batch id : 0
[93, 1] element, prob = 0.020020    (1092, 191)-(1128, 246) batch id : 0
[94, 1] element, prob = 0.020020    (1124, 225)-(1161, 291) batch id : 0
[95, 1] element, prob = 0.020020    (1007, -2)-(1083, 55) batch id : 0
[96, 1] element, prob = 0.020020    (890, 60)-(960, 138) batch id : 0
[97, 1] element, prob = 0.020020    (962, 65)-(1023, 137) batch id : 0
[98, 1] element, prob = 0.020020    (1101, 145)-(1183, 284) batch id : 0
[99, 1] element, prob = 0.020020    (866, -10)-(1029, 107) batch id : 0
[100, 1] element, prob = 0.020020    (1005, 179)-(1111, 360) batch id : 0
[101, 1] element, prob = 0.020020    (1019, 342)-(1283, 785) batch id : 0
[102, 1] element, prob = 0.019043    (758, 2)-(795, 45) batch id : 0
[103, 1] element, prob = 0.019043    (756, 38)-(792, 89) batch id : 0
[104, 1] element, prob = 0.019043    (968, 36)-(1011, 82) batch id : 0
[105, 1] element, prob = 0.019043    (729, 77)-(763, 127) batch id : 0
[106, 1] element, prob = 0.019043    (1003, 109)-(1044, 169) batch id : 0
[107, 1] element, prob = 0.019043    (1030, 190)-(1066, 242) batch id : 0
[108, 1] element, prob = 0.019043    (887, 83)-(971, 209) batch id : 0
[109, 1] element, prob = 0.019043    (995, 99)-(1055, 192) batch id : 0
[110, 1] element, prob = 0.019043    (902, 135)-(963, 221) batch id : 0
[ INFO ] Image out_0.bmp created!
[ INFO ] Execution successful
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➜  samples ./c/build/armv7l/Release/object_detection_sample_ssd_c -m face-detection-adas-0001.xml -d MYRIAD -i me.jpg     
[ INFO ] InferenceEngine: 
2.1.2020.3.0-3467-15f2c61a-releases/2020/3
[ INFO ] Parsing input parameters
[ INFO ] Files were added: 1
[ INFO ]     me.jpg
[ INFO ] Loading Inference Engine
[ INFO ] Device info: 
         MYRIAD
         myriadPlugin version ......... 2.1
         Build ......... 2020.3.0-3467-15f2c61a-releases/2020/3
[ INFO ] Loading network files:
	face-detection-adas-0001.xml
	face-detection-adas-0001.bin
[ INFO ] Preparing input blobs
[ WARNING ] Image is resized from (924, 1280) to (672, 384)
[ INFO ] Batch size is 1
[ INFO ] Preparing output blobs
[ INFO ] Loading model to the device
[ INFO ] Create infer request
[ INFO ] Start inference
[ INFO ] Processing output blobs
[0, 1] element, prob = 0.870117    (421, 202)-(745, 608) batch id : 0 WILL BE PRINTED!
[1, 1] element, prob = 0.026855    (-55, 556)-(234, 1100) batch id : 0
[2, 1] element, prob = 0.021973    (20, 925)-(239, 1340) batch id : 0
[3, 1] element, prob = 0.020020    (-18, -144)-(179, 581) batch id : 0
[4, 1] element, prob = 0.018066    (636, 315)-(757, 471) batch id : 0
[5, 1] element, prob = 0.017090    (-27, 672)-(137, 1217) batch id : 0
[6, 1] element, prob = 0.017090    (74, 976)-(364, 1322) batch id : 0
[7, 1] element, prob = 0.016113    (291, 958)-(349, 1122) batch id : 0
[8, 1] element, prob = 0.016113    (329, 963)-(391, 1126) batch id : 0
[9, 1] element, prob = 0.016113    (203, 928)-(423, 1346) batch id : 0
[10, 1] element, prob = 0.015625    (694, 1034)-(732, 1140) batch id : 0
[11, 1] element, prob = 0.015625    (832, 1121)-(937, 1295) batch id : 0
[12, 1] element, prob = 0.015625    (219, 39)-(388, 627) batch id : 0
[13, 1] element, prob = 0.015625    (76, 785)-(357, 1160) batch id : 0
[14, 1] element, prob = 0.014648    (355, 616)-(475, 778) batch id : 0
[15, 1] element, prob = 0.014648    (240, 779)-(331, 1048) batch id : 0
[16, 1] element, prob = 0.013672    (0, 893)-(24, 984) batch id : 0
[17, 1] element, prob = 0.013672    (658, 306)-(752, 405) batch id : 0
[18, 1] element, prob = 0.013672    (204, 1198)-(267, 1275) batch id : 0
[19, 1] element, prob = 0.013672    (322, 611)-(430, 794) batch id : 0
[20, 1] element, prob = 0.013672    (171, 244)-(423, 798) batch id : 0
[21, 1] element, prob = 0.013672    (252, 798)-(539, 1177) batch id : 0
[22, 1] element, prob = 0.013672    (133, 875)-(301, 1420) batch id : 0
[23, 1] element, prob = 0.013672    (443, 202)-(802, 1022) batch id : 0
[24, 1] element, prob = 0.012695    (718, 466)-(757, 540) batch id : 0
[25, 1] element, prob = 0.012695    (166, 527)-(206, 594) batch id : 0
[26, 1] element, prob = 0.012695    (694, 511)-(731, 590) batch id : 0
[27, 1] element, prob = 0.012695    (705, 441)-(771, 555) batch id : 0
[28, 1] element, prob = 0.012695    (272, 962)-(322, 1117) batch id : 0
[29, 1] element, prob = 0.012695    (268, 486)-(376, 688) batch id : 0
[30, 1] element, prob = 0.012695    (323, 489)-(422, 690) batch id : 0
[31, 1] element, prob = 0.012695    (277, 607)-(384, 796) batch id : 0
[32, 1] element, prob = 0.012695    (233, 709)-(342, 924) batch id : 0
[33, 1] element, prob = 0.012695    (256, 672)-(406, 957) batch id : 0
[34, 1] element, prob = 0.012695    (252, 1043)-(431, 1214) batch id : 0
[35, 1] element, prob = 0.011719    (695, 466)-(733, 539) batch id : 0
[36, 1] element, prob = 0.011719    (673, 514)-(709, 587) batch id : 0
[37, 1] element, prob = 0.011719    (370, 1005)-(399, 1078) batch id : 0
[38, 1] element, prob = 0.011719    (670, 256)-(753, 346) batch id : 0
[39, 1] element, prob = 0.011719    (151, 500)-(221, 610) batch id : 0
[40, 1] element, prob = 0.011719    (447, 489)-(511, 608) batch id : 0
[41, 1] element, prob = 0.011719    (126, 625)-(200, 719) batch id : 0
[42, 1] element, prob = 0.011719    (299, 626)-(387, 709) batch id : 0
[43, 1] element, prob = 0.011719    (164, 673)-(251, 747) batch id : 0
[44, 1] element, prob = 0.011719    (398, 850)-(453, 1008) batch id : 0
[45, 1] element, prob = 0.011719    (425, 851)-(476, 1008) batch id : 0
[46, 1] element, prob = 0.011719    (310, 915)-(371, 1077) batch id : 0
[47, 1] element, prob = 0.011719    (357, 925)-(413, 1073) batch id : 0
[48, 1] element, prob = 0.011719    (378, 923)-(435, 1074) batch id : 0
[49, 1] element, prob = 0.011719    (468, 906)-(521, 1070) batch id : 0
[50, 1] element, prob = 0.011719    (284, 1016)-(357, 1188) batch id : 0
[51, 1] element, prob = 0.011719    (203, -95)-(283, 182) batch id : 0
[52, 1] element, prob = 0.011719    (269, 378)-(374, 595) batch id : 0
[53, 1] element, prob = 0.011719    (-22, 575)-(71, 831) batch id : 0
[54, 1] element, prob = 0.011719    (318, 698)-(433, 911) batch id : 0
[55, 1] element, prob = 0.011719    (221, 716)-(395, 1258) batch id : 0
[56, 1] element, prob = 0.011719    (859, 771)-(987, 1518) batch id : 0
[57, 1] element, prob = 0.010742    (145, 466)-(185, 527) batch id : 0
[58, 1] element, prob = 0.010742    (166, 463)-(204, 521) batch id : 0
[59, 1] element, prob = 0.010742    (185, 477)-(224, 537) batch id : 0
[60, 1] element, prob = 0.010742    (214, 477)-(250, 540) batch id : 0
[61, 1] element, prob = 0.010742    (458, 478)-(488, 536) batch id : 0
[62, 1] element, prob = 0.010742    (151, 537)-(177, 594) batch id : 0
[63, 1] element, prob = 0.010742    (460, 522)-(488, 590) batch id : 0
[64, 1] element, prob = 0.010742    (481, 523)-(509, 588) batch id : 0
[65, 1] element, prob = 0.010742    (716, 512)-(755, 592) batch id : 0
[66, 1] element, prob = 0.010742    (129, 581)-(158, 641) batch id : 0
[67, 1] element, prob = 0.010742    (150, 579)-(179, 635) batch id : 0
[68, 1] element, prob = 0.010742    (171, 583)-(201, 641) batch id : 0
[69, 1] element, prob = 0.010742    (457, 564)-(491, 658) batch id : 0
[70, 1] element, prob = 0.010742    (481, 581)-(510, 647) batch id : 0
[71, 1] element, prob = 0.010742    (652, 565)-(689, 650) batch id : 0
[72, 1] element, prob = 0.010742    (674, 571)-(710, 650) batch id : 0
[73, 1] element, prob = 0.010742    (694, 571)-(734, 649) batch id : 0
[74, 1] element, prob = 0.010742    (143, 640)-(188, 705) batch id : 0
[75, 1] element, prob = 0.010742    (191, 743)-(224, 805) batch id : 0
[76, 1] element, prob = 0.010742    (437, 843)-(465, 920) batch id : 0
[77, 1] element, prob = 0.010742    (-1, 943)-(22, 1037) batch id : 0
[78, 1] element, prob = 0.010742    (414, 1007)-(443, 1080) batch id : 0
[79, 1] element, prob = 0.010742    (896, 1206)-(924, 1279) batch id : 0
[80, 1] element, prob = 0.010742    (678, 200)-(743, 294) batch id : 0
[81, 1] element, prob = 0.010742    (70, 374)-(190, 512) batch id : 0
[82, 1] element, prob = 0.010742    (422, 429)-(485, 563) batch id : 0
[83, 1] element, prob = 0.010742    (106, 477)-(213, 629) batch id : 0
[84, 1] element, prob = 0.010742    (428, 477)-(479, 615) batch id : 0
[85, 1] element, prob = 0.010742    (648, 498)-(727, 605) batch id : 0
[86, 1] element, prob = 0.010742    (703, 490)-(769, 614) batch id : 0
[87, 1] element, prob = 0.010742    (16, 531)-(150, 712) batch id : 0
[88, 1] element, prob = 0.010742    (87, 546)-(150, 685) batch id : 0
[89, 1] element, prob = 0.010742    (127, 552)-(197, 653) batch id : 0
[90, 1] element, prob = 0.010742    (425, 557)-(481, 671) batch id : 0
[91, 1] element, prob = 0.010742    (437, 509)-(516, 700) batch id : 0
[92, 1] element, prob = 0.010742    (467, 549)-(524, 678) batch id : 0
[93, 1] element, prob = 0.010742    (633, 551)-(704, 664) batch id : 0
[94, 1] element, prob = 0.010742    (416, 611)-(487, 735) batch id : 0
[95, 1] element, prob = 0.010742    (466, 602)-(530, 734) batch id : 0
[96, 1] element, prob = 0.010742    (124, 673)-(198, 759) batch id : 0
[97, 1] element, prob = 0.010742    (156, 705)-(260, 849) batch id : 0
[98, 1] element, prob = 0.010742    (54, 828)-(137, 933) batch id : 0
[99, 1] element, prob = 0.010742    (451, 812)-(498, 960) batch id : 0
[100, 1] element, prob = 0.010742    (470, 843)-(520, 1004) batch id : 0
[101, 1] element, prob = 0.010742    (447, 897)-(500, 1066) batch id : 0
[102, 1] element, prob = 0.010742    (641, 966)-(706, 1122) batch id : 0
[103, 1] element, prob = 0.010742    (164, 85)-(233, 410) batch id : 0
[104, 1] element, prob = 0.010742    (188, 497)-(307, 688) batch id : 0
[105, 1] element, prob = 0.010742    (205, 445)-(357, 715) batch id : 0
[106, 1] element, prob = 0.010742    (230, 602)-(341, 791) batch id : 0
[107, 1] element, prob = 0.010742    (274, 785)-(390, 1021) batch id : 0
[108, 1] element, prob = 0.010742    (26, 709)-(209, 1239) batch id : 0
[109, 1] element, prob = 0.010742    (309, 868)-(485, 1410) batch id : 0
[110, 1] element, prob = 0.010742    (229, 981)-(575, 1306) batch id : 0
[111, 1] element, prob = 0.010742    (29, 141)-(518, 883) batch id : 0
[ INFO ] Image out_0.bmp created!
[ INFO ] Execution successful

For NCS 1

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➜  samples pwd
/opt/intel/openvino/inference_engine/samples
➜  samples ./c/build/armv7l/Release/object_detection_sample_ssd_c -m face-detection-adas-0001.xml -d MYRIAD -i parents.jpg
[ INFO ] InferenceEngine: 
2.1.2020.3.0-3467-15f2c61a-releases/2020/3
[ INFO ] Parsing input parameters
[ INFO ] Files were added: 1
[ INFO ]     parents.jpg
[ INFO ] Loading Inference Engine
[ INFO ] Device info: 
         MYRIAD
         myriadPlugin version ......... 2.1
         Build ......... 2020.3.0-3467-15f2c61a-releases/2020/3
[ INFO ] Loading network files:
	face-detection-adas-0001.xml
	face-detection-adas-0001.bin
[ INFO ] Preparing input blobs
[ WARNING ] Image is resized from (1280, 960) to (672, 384)
[ INFO ] Batch size is 1
[ INFO ] Preparing output blobs
[ INFO ] Loading model to the device
[ INFO ] Create infer request
[ INFO ] Start inference
[ INFO ] Processing output blobs
[0, 1] element, prob = 1.000000    (826, 366)-(1026, 644) batch id : 0 WILL BE PRINTED!
[1, 1] element, prob = 0.996582    (539, 173)-(693, 429) batch id : 0 WILL BE PRINTED!
[2, 1] element, prob = 0.552734    (1094, 47)-(1135, 95) batch id : 0 WILL BE PRINTED!
[3, 1] element, prob = 0.221680    (848, 22)-(886, 70) batch id : 0
[4, 1] element, prob = 0.053711    (8, 784)-(151, 956) batch id : 0
[5, 1] element, prob = 0.039551    (1033, 78)-(1070, 122) batch id : 0
[6, 1] element, prob = 0.034668    (1123, 75)-(1158, 122) batch id : 0
[7, 1] element, prob = 0.032227    (1086, 14)-(1138, 85) batch id : 0
[8, 1] element, prob = 0.031250    (1091, 110)-(1130, 171) batch id : 0
[9, 1] element, prob = 0.030273    (1108, 43)-(1150, 93) batch id : 0
[10, 1] element, prob = 0.029297    (1151, 83)-(1190, 131) batch id : 0
[11, 1] element, prob = 0.027344    (1091, 6)-(1133, 55) batch id : 0
[12, 1] element, prob = 0.027344    (1064, 73)-(1098, 121) batch id : 0
[13, 1] element, prob = 0.026855    (1030, 2)-(1073, 42) batch id : 0
[14, 1] element, prob = 0.026855    (1058, 113)-(1096, 168) batch id : 0
[15, 1] element, prob = 0.026855    (1047, -3)-(1110, 59) batch id : 0
[16, 1] element, prob = 0.026855    (1142, 75)-(1211, 165) batch id : 0
[17, 1] element, prob = 0.026855    (849, 14)-(1030, 236) batch id : 0
[18, 1] element, prob = 0.025879    (941, 22)-(981, 74) batch id : 0
[19, 1] element, prob = 0.025879    (1030, 36)-(1074, 89) batch id : 0
[20, 1] element, prob = 0.025879    (876, 73)-(915, 118) batch id : 0
[21, 1] element, prob = 0.025879    (845, 112)-(886, 167) batch id : 0
[22, 1] element, prob = 0.025879    (1036, 110)-(1076, 171) batch id : 0
[23, 1] element, prob = 0.025879    (1113, -1)-(1175, 59) batch id : 0
[24, 1] element, prob = 0.025879    (1003, 41)-(1094, 151) batch id : 0
[25, 1] element, prob = 0.024902    (1059, 0)-(1098, 43) batch id : 0
[26, 1] element, prob = 0.024902    (1002, 69)-(1048, 123) batch id : 0
[27, 1] element, prob = 0.024902    (1133, 105)-(1170, 164) batch id : 0
[28, 1] element, prob = 0.023926    (966, 1)-(1018, 45) batch id : 0
[29, 1] element, prob = 0.023926    (876, 29)-(918, 79) batch id : 0
[30, 1] element, prob = 0.023926    (1056, 145)-(1094, 208) batch id : 0
[31, 1] element, prob = 0.023926    (861, 2)-(918, 63) batch id : 0
[32, 1] element, prob = 0.023926    (1010, 84)-(1092, 204) batch id : 0
[33, 1] element, prob = 0.023926    (1022, 139)-(1076, 224) batch id : 0
[34, 1] element, prob = 0.023926    (942, 35)-(1072, 208) batch id : 0
[35, 1] element, prob = 0.022949    (811, 3)-(851, 48) batch id : 0
[36, 1] element, prob = 0.022949    (1098, 63)-(1131, 109) batch id : 0
[37, 1] element, prob = 0.022949    (983, -9)-(1141, 105) batch id : 0
[38, 1] element, prob = 0.022949    (921, 81)-(1087, 304) batch id : 0
[39, 1] element, prob = 0.021973    (796, 3)-(835, 47) batch id : 0
[40, 1] element, prob = 0.021973    (911, 4)-(953, 55) batch id : 0
[41, 1] element, prob = 0.021973    (797, 29)-(835, 79) batch id : 0
[42, 1] element, prob = 0.021973    (1154, 27)-(1198, 79) batch id : 0
[43, 1] element, prob = 0.021973    (969, 80)-(1008, 128) batch id : 0
[44, 1] element, prob = 0.021973    (1000, 155)-(1046, 226) batch id : 0
[45, 1] element, prob = 0.021973    (1086, 145)-(1125, 208) batch id : 0
[46, 1] element, prob = 0.021973    (1123, 186)-(1161, 252) batch id : 0
[47, 1] element, prob = 0.021973    (958, 20)-(1030, 84) batch id : 0
[48, 1] element, prob = 0.021973    (985, 28)-(1056, 105) batch id : 0
[49, 1] element, prob = 0.021973    (845, 56)-(898, 134) batch id : 0
[50, 1] element, prob = 0.021973    (934, 65)-(986, 155) batch id : 0
[51, 1] element, prob = 0.021973    (1046, 99)-(1107, 178) batch id : 0
[52, 1] element, prob = 0.021973    (1035, 116)-(1110, 241) batch id : 0
[53, 1] element, prob = 0.021973    (990, 166)-(1054, 269) batch id : 0
[54, 1] element, prob = 0.021973    (1065, -16)-(1192, 124) batch id : 0
[55, 1] element, prob = 0.021973    (971, 13)-(1140, 235) batch id : 0
[56, 1] element, prob = 0.021973    (980, 125)-(1108, 286) batch id : 0
[57, 1] element, prob = 0.020996    (991, 0)-(1043, 41) batch id : 0
[58, 1] element, prob = 0.020996    (1126, 0)-(1165, 42) batch id : 0
[59, 1] element, prob = 0.020996    (821, 35)-(856, 79) batch id : 0
[60, 1] element, prob = 0.020996    (910, 28)-(945, 78) batch id : 0
[61, 1] element, prob = 0.020996    (997, 41)-(1038, 86) batch id : 0
[62, 1] element, prob = 0.020996    (1068, 40)-(1103, 86) batch id : 0
[63, 1] element, prob = 0.020996    (754, 77)-(789, 125) batch id : 0
[64, 1] element, prob = 0.020996    (819, 77)-(855, 123) batch id : 0
[65, 1] element, prob = 0.020996    (913, 79)-(949, 128) batch id : 0
[66, 1] element, prob = 0.020996    (821, 112)-(861, 164) batch id : 0
[67, 1] element, prob = 0.020996    (938, 108)-(980, 173) batch id : 0
[68, 1] element, prob = 0.020996    (1149, 112)-(1189, 162) batch id : 0
[69, 1] element, prob = 0.020996    (870, 149)-(918, 215) batch id : 0
[70, 1] element, prob = 0.020996    (1033, 152)-(1070, 209) batch id : 0
[71, 1] element, prob = 0.020996    (1064, 188)-(1099, 246) batch id : 0
[72, 1] element, prob = 0.020996    (1023, 63)-(1081, 131) batch id : 0
[73, 1] element, prob = 0.020996    (1054, 58)-(1110, 126) batch id : 0
[74, 1] element, prob = 0.020996    (1041, 40)-(1125, 143) batch id : 0
[75, 1] element, prob = 0.020996    (1111, 99)-(1190, 228) batch id : 0
[76, 1] element, prob = 0.020996    (1078, 169)-(1130, 261) batch id : 0
[77, 1] element, prob = 0.020996    (950, 189)-(1072, 367) batch id : 0
[78, 1] element, prob = 0.020020    (940, 6)-(987, 56) batch id : 0
[79, 1] element, prob = 0.020020    (943, 81)-(978, 130) batch id : 0
[80, 1] element, prob = 0.020020    (878, 114)-(919, 175) batch id : 0
[81, 1] element, prob = 0.020020    (910, 110)-(950, 170) batch id : 0
[82, 1] element, prob = 0.020020    (968, 104)-(1008, 159) batch id : 0
[83, 1] element, prob = 0.020020    (1128, 141)-(1165, 204) batch id : 0
[84, 1] element, prob = 0.020020    (1147, 135)-(1188, 199) batch id : 0
[85, 1] element, prob = 0.020020    (1238, 903)-(1279, 963) batch id : 0
[86, 1] element, prob = 0.020020    (1007, -2)-(1083, 55) batch id : 0
[87, 1] element, prob = 0.020020    (1052, 161)-(1105, 266) batch id : 0
[88, 1] element, prob = 0.020020    (1061, 145)-(1140, 284) batch id : 0
[89, 1] element, prob = 0.020020    (-16, 860)-(79, 985) batch id : 0
[90, 1] element, prob = 0.020020    (866, -10)-(1029, 107) batch id : 0
[91, 1] element, prob = 0.020020    (1173, -17)-(1280, 136) batch id : 0
[92, 1] element, prob = 0.020020    (1072, 32)-(1197, 189) batch id : 0
[93, 1] element, prob = 0.020020    (1005, 179)-(1111, 359) batch id : 0
[94, 1] element, prob = 0.019043    (758, 2)-(795, 45) batch id : 0
[95, 1] element, prob = 0.019043    (968, 35)-(1011, 82) batch id : 0
[96, 1] element, prob = 0.019043    (1003, 109)-(1044, 169) batch id : 0
[97, 1] element, prob = 0.019043    (930, 148)-(973, 211) batch id : 0
[98, 1] element, prob = 0.019043    (1092, 191)-(1128, 246) batch id : 0
[99, 1] element, prob = 0.019043    (1123, 225)-(1161, 291) batch id : 0
[100, 1] element, prob = 0.019043    (1146, 19)-(1208, 109) batch id : 0
[101, 1] element, prob = 0.019043    (897, 59)-(958, 143) batch id : 0
[102, 1] element, prob = 0.019043    (962, 65)-(1023, 137) batch id : 0
[103, 1] element, prob = 0.019043    (886, 83)-(971, 209) batch id : 0
[104, 1] element, prob = 0.019043    (995, 99)-(1055, 192) batch id : 0
[105, 1] element, prob = 0.019043    (902, 135)-(963, 221) batch id : 0
[106, 1] element, prob = 0.019043    (1101, 145)-(1183, 284) batch id : 0
[107, 1] element, prob = 0.019043    (821, -17)-(946, 129) batch id : 0
[108, 1] element, prob = 0.019043    (940, -16)-(1076, 120) batch id : 0
[109, 1] element, prob = 0.019043    (819, 34)-(945, 205) batch id : 0
[110, 1] element, prob = 0.019043    (1019, 342)-(1283, 785) batch id : 0
[111, 1] element, prob = 0.018066    (1147, 7)-(1193, 54) batch id : 0
[112, 1] element, prob = 0.018066    (756, 38)-(792, 89) batch id : 0
[ INFO ] Image out_0.bmp created!
[ INFO ] Execution successful
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➜  samples ./c/build/armv7l/Release/object_detection_sample_ssd_c -m face-detection-adas-0001.xml -d MYRIAD -i me.jpg     
[ INFO ] InferenceEngine: 
2.1.2020.3.0-3467-15f2c61a-releases/2020/3
[ INFO ] Parsing input parameters
[ INFO ] Files were added: 1
[ INFO ]     me.jpg
[ INFO ] Loading Inference Engine
[ INFO ] Device info: 
         MYRIAD
         myriadPlugin version ......... 2.1
         Build ......... 2020.3.0-3467-15f2c61a-releases/2020/3
[ INFO ] Loading network files:
	face-detection-adas-0001.xml
	face-detection-adas-0001.bin
[ INFO ] Preparing input blobs
[ WARNING ] Image is resized from (924, 1280) to (672, 384)
[ INFO ] Batch size is 1
[ INFO ] Preparing output blobs
[ INFO ] Loading model to the device
[ INFO ] Create infer request
[ INFO ] Start inference
[ INFO ] Processing output blobs
[0, 1] element, prob = 0.871094    (420, 202)-(745, 608) batch id : 0 WILL BE PRINTED!
[1, 1] element, prob = 0.026855    (-54, 555)-(234, 1100) batch id : 0
[2, 1] element, prob = 0.020996    (21, 925)-(239, 1340) batch id : 0
[3, 1] element, prob = 0.019043    (-18, -144)-(178, 583) batch id : 0
[4, 1] element, prob = 0.018066    (636, 315)-(757, 470) batch id : 0
[5, 1] element, prob = 0.017090    (75, 977)-(363, 1321) batch id : 0
[6, 1] element, prob = 0.016113    (291, 958)-(349, 1123) batch id : 0
[7, 1] element, prob = 0.015625    (329, 962)-(391, 1126) batch id : 0
[8, 1] element, prob = 0.015625    (832, 1121)-(937, 1293) batch id : 0
[9, 1] element, prob = 0.015625    (-27, 670)-(136, 1220) batch id : 0
[10, 1] element, prob = 0.015625    (76, 785)-(357, 1160) batch id : 0
[11, 1] element, prob = 0.015625    (204, 928)-(423, 1346) batch id : 0
[12, 1] element, prob = 0.014648    (694, 1035)-(732, 1141) batch id : 0
[13, 1] element, prob = 0.014648    (219, 40)-(388, 626) batch id : 0
[14, 1] element, prob = 0.013672    (658, 306)-(752, 405) batch id : 0
[15, 1] element, prob = 0.013672    (204, 1198)-(267, 1275) batch id : 0
[16, 1] element, prob = 0.013672    (322, 612)-(430, 793) batch id : 0
[17, 1] element, prob = 0.013672    (240, 779)-(331, 1046) batch id : 0
[18, 1] element, prob = 0.013672    (171, 245)-(424, 798) batch id : 0
[19, 1] element, prob = 0.013672    (443, 205)-(802, 1021) batch id : 0
[20, 1] element, prob = 0.012695    (694, 511)-(731, 590) batch id : 0
[21, 1] element, prob = 0.012695    (0, 893)-(24, 984) batch id : 0
[22, 1] element, prob = 0.012695    (272, 962)-(322, 1117) batch id : 0
[23, 1] element, prob = 0.012695    (268, 487)-(376, 688) batch id : 0
[24, 1] element, prob = 0.012695    (323, 490)-(422, 690) batch id : 0
[25, 1] element, prob = 0.012695    (277, 608)-(384, 795) batch id : 0
[26, 1] element, prob = 0.012695    (233, 710)-(342, 923) batch id : 0
[27, 1] element, prob = 0.012695    (256, 672)-(406, 957) batch id : 0
[28, 1] element, prob = 0.012695    (252, 799)-(540, 1176) batch id : 0
[29, 1] element, prob = 0.012695    (133, 876)-(301, 1420) batch id : 0
[30, 1] element, prob = 0.011719    (718, 466)-(757, 540) batch id : 0
[31, 1] element, prob = 0.011719    (166, 527)-(206, 594) batch id : 0
[32, 1] element, prob = 0.011719    (673, 514)-(709, 587) batch id : 0
[33, 1] element, prob = 0.011719    (-1, 943)-(22, 1038) batch id : 0
[34, 1] element, prob = 0.011719    (670, 256)-(753, 346) batch id : 0
[35, 1] element, prob = 0.011719    (705, 441)-(771, 555) batch id : 0
[36, 1] element, prob = 0.011719    (126, 625)-(200, 718) batch id : 0
[37, 1] element, prob = 0.011719    (164, 673)-(251, 747) batch id : 0
[38, 1] element, prob = 0.011719    (399, 850)-(453, 1008) batch id : 0
[39, 1] element, prob = 0.011719    (424, 851)-(476, 1009) batch id : 0
[40, 1] element, prob = 0.011719    (357, 925)-(413, 1073) batch id : 0
[41, 1] element, prob = 0.011719    (399, 919)-(454, 1074) batch id : 0
[42, 1] element, prob = 0.011719    (468, 906)-(521, 1070) batch id : 0
[43, 1] element, prob = 0.011719    (284, 1016)-(357, 1186) batch id : 0
[44, 1] element, prob = 0.011719    (269, 379)-(374, 595) batch id : 0
[45, 1] element, prob = 0.011719    (349, 563)-(485, 831) batch id : 0
[46, 1] element, prob = 0.011719    (252, 1042)-(431, 1215) batch id : 0
[47, 1] element, prob = 0.011719    (858, 771)-(987, 1518) batch id : 0
[48, 1] element, prob = 0.010742    (165, 463)-(204, 521) batch id : 0
[49, 1] element, prob = 0.010742    (185, 477)-(224, 537) batch id : 0
[50, 1] element, prob = 0.010742    (214, 477)-(250, 540) batch id : 0
[51, 1] element, prob = 0.010742    (458, 478)-(488, 536) batch id : 0
[52, 1] element, prob = 0.010742    (695, 466)-(733, 539) batch id : 0
[53, 1] element, prob = 0.010742    (460, 522)-(488, 590) batch id : 0
[54, 1] element, prob = 0.010742    (481, 523)-(509, 588) batch id : 0
[55, 1] element, prob = 0.010742    (716, 512)-(755, 592) batch id : 0
[56, 1] element, prob = 0.010742    (150, 579)-(179, 635) batch id : 0
[57, 1] element, prob = 0.010742    (171, 584)-(201, 641) batch id : 0
[58, 1] element, prob = 0.010742    (481, 582)-(510, 647) batch id : 0
[59, 1] element, prob = 0.010742    (652, 565)-(689, 650) batch id : 0
[60, 1] element, prob = 0.010742    (674, 571)-(710, 650) batch id : 0
[61, 1] element, prob = 0.010742    (694, 571)-(734, 648) batch id : 0
[62, 1] element, prob = 0.010742    (142, 640)-(188, 705) batch id : 0
[63, 1] element, prob = 0.010742    (335, 627)-(390, 695) batch id : 0
[64, 1] element, prob = 0.010742    (191, 744)-(225, 803) batch id : 0
[65, 1] element, prob = 0.010742    (370, 1005)-(399, 1078) batch id : 0
[66, 1] element, prob = 0.010742    (414, 1006)-(443, 1079) batch id : 0
[67, 1] element, prob = 0.010742    (896, 1207)-(924, 1280) batch id : 0
[68, 1] element, prob = 0.010742    (86, 386)-(160, 513) batch id : 0
[69, 1] element, prob = 0.010742    (106, 477)-(214, 630) batch id : 0
[70, 1] element, prob = 0.010742    (151, 501)-(221, 610) batch id : 0
[71, 1] element, prob = 0.010742    (428, 478)-(479, 615) batch id : 0
[72, 1] element, prob = 0.010742    (450, 478)-(506, 611) batch id : 0
[73, 1] element, prob = 0.010742    (647, 498)-(728, 605) batch id : 0
[74, 1] element, prob = 0.010742    (148, 556)-(220, 658) batch id : 0
[75, 1] element, prob = 0.010742    (425, 557)-(481, 671) batch id : 0
[76, 1] element, prob = 0.010742    (437, 510)-(516, 700) batch id : 0
[77, 1] element, prob = 0.010742    (463, 554)-(527, 674) batch id : 0
[78, 1] element, prob = 0.010742    (633, 551)-(704, 664) batch id : 0
[79, 1] element, prob = 0.010742    (280, 628)-(367, 711) batch id : 0
[80, 1] element, prob = 0.010742    (416, 612)-(488, 735) batch id : 0
[81, 1] element, prob = 0.010742    (155, 706)-(260, 850) batch id : 0
[82, 1] element, prob = 0.010742    (55, 828)-(137, 932) batch id : 0
[83, 1] element, prob = 0.010742    (451, 813)-(498, 960) batch id : 0
[84, 1] element, prob = 0.010742    (310, 914)-(371, 1076) batch id : 0
[85, 1] element, prob = 0.010742    (447, 896)-(500, 1065) batch id : 0
[86, 1] element, prob = 0.010742    (641, 966)-(706, 1121) batch id : 0
[87, 1] element, prob = 0.010742    (203, -95)-(283, 182) batch id : 0
[88, 1] element, prob = 0.010742    (164, 86)-(233, 410) batch id : 0
[89, 1] element, prob = 0.010742    (188, 498)-(307, 688) batch id : 0
[90, 1] element, prob = 0.010742    (205, 445)-(357, 715) batch id : 0
[91, 1] element, prob = 0.010742    (-22, 576)-(71, 832) batch id : 0
[92, 1] element, prob = 0.010742    (230, 603)-(342, 791) batch id : 0
[93, 1] element, prob = 0.010742    (318, 698)-(433, 909) batch id : 0
[94, 1] element, prob = 0.010742    (273, 784)-(390, 1021) batch id : 0
[95, 1] element, prob = 0.010742    (175, 740)-(444, 1237) batch id : 0
[ INFO ] Image out_0.bmp created!
[ INFO ] Execution successful

3.3.2 C++

For NCS 2

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➜  samples ./cpp/build/armv7l/Release/object_detection_sample_ssd -m face-detection-adas-0001.xml -d MYRIAD -i me.jpg 
[ INFO ] InferenceEngine: 
	API version ............ 2.1
	Build .................. 2020.3.0-3467-15f2c61a-releases/2020/3
	Description ....... API
Parsing input parameters
[ INFO ] Files were added: 1
[ INFO ]     me.jpg
[ INFO ] Loading Inference Engine
[ INFO ] Device info: 
	MYRIAD
	myriadPlugin version ......... 2.1
	Build ........... 2020.3.0-3467-15f2c61a-releases/2020/3
[ INFO ] Loading network files:
	face-detection-adas-0001.xml
	face-detection-adas-0001.bin
[ INFO ] Preparing input blobs
[ INFO ] Batch size is 1
[ INFO ] Preparing output blobs
[ INFO ] Loading model to the device
[ INFO ] Create infer request
[ WARNING ] Image is resized from (924, 1280) to (672, 384)
[ INFO ] Batch size is 1
[ INFO ] Start inference
[ INFO ] Processing output blobs
[0,1] element, prob = 0.870117    (421,202)-(745,608) batch id : 0 WILL BE PRINTED!
[1,1] element, prob = 0.0268555    (-55,556)-(234,1100) batch id : 0
[2,1] element, prob = 0.0219727    (20,925)-(239,1340) batch id : 0
[3,1] element, prob = 0.0200195    (-18,-144)-(179,581) batch id : 0
[4,1] element, prob = 0.0180664    (636,315)-(757,471) batch id : 0
[5,1] element, prob = 0.0170898    (-27,672)-(137,1217) batch id : 0
[6,1] element, prob = 0.0170898    (74,976)-(364,1322) batch id : 0
[7,1] element, prob = 0.0161133    (291,958)-(349,1122) batch id : 0
[8,1] element, prob = 0.0161133    (329,963)-(391,1126) batch id : 0
[9,1] element, prob = 0.0161133    (203,928)-(423,1346) batch id : 0
[10,1] element, prob = 0.015625    (694,1034)-(732,1140) batch id : 0
[11,1] element, prob = 0.015625    (832,1121)-(937,1295) batch id : 0
[12,1] element, prob = 0.015625    (219,39)-(388,627) batch id : 0
[13,1] element, prob = 0.015625    (76,785)-(357,1160) batch id : 0
[14,1] element, prob = 0.0146484    (355,616)-(475,778) batch id : 0
[15,1] element, prob = 0.0146484    (240,779)-(331,1048) batch id : 0
[16,1] element, prob = 0.0136719    (0,893)-(24,984) batch id : 0
[17,1] element, prob = 0.0136719    (658,306)-(752,405) batch id : 0
[18,1] element, prob = 0.0136719    (204,1198)-(267,1275) batch id : 0
[19,1] element, prob = 0.0136719    (322,611)-(430,794) batch id : 0
[20,1] element, prob = 0.0136719    (171,244)-(423,798) batch id : 0
[21,1] element, prob = 0.0136719    (252,798)-(539,1177) batch id : 0
[22,1] element, prob = 0.0136719    (133,875)-(301,1420) batch id : 0
[23,1] element, prob = 0.0136719    (443,202)-(802,1022) batch id : 0
[24,1] element, prob = 0.0126953    (718,466)-(757,540) batch id : 0
[25,1] element, prob = 0.0126953    (166,527)-(206,594) batch id : 0
[26,1] element, prob = 0.0126953    (694,511)-(731,590) batch id : 0
[27,1] element, prob = 0.0126953    (705,441)-(771,555) batch id : 0
[28,1] element, prob = 0.0126953    (272,962)-(322,1117) batch id : 0
[29,1] element, prob = 0.0126953    (268,486)-(376,688) batch id : 0
[30,1] element, prob = 0.0126953    (323,489)-(422,690) batch id : 0
[31,1] element, prob = 0.0126953    (277,607)-(384,796) batch id : 0
[32,1] element, prob = 0.0126953    (233,709)-(342,924) batch id : 0
[33,1] element, prob = 0.0126953    (256,672)-(406,957) batch id : 0
[34,1] element, prob = 0.0126953    (252,1043)-(431,1214) batch id : 0
[35,1] element, prob = 0.0117188    (695,466)-(733,539) batch id : 0
[36,1] element, prob = 0.0117188    (673,514)-(709,587) batch id : 0
[37,1] element, prob = 0.0117188    (370,1005)-(399,1078) batch id : 0
[38,1] element, prob = 0.0117188    (670,256)-(753,346) batch id : 0
[39,1] element, prob = 0.0117188    (151,500)-(221,610) batch id : 0
[40,1] element, prob = 0.0117188    (447,489)-(511,608) batch id : 0
[41,1] element, prob = 0.0117188    (126,625)-(200,719) batch id : 0
[42,1] element, prob = 0.0117188    (299,626)-(387,709) batch id : 0
[43,1] element, prob = 0.0117188    (164,673)-(251,747) batch id : 0
[44,1] element, prob = 0.0117188    (398,850)-(453,1008) batch id : 0
[45,1] element, prob = 0.0117188    (425,851)-(476,1008) batch id : 0
[46,1] element, prob = 0.0117188    (310,915)-(371,1077) batch id : 0
[47,1] element, prob = 0.0117188    (357,925)-(413,1073) batch id : 0
[48,1] element, prob = 0.0117188    (378,923)-(435,1074) batch id : 0
[49,1] element, prob = 0.0117188    (468,906)-(521,1070) batch id : 0
[50,1] element, prob = 0.0117188    (284,1016)-(357,1188) batch id : 0
[51,1] element, prob = 0.0117188    (203,-95)-(283,182) batch id : 0
[52,1] element, prob = 0.0117188    (269,378)-(374,595) batch id : 0
[53,1] element, prob = 0.0117188    (-22,575)-(71,831) batch id : 0
[54,1] element, prob = 0.0117188    (318,698)-(433,911) batch id : 0
[55,1] element, prob = 0.0117188    (221,716)-(395,1258) batch id : 0
[56,1] element, prob = 0.0117188    (859,771)-(987,1518) batch id : 0
[57,1] element, prob = 0.0107422    (145,466)-(185,527) batch id : 0
[58,1] element, prob = 0.0107422    (166,463)-(204,521) batch id : 0
[59,1] element, prob = 0.0107422    (185,477)-(224,537) batch id : 0
[60,1] element, prob = 0.0107422    (214,477)-(250,540) batch id : 0
[61,1] element, prob = 0.0107422    (458,478)-(488,536) batch id : 0
[62,1] element, prob = 0.0107422    (151,537)-(177,594) batch id : 0
[63,1] element, prob = 0.0107422    (460,522)-(488,590) batch id : 0
[64,1] element, prob = 0.0107422    (481,523)-(509,588) batch id : 0
[65,1] element, prob = 0.0107422    (716,512)-(755,592) batch id : 0
[66,1] element, prob = 0.0107422    (129,581)-(158,641) batch id : 0
[67,1] element, prob = 0.0107422    (150,579)-(179,635) batch id : 0
[68,1] element, prob = 0.0107422    (171,583)-(201,641) batch id : 0
[69,1] element, prob = 0.0107422    (457,564)-(491,658) batch id : 0
[70,1] element, prob = 0.0107422    (481,581)-(510,647) batch id : 0
[71,1] element, prob = 0.0107422    (652,565)-(689,650) batch id : 0
[72,1] element, prob = 0.0107422    (674,571)-(710,650) batch id : 0
[73,1] element, prob = 0.0107422    (694,571)-(734,649) batch id : 0
[74,1] element, prob = 0.0107422    (143,640)-(188,705) batch id : 0
[75,1] element, prob = 0.0107422    (191,743)-(224,805) batch id : 0
[76,1] element, prob = 0.0107422    (437,843)-(465,920) batch id : 0
[77,1] element, prob = 0.0107422    (-1,943)-(22,1037) batch id : 0
[78,1] element, prob = 0.0107422    (414,1007)-(443,1080) batch id : 0
[79,1] element, prob = 0.0107422    (896,1206)-(924,1279) batch id : 0
[80,1] element, prob = 0.0107422    (678,200)-(743,294) batch id : 0
[81,1] element, prob = 0.0107422    (70,374)-(190,512) batch id : 0
[82,1] element, prob = 0.0107422    (422,429)-(485,563) batch id : 0
[83,1] element, prob = 0.0107422    (106,477)-(213,629) batch id : 0
[84,1] element, prob = 0.0107422    (428,477)-(479,615) batch id : 0
[85,1] element, prob = 0.0107422    (648,498)-(727,605) batch id : 0
[86,1] element, prob = 0.0107422    (703,490)-(769,614) batch id : 0
[87,1] element, prob = 0.0107422    (16,531)-(150,712) batch id : 0
[88,1] element, prob = 0.0107422    (87,546)-(150,685) batch id : 0
[89,1] element, prob = 0.0107422    (127,552)-(197,653) batch id : 0
[90,1] element, prob = 0.0107422    (425,557)-(481,671) batch id : 0
[91,1] element, prob = 0.0107422    (437,509)-(516,700) batch id : 0
[92,1] element, prob = 0.0107422    (467,549)-(524,678) batch id : 0
[93,1] element, prob = 0.0107422    (633,551)-(704,664) batch id : 0
[94,1] element, prob = 0.0107422    (416,611)-(487,735) batch id : 0
[95,1] element, prob = 0.0107422    (466,602)-(530,734) batch id : 0
[96,1] element, prob = 0.0107422    (124,673)-(198,759) batch id : 0
[97,1] element, prob = 0.0107422    (156,705)-(260,849) batch id : 0
[98,1] element, prob = 0.0107422    (54,828)-(137,933) batch id : 0
[99,1] element, prob = 0.0107422    (451,812)-(498,960) batch id : 0
[100,1] element, prob = 0.0107422    (470,843)-(520,1004) batch id : 0
[101,1] element, prob = 0.0107422    (447,897)-(500,1066) batch id : 0
[102,1] element, prob = 0.0107422    (641,966)-(706,1122) batch id : 0
[103,1] element, prob = 0.0107422    (164,85)-(233,410) batch id : 0
[104,1] element, prob = 0.0107422    (188,497)-(307,688) batch id : 0
[105,1] element, prob = 0.0107422    (205,445)-(357,715) batch id : 0
[106,1] element, prob = 0.0107422    (230,602)-(341,791) batch id : 0
[107,1] element, prob = 0.0107422    (274,785)-(390,1021) batch id : 0
[108,1] element, prob = 0.0107422    (26,709)-(209,1239) batch id : 0
[109,1] element, prob = 0.0107422    (309,868)-(485,1410) batch id : 0
[110,1] element, prob = 0.0107422    (229,981)-(575,1306) batch id : 0
[111,1] element, prob = 0.0107422    (29,141)-(518,883) batch id : 0
[ INFO ] Image out_0.bmp created!
[ INFO ] Execution successful

[ INFO ] This sample is an API example, for any performance measurements please use the dedicated benchmark_app tool
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➜  samples ./cpp/build/armv7l/Release/object_detection_sample_ssd -m face-detection-adas-0001.xml -d MYRIAD -i parents.jpg
[ INFO ] InferenceEngine: 
	API version ............ 2.1
	Build .................. 2020.3.0-3467-15f2c61a-releases/2020/3
	Description ....... API
Parsing input parameters
[ INFO ] Files were added: 1
[ INFO ]     parents.jpg
[ INFO ] Loading Inference Engine
[ INFO ] Device info: 
	MYRIAD
	myriadPlugin version ......... 2.1
	Build ........... 2020.3.0-3467-15f2c61a-releases/2020/3
[ INFO ] Loading network files:
	face-detection-adas-0001.xml
	face-detection-adas-0001.bin
[ INFO ] Preparing input blobs
[ INFO ] Batch size is 1
[ INFO ] Preparing output blobs
[ INFO ] Loading model to the device
[ INFO ] Create infer request
[ WARNING ] Image is resized from (1280, 960) to (672, 384)
[ INFO ] Batch size is 1
[ INFO ] Start inference
[ INFO ] Processing output blobs
[0,1] element, prob = 1    (826,366)-(1026,644) batch id : 0 WILL BE PRINTED!
[1,1] element, prob = 0.996582    (539,173)-(693,429) batch id : 0 WILL BE PRINTED!
[2,1] element, prob = 0.539062    (1094,47)-(1135,96) batch id : 0 WILL BE PRINTED!
[3,1] element, prob = 0.212402    (848,22)-(886,70) batch id : 0
[4,1] element, prob = 0.0527344    (7,783)-(151,957) batch id : 0
[5,1] element, prob = 0.0395508    (1033,78)-(1070,122) batch id : 0
[6,1] element, prob = 0.034668    (1123,75)-(1158,122) batch id : 0
[7,1] element, prob = 0.0322266    (1091,109)-(1130,171) batch id : 0
[8,1] element, prob = 0.0322266    (1086,14)-(1138,85) batch id : 0
[9,1] element, prob = 0.0302734    (1109,43)-(1150,93) batch id : 0
[10,1] element, prob = 0.0292969    (1151,83)-(1190,131) batch id : 0
[11,1] element, prob = 0.0273438    (1030,2)-(1073,43) batch id : 0
[12,1] element, prob = 0.0273438    (1091,6)-(1133,55) batch id : 0
[13,1] element, prob = 0.0273438    (1064,73)-(1098,121) batch id : 0
[14,1] element, prob = 0.0273438    (1058,113)-(1096,168) batch id : 0
[15,1] element, prob = 0.0273438    (1143,74)-(1211,165) batch id : 0
[16,1] element, prob = 0.0268555    (876,73)-(915,118) batch id : 0
[17,1] element, prob = 0.0268555    (1047,-3)-(1110,59) batch id : 0
[18,1] element, prob = 0.0268555    (849,13)-(1030,236) batch id : 0
[19,1] element, prob = 0.0258789    (941,22)-(981,74) batch id : 0
[20,1] element, prob = 0.0258789    (1030,35)-(1074,89) batch id : 0
[21,1] element, prob = 0.0258789    (845,112)-(886,167) batch id : 0
[22,1] element, prob = 0.0258789    (1036,111)-(1076,171) batch id : 0
[23,1] element, prob = 0.0258789    (1113,-1)-(1175,59) batch id : 0
[24,1] element, prob = 0.0258789    (1002,40)-(1093,151) batch id : 0
[25,1] element, prob = 0.0249023    (1059,0)-(1098,43) batch id : 0
[26,1] element, prob = 0.0249023    (876,29)-(918,79) batch id : 0
[27,1] element, prob = 0.0249023    (1002,69)-(1048,123) batch id : 0
[28,1] element, prob = 0.0249023    (1133,104)-(1170,163) batch id : 0
[29,1] element, prob = 0.0249023    (1056,145)-(1094,208) batch id : 0
[30,1] element, prob = 0.0249023    (861,2)-(918,63) batch id : 0
[31,1] element, prob = 0.0239258    (966,1)-(1018,45) batch id : 0
[32,1] element, prob = 0.0239258    (1098,63)-(1131,109) batch id : 0
[33,1] element, prob = 0.0239258    (1010,84)-(1092,204) batch id : 0
[34,1] element, prob = 0.0239258    (1022,139)-(1076,224) batch id : 0
[35,1] element, prob = 0.0239258    (943,34)-(1073,208) batch id : 0
[36,1] element, prob = 0.0229492    (811,3)-(851,48) batch id : 0
[37,1] element, prob = 0.0229492    (1154,27)-(1198,79) batch id : 0
[38,1] element, prob = 0.0229492    (1000,155)-(1046,226) batch id : 0
[39,1] element, prob = 0.0229492    (1086,145)-(1125,208) batch id : 0
[40,1] element, prob = 0.0229492    (958,20)-(1030,84) batch id : 0
[41,1] element, prob = 0.0229492    (934,65)-(986,155) batch id : 0
[42,1] element, prob = 0.0229492    (1045,99)-(1106,178) batch id : 0
[43,1] element, prob = 0.0229492    (983,-9)-(1141,105) batch id : 0
[44,1] element, prob = 0.0229492    (1065,-16)-(1192,124) batch id : 0
[45,1] element, prob = 0.0229492    (922,81)-(1087,304) batch id : 0
[46,1] element, prob = 0.0219727    (796,3)-(834,47) batch id : 0
[47,1] element, prob = 0.0219727    (911,4)-(953,55) batch id : 0
[48,1] element, prob = 0.0219727    (797,29)-(835,79) batch id : 0
[49,1] element, prob = 0.0219727    (821,34)-(855,81) batch id : 0
[50,1] element, prob = 0.0219727    (910,28)-(945,78) batch id : 0
[51,1] element, prob = 0.0219727    (755,77)-(790,125) batch id : 0
[52,1] element, prob = 0.0219727    (969,80)-(1008,128) batch id : 0
[53,1] element, prob = 0.0219727    (938,108)-(980,173) batch id : 0
[54,1] element, prob = 0.0219727    (1123,186)-(1161,252) batch id : 0
[55,1] element, prob = 0.0219727    (985,28)-(1056,105) batch id : 0
[56,1] element, prob = 0.0219727    (845,56)-(898,135) batch id : 0
[57,1] element, prob = 0.0219727    (1023,62)-(1080,131) batch id : 0
[58,1] element, prob = 0.0219727    (1041,40)-(1125,143) batch id : 0
[59,1] element, prob = 0.0219727    (1035,116)-(1110,242) batch id : 0
[60,1] element, prob = 0.0219727    (990,166)-(1054,269) batch id : 0
[61,1] element, prob = 0.0219727    (1078,169)-(1130,261) batch id : 0
[62,1] element, prob = 0.0219727    (971,13)-(1140,235) batch id : 0
[63,1] element, prob = 0.0219727    (980,125)-(1108,286) batch id : 0
[64,1] element, prob = 0.0209961    (940,5)-(987,55) batch id : 0
[65,1] element, prob = 0.0209961    (991,0)-(1043,41) batch id : 0
[66,1] element, prob = 0.0209961    (1126,0)-(1165,42) batch id : 0
[67,1] element, prob = 0.0209961    (998,41)-(1039,86) batch id : 0
[68,1] element, prob = 0.0209961    (1068,40)-(1103,86) batch id : 0
[69,1] element, prob = 0.0209961    (821,77)-(856,123) batch id : 0
[70,1] element, prob = 0.0209961    (913,80)-(949,128) batch id : 0
[71,1] element, prob = 0.0209961    (821,112)-(861,164) batch id : 0
[72,1] element, prob = 0.0209961    (878,113)-(919,175) batch id : 0
[73,1] element, prob = 0.0209961    (910,110)-(950,170) batch id : 0
[74,1] element, prob = 0.0209961    (1149,111)-(1189,163) batch id : 0
[75,1] element, prob = 0.0209961    (870,149)-(918,215) batch id : 0
[76,1] element, prob = 0.0209961    (1033,152)-(1070,209) batch id : 0
[77,1] element, prob = 0.0209961    (1128,141)-(1165,204) batch id : 0
[78,1] element, prob = 0.0209961    (1147,135)-(1188,199) batch id : 0
[79,1] element, prob = 0.0209961    (1064,188)-(1099,246) batch id : 0
[80,1] element, prob = 0.0209961    (1238,903)-(1279,963) batch id : 0
[81,1] element, prob = 0.0209961    (1146,19)-(1208,109) batch id : 0
[82,1] element, prob = 0.0209961    (1054,58)-(1110,126) batch id : 0
[83,1] element, prob = 0.0209961    (1111,99)-(1190,228) batch id : 0
[84,1] element, prob = 0.0209961    (1052,161)-(1105,265) batch id : 0
[85,1] element, prob = 0.0209961    (1060,145)-(1139,284) batch id : 0
[86,1] element, prob = 0.0209961    (-16,860)-(79,985) batch id : 0
[87,1] element, prob = 0.0209961    (1174,-17)-(1281,136) batch id : 0
[88,1] element, prob = 0.0209961    (1073,32)-(1198,190) batch id : 0
[89,1] element, prob = 0.0209961    (950,188)-(1072,366) batch id : 0
[90,1] element, prob = 0.0200195    (943,81)-(978,130) batch id : 0
[91,1] element, prob = 0.0200195    (968,104)-(1008,159) batch id : 0
[92,1] element, prob = 0.0200195    (930,148)-(973,211) batch id : 0
[93,1] element, prob = 0.0200195    (1092,191)-(1128,246) batch id : 0
[94,1] element, prob = 0.0200195    (1124,225)-(1161,291) batch id : 0
[95,1] element, prob = 0.0200195    (1007,-2)-(1083,55) batch id : 0
[96,1] element, prob = 0.0200195    (890,60)-(960,138) batch id : 0
[97,1] element, prob = 0.0200195    (962,65)-(1023,137) batch id : 0
[98,1] element, prob = 0.0200195    (1101,145)-(1183,284) batch id : 0
[99,1] element, prob = 0.0200195    (866,-10)-(1029,107) batch id : 0
[100,1] element, prob = 0.0200195    (1005,179)-(1111,360) batch id : 0
[101,1] element, prob = 0.0200195    (1019,342)-(1283,785) batch id : 0
[102,1] element, prob = 0.019043    (758,2)-(795,45) batch id : 0
[103,1] element, prob = 0.019043    (756,38)-(792,89) batch id : 0
[104,1] element, prob = 0.019043    (968,36)-(1011,82) batch id : 0
[105,1] element, prob = 0.019043    (729,77)-(763,127) batch id : 0
[106,1] element, prob = 0.019043    (1003,109)-(1044,169) batch id : 0
[107,1] element, prob = 0.019043    (1030,190)-(1066,242) batch id : 0
[108,1] element, prob = 0.019043    (887,83)-(971,209) batch id : 0
[109,1] element, prob = 0.019043    (995,99)-(1055,192) batch id : 0
[110,1] element, prob = 0.019043    (902,135)-(963,221) batch id : 0
[ INFO ] Image out_0.bmp created!
object_detection_sample_ssd: ../../libusb/io.c:2116: handle_events: Assertion `ctx->pollfds_cnt >= internal_nfds' failed.
Aborted

For NCS 1

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➜  samples ./cpp/build/armv7l/Release/object_detection_sample_ssd -m face-detection-adas-0001.xml -d MYRIAD -i me.jpg     
[ INFO ] InferenceEngine: 
	API version ............ 2.1
	Build .................. 2020.3.0-3467-15f2c61a-releases/2020/3
	Description ....... API
Parsing input parameters
[ INFO ] Files were added: 1
[ INFO ]     me.jpg
[ INFO ] Loading Inference Engine
[ INFO ] Device info: 
	MYRIAD
	myriadPlugin version ......... 2.1
	Build ........... 2020.3.0-3467-15f2c61a-releases/2020/3
[ INFO ] Loading network files:
	face-detection-adas-0001.xml
	face-detection-adas-0001.bin
[ INFO ] Preparing input blobs
[ INFO ] Batch size is 1
[ INFO ] Preparing output blobs
[ INFO ] Loading model to the device
[ INFO ] Create infer request
[ WARNING ] Image is resized from (924, 1280) to (672, 384)
[ INFO ] Batch size is 1
[ INFO ] Start inference
[ INFO ] Processing output blobs
object_detection_sample_ssd: ../../libusb/io.c:2116: handle_events: Assertion `ctx->pollfds_cnt >= internal_nfds' failed.
Aborted
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➜  samples ./cpp/build/armv7l/Release/object_detection_sample_ssd -m face-detection-adas-0001.xml -d MYRIAD -i parents.jpg
[ INFO ] InferenceEngine: 
	API version ............ 2.1
	Build .................. 2020.3.0-3467-15f2c61a-releases/2020/3
	Description ....... API
Parsing input parameters
[ INFO ] Files were added: 1
[ INFO ]     parents.jpg
[ INFO ] Loading Inference Engine
[ INFO ] Device info: 
	MYRIAD
	myriadPlugin version ......... 2.1
	Build ........... 2020.3.0-3467-15f2c61a-releases/2020/3
[ INFO ] Loading network files:
	face-detection-adas-0001.xml
	face-detection-adas-0001.bin
[ INFO ] Preparing input blobs
[ INFO ] Batch size is 1
[ INFO ] Preparing output blobs
[ INFO ] Loading model to the device
[ INFO ] Create infer request
[ WARNING ] Image is resized from (1280, 960) to (672, 384)
[ INFO ] Batch size is 1
[ INFO ] Start inference
[ INFO ] Processing output blobs
object_detection_sample_ssd: ../../libusb/io.c:2116: handle_events: Assertion `ctx->pollfds_cnt >= internal_nfds' failed.
Aborted

3.3.3 Python

For NCS 2

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➜  samples python ./python/object_detection_sample_ssd/object_detection_sample_ssd.py -m face-detection-adas-0001.xml -d MYRIAD -i me.jpg
[ INFO ] Loading Inference Engine
[ INFO ] Loading network files:
	face-detection-adas-0001.xml
	face-detection-adas-0001.bin
[ INFO ] Device info:
        MYRIAD
        MKLDNNPlugin version ......... 2.1
        Build ........... 2020.3.0-3467-15f2c61a-releases/2020/3
inputs number: 1
input shape: [1, 3, 384, 672]
input key: data
[ INFO ] File was added: 
[ INFO ]         me.jpg
[ WARNING ] Image me.jpg is resized from (384, 672) to (384, 672)
[ INFO ] Preparing input blobs
[ INFO ] Batch size is 1
[ INFO ] Preparing output blobs
[ INFO ] Loading model to the device
[ INFO ] Creating infer request and starting inference
[ INFO ] Processing output blobs
[0,1] element, prob = 0.870117    (421,202)-(745,608) batch id : 0 WILL BE PRINTED!
[1,1] element, prob = 0.0268555    (-55,556)-(234,1100) batch id : 0
[2,1] element, prob = 0.0219727    (20,925)-(239,1340) batch id : 0
[3,1] element, prob = 0.0200195    (-18,-144)-(179,581) batch id : 0
[4,1] element, prob = 0.0180664    (636,315)-(757,471) batch id : 0
[5,1] element, prob = 0.0170898    (-27,672)-(137,1217) batch id : 0
[6,1] element, prob = 0.0170898    (74,976)-(364,1322) batch id : 0
[7,1] element, prob = 0.0161133    (291,958)-(349,1122) batch id : 0
[8,1] element, prob = 0.0161133    (329,963)-(391,1126) batch id : 0
[9,1] element, prob = 0.0161133    (203,928)-(423,1346) batch id : 0
[10,1] element, prob = 0.015625    (694,1034)-(732,1140) batch id : 0
[11,1] element, prob = 0.015625    (832,1121)-(937,1295) batch id : 0
[12,1] element, prob = 0.015625    (219,39)-(388,627) batch id : 0
[13,1] element, prob = 0.015625    (76,785)-(357,1160) batch id : 0
[14,1] element, prob = 0.0146484    (355,616)-(475,778) batch id : 0
[15,1] element, prob = 0.0146484    (240,779)-(331,1048) batch id : 0
[16,1] element, prob = 0.0136719    (0,893)-(24,984) batch id : 0
[17,1] element, prob = 0.0136719    (658,306)-(752,405) batch id : 0
[18,1] element, prob = 0.0136719    (204,1198)-(267,1275) batch id : 0
[19,1] element, prob = 0.0136719    (322,611)-(430,794) batch id : 0
[20,1] element, prob = 0.0136719    (171,244)-(423,798) batch id : 0
[21,1] element, prob = 0.0136719    (252,798)-(539,1177) batch id : 0
[22,1] element, prob = 0.0136719    (133,875)-(301,1420) batch id : 0
[23,1] element, prob = 0.0136719    (443,202)-(802,1022) batch id : 0
[24,1] element, prob = 0.0126953    (718,466)-(757,540) batch id : 0
[25,1] element, prob = 0.0126953    (166,527)-(206,594) batch id : 0
[26,1] element, prob = 0.0126953    (694,511)-(731,590) batch id : 0
[27,1] element, prob = 0.0126953    (705,441)-(771,555) batch id : 0
[28,1] element, prob = 0.0126953    (272,962)-(322,1117) batch id : 0
[29,1] element, prob = 0.0126953    (268,486)-(376,688) batch id : 0
[30,1] element, prob = 0.0126953    (323,489)-(422,690) batch id : 0
[31,1] element, prob = 0.0126953    (277,607)-(384,796) batch id : 0
[32,1] element, prob = 0.0126953    (233,709)-(342,924) batch id : 0
[33,1] element, prob = 0.0126953    (256,672)-(406,957) batch id : 0
[34,1] element, prob = 0.0126953    (252,1043)-(431,1214) batch id : 0
[35,1] element, prob = 0.0117188    (695,466)-(733,539) batch id : 0
[36,1] element, prob = 0.0117188    (673,514)-(709,587) batch id : 0
[37,1] element, prob = 0.0117188    (370,1005)-(399,1078) batch id : 0
[38,1] element, prob = 0.0117188    (670,256)-(753,346) batch id : 0
[39,1] element, prob = 0.0117188    (151,500)-(221,610) batch id : 0
[40,1] element, prob = 0.0117188    (447,489)-(511,608) batch id : 0
[41,1] element, prob = 0.0117188    (126,625)-(200,719) batch id : 0
[42,1] element, prob = 0.0117188    (299,626)-(387,709) batch id : 0
[43,1] element, prob = 0.0117188    (164,673)-(251,747) batch id : 0
[44,1] element, prob = 0.0117188    (398,850)-(453,1008) batch id : 0
[45,1] element, prob = 0.0117188    (425,851)-(476,1008) batch id : 0
[46,1] element, prob = 0.0117188    (310,915)-(371,1077) batch id : 0
[47,1] element, prob = 0.0117188    (357,925)-(413,1073) batch id : 0
[48,1] element, prob = 0.0117188    (378,923)-(435,1074) batch id : 0
[49,1] element, prob = 0.0117188    (468,906)-(521,1070) batch id : 0
[50,1] element, prob = 0.0117188    (284,1016)-(357,1188) batch id : 0
[51,1] element, prob = 0.0117188    (203,-95)-(283,182) batch id : 0
[52,1] element, prob = 0.0117188    (269,378)-(374,595) batch id : 0
[53,1] element, prob = 0.0117188    (-22,575)-(71,831) batch id : 0
[54,1] element, prob = 0.0117188    (318,698)-(433,911) batch id : 0
[55,1] element, prob = 0.0117188    (221,716)-(395,1258) batch id : 0
[56,1] element, prob = 0.0117188    (859,771)-(987,1518) batch id : 0
[57,1] element, prob = 0.0107422    (145,466)-(185,527) batch id : 0
[58,1] element, prob = 0.0107422    (166,463)-(204,521) batch id : 0
[59,1] element, prob = 0.0107422    (185,477)-(224,537) batch id : 0
[60,1] element, prob = 0.0107422    (214,477)-(250,540) batch id : 0
[61,1] element, prob = 0.0107422    (458,478)-(488,536) batch id : 0
[62,1] element, prob = 0.0107422    (151,537)-(177,594) batch id : 0
[63,1] element, prob = 0.0107422    (460,522)-(488,590) batch id : 0
[64,1] element, prob = 0.0107422    (481,523)-(509,588) batch id : 0
[65,1] element, prob = 0.0107422    (716,512)-(755,592) batch id : 0
[66,1] element, prob = 0.0107422    (129,581)-(158,641) batch id : 0
[67,1] element, prob = 0.0107422    (150,579)-(179,635) batch id : 0
[68,1] element, prob = 0.0107422    (171,583)-(201,641) batch id : 0
[69,1] element, prob = 0.0107422    (457,564)-(491,658) batch id : 0
[70,1] element, prob = 0.0107422    (481,581)-(510,647) batch id : 0
[71,1] element, prob = 0.0107422    (652,565)-(689,650) batch id : 0
[72,1] element, prob = 0.0107422    (674,571)-(710,650) batch id : 0
[73,1] element, prob = 0.0107422    (694,571)-(734,649) batch id : 0
[74,1] element, prob = 0.0107422    (143,640)-(188,705) batch id : 0
[75,1] element, prob = 0.0107422    (191,743)-(224,805) batch id : 0
[76,1] element, prob = 0.0107422    (437,843)-(465,920) batch id : 0
[77,1] element, prob = 0.0107422    (-1,943)-(22,1037) batch id : 0
[78,1] element, prob = 0.0107422    (414,1007)-(443,1080) batch id : 0
[79,1] element, prob = 0.0107422    (896,1206)-(924,1279) batch id : 0
[80,1] element, prob = 0.0107422    (678,200)-(743,294) batch id : 0
[81,1] element, prob = 0.0107422    (70,374)-(190,512) batch id : 0
[82,1] element, prob = 0.0107422    (422,429)-(485,563) batch id : 0
[83,1] element, prob = 0.0107422    (106,477)-(213,629) batch id : 0
[84,1] element, prob = 0.0107422    (428,477)-(479,615) batch id : 0
[85,1] element, prob = 0.0107422    (648,498)-(727,605) batch id : 0
[86,1] element, prob = 0.0107422    (703,490)-(769,614) batch id : 0
[87,1] element, prob = 0.0107422    (16,531)-(150,712) batch id : 0
[88,1] element, prob = 0.0107422    (87,546)-(150,685) batch id : 0
[89,1] element, prob = 0.0107422    (127,552)-(197,653) batch id : 0
[90,1] element, prob = 0.0107422    (425,557)-(481,671) batch id : 0
[91,1] element, prob = 0.0107422    (437,509)-(516,700) batch id : 0
[92,1] element, prob = 0.0107422    (467,549)-(524,678) batch id : 0
[93,1] element, prob = 0.0107422    (633,551)-(704,664) batch id : 0
[94,1] element, prob = 0.0107422    (416,611)-(487,735) batch id : 0
[95,1] element, prob = 0.0107422    (466,602)-(530,734) batch id : 0
[96,1] element, prob = 0.0107422    (124,673)-(198,759) batch id : 0
[97,1] element, prob = 0.0107422    (156,705)-(260,849) batch id : 0
[98,1] element, prob = 0.0107422    (54,828)-(137,933) batch id : 0
[99,1] element, prob = 0.0107422    (451,812)-(498,960) batch id : 0
[100,1] element, prob = 0.0107422    (470,843)-(520,1004) batch id : 0
[101,1] element, prob = 0.0107422    (447,897)-(500,1066) batch id : 0
[102,1] element, prob = 0.0107422    (641,966)-(706,1122) batch id : 0
[103,1] element, prob = 0.0107422    (164,85)-(233,410) batch id : 0
[104,1] element, prob = 0.0107422    (188,497)-(307,688) batch id : 0
[105,1] element, prob = 0.0107422    (205,445)-(357,715) batch id : 0
[106,1] element, prob = 0.0107422    (230,602)-(341,791) batch id : 0
[107,1] element, prob = 0.0107422    (274,785)-(390,1021) batch id : 0
[108,1] element, prob = 0.0107422    (26,709)-(209,1239) batch id : 0
[109,1] element, prob = 0.0107422    (309,868)-(485,1410) batch id : 0
[110,1] element, prob = 0.0107422    (229,981)-(575,1306) batch id : 0
[111,1] element, prob = 0.0107422    (29,141)-(518,883) batch id : 0
[ INFO ] Image out.bmp created!
[ INFO ] Execution successful

[ INFO ] This sample is an API example, for any performance measurements please use the dedicated benchmark_app tool
corrupted double-linked list
Aborted
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➜  samples python ./python/object_detection_sample_ssd/object_detection_sample_ssd.py -m face-detection-adas-0001.xml -d MYRIAD -i parents.jpg
[ INFO ] Loading Inference Engine
[ INFO ] Loading network files:
	face-detection-adas-0001.xml
	face-detection-adas-0001.bin
[ INFO ] Device info:
        MYRIAD
        MKLDNNPlugin version ......... 2.1
        Build ........... 2020.3.0-3467-15f2c61a-releases/2020/3
inputs number: 1
input shape: [1, 3, 384, 672]
input key: data
[ INFO ] File was added: 
[ INFO ]         parents.jpg
[ WARNING ] Image parents.jpg is resized from (384, 672) to (384, 672)
[ INFO ] Preparing input blobs
[ INFO ] Batch size is 1
[ INFO ] Preparing output blobs
[ INFO ] Loading model to the device
[ INFO ] Creating infer request and starting inference
[ INFO ] Processing output blobs
[0,1] element, prob = 1.0    (826,366)-(1026,644) batch id : 0 WILL BE PRINTED!
[1,1] element, prob = 0.996582    (539,173)-(693,429) batch id : 0 WILL BE PRINTED!
[2,1] element, prob = 0.539062    (1094,47)-(1135,96) batch id : 0 WILL BE PRINTED!
[3,1] element, prob = 0.212402    (848,22)-(886,70) batch id : 0
[4,1] element, prob = 0.0527344    (7,783)-(151,957) batch id : 0
[5,1] element, prob = 0.0395508    (1033,78)-(1070,122) batch id : 0
[6,1] element, prob = 0.034668    (1123,75)-(1158,122) batch id : 0
[7,1] element, prob = 0.0322266    (1091,109)-(1130,171) batch id : 0
[8,1] element, prob = 0.0322266    (1086,14)-(1138,85) batch id : 0
[9,1] element, prob = 0.0302734    (1109,43)-(1150,93) batch id : 0
[10,1] element, prob = 0.0292969    (1151,83)-(1190,131) batch id : 0
[11,1] element, prob = 0.0273438    (1030,2)-(1073,43) batch id : 0
[12,1] element, prob = 0.0273438    (1091,6)-(1133,55) batch id : 0
[13,1] element, prob = 0.0273438    (1064,73)-(1098,121) batch id : 0
[14,1] element, prob = 0.0273438    (1058,113)-(1096,168) batch id : 0
[15,1] element, prob = 0.0273438    (1143,74)-(1211,165) batch id : 0
[16,1] element, prob = 0.0268555    (876,73)-(915,118) batch id : 0
[17,1] element, prob = 0.0268555    (1047,-3)-(1110,59) batch id : 0
[18,1] element, prob = 0.0268555    (849,13)-(1030,236) batch id : 0
[19,1] element, prob = 0.0258789    (941,22)-(981,74) batch id : 0
[20,1] element, prob = 0.0258789    (1030,35)-(1074,89) batch id : 0
[21,1] element, prob = 0.0258789    (845,112)-(886,167) batch id : 0
[22,1] element, prob = 0.0258789    (1036,111)-(1076,171) batch id : 0
[23,1] element, prob = 0.0258789    (1113,-1)-(1175,59) batch id : 0
[24,1] element, prob = 0.0258789    (1002,40)-(1093,151) batch id : 0
[25,1] element, prob = 0.0249023    (1059,0)-(1098,43) batch id : 0
[26,1] element, prob = 0.0249023    (876,29)-(918,79) batch id : 0
[27,1] element, prob = 0.0249023    (1002,69)-(1048,123) batch id : 0
[28,1] element, prob = 0.0249023    (1133,104)-(1170,163) batch id : 0
[29,1] element, prob = 0.0249023    (1056,145)-(1094,208) batch id : 0
[30,1] element, prob = 0.0249023    (861,2)-(918,63) batch id : 0
[31,1] element, prob = 0.0239258    (966,1)-(1018,45) batch id : 0
[32,1] element, prob = 0.0239258    (1098,63)-(1131,109) batch id : 0
[33,1] element, prob = 0.0239258    (1010,84)-(1092,204) batch id : 0
[34,1] element, prob = 0.0239258    (1022,139)-(1076,224) batch id : 0
[35,1] element, prob = 0.0239258    (943,34)-(1073,208) batch id : 0
[36,1] element, prob = 0.0229492    (811,3)-(851,48) batch id : 0
[37,1] element, prob = 0.0229492    (1154,27)-(1198,79) batch id : 0
[38,1] element, prob = 0.0229492    (1000,155)-(1046,226) batch id : 0
[39,1] element, prob = 0.0229492    (1086,145)-(1125,208) batch id : 0
[40,1] element, prob = 0.0229492    (958,20)-(1030,84) batch id : 0
[41,1] element, prob = 0.0229492    (934,65)-(986,155) batch id : 0
[42,1] element, prob = 0.0229492    (1045,99)-(1106,178) batch id : 0
[43,1] element, prob = 0.0229492    (983,-9)-(1141,105) batch id : 0
[44,1] element, prob = 0.0229492    (1065,-16)-(1192,124) batch id : 0
[45,1] element, prob = 0.0229492    (922,81)-(1087,304) batch id : 0
[46,1] element, prob = 0.0219727    (796,3)-(834,47) batch id : 0
[47,1] element, prob = 0.0219727    (911,4)-(953,55) batch id : 0
[48,1] element, prob = 0.0219727    (797,29)-(835,79) batch id : 0
[49,1] element, prob = 0.0219727    (821,34)-(855,81) batch id : 0
[50,1] element, prob = 0.0219727    (910,28)-(945,78) batch id : 0
[51,1] element, prob = 0.0219727    (755,77)-(790,125) batch id : 0
[52,1] element, prob = 0.0219727    (969,80)-(1008,128) batch id : 0
[53,1] element, prob = 0.0219727    (938,108)-(980,173) batch id : 0
[54,1] element, prob = 0.0219727    (1123,186)-(1161,252) batch id : 0
[55,1] element, prob = 0.0219727    (985,28)-(1056,105) batch id : 0
[56,1] element, prob = 0.0219727    (845,56)-(898,135) batch id : 0
[57,1] element, prob = 0.0219727    (1023,62)-(1080,131) batch id : 0
[58,1] element, prob = 0.0219727    (1041,40)-(1125,143) batch id : 0
[59,1] element, prob = 0.0219727    (1035,116)-(1110,242) batch id : 0
[60,1] element, prob = 0.0219727    (990,166)-(1054,269) batch id : 0
[61,1] element, prob = 0.0219727    (1078,169)-(1130,261) batch id : 0
[62,1] element, prob = 0.0219727    (971,13)-(1140,235) batch id : 0
[63,1] element, prob = 0.0219727    (980,125)-(1108,286) batch id : 0
[64,1] element, prob = 0.0209961    (940,5)-(987,55) batch id : 0
[65,1] element, prob = 0.0209961    (991,0)-(1043,41) batch id : 0
[66,1] element, prob = 0.0209961    (1126,0)-(1165,42) batch id : 0
[67,1] element, prob = 0.0209961    (998,41)-(1039,86) batch id : 0
[68,1] element, prob = 0.0209961    (1068,40)-(1103,86) batch id : 0
[69,1] element, prob = 0.0209961    (821,77)-(856,123) batch id : 0
[70,1] element, prob = 0.0209961    (913,80)-(949,128) batch id : 0
[71,1] element, prob = 0.0209961    (821,112)-(861,164) batch id : 0
[72,1] element, prob = 0.0209961    (878,113)-(919,175) batch id : 0
[73,1] element, prob = 0.0209961    (910,110)-(950,170) batch id : 0
[74,1] element, prob = 0.0209961    (1149,111)-(1189,163) batch id : 0
[75,1] element, prob = 0.0209961    (870,149)-(918,215) batch id : 0
[76,1] element, prob = 0.0209961    (1033,152)-(1070,209) batch id : 0
[77,1] element, prob = 0.0209961    (1128,141)-(1165,204) batch id : 0
[78,1] element, prob = 0.0209961    (1147,135)-(1188,199) batch id : 0
[79,1] element, prob = 0.0209961    (1064,188)-(1099,246) batch id : 0
[80,1] element, prob = 0.0209961    (1238,903)-(1279,963) batch id : 0
[81,1] element, prob = 0.0209961    (1146,19)-(1208,109) batch id : 0
[82,1] element, prob = 0.0209961    (1054,58)-(1110,126) batch id : 0
[83,1] element, prob = 0.0209961    (1111,99)-(1190,228) batch id : 0
[84,1] element, prob = 0.0209961    (1052,161)-(1105,265) batch id : 0
[85,1] element, prob = 0.0209961    (1060,145)-(1139,284) batch id : 0
[86,1] element, prob = 0.0209961    (-16,860)-(79,985) batch id : 0
[87,1] element, prob = 0.0209961    (1174,-17)-(1281,136) batch id : 0
[88,1] element, prob = 0.0209961    (1073,32)-(1198,190) batch id : 0
[89,1] element, prob = 0.0209961    (950,188)-(1072,366) batch id : 0
[90,1] element, prob = 0.0200195    (943,81)-(978,130) batch id : 0
[91,1] element, prob = 0.0200195    (968,104)-(1008,159) batch id : 0
[92,1] element, prob = 0.0200195    (930,148)-(973,211) batch id : 0
[93,1] element, prob = 0.0200195    (1092,191)-(1128,246) batch id : 0
[94,1] element, prob = 0.0200195    (1124,225)-(1161,291) batch id : 0
[95,1] element, prob = 0.0200195    (1007,-2)-(1083,55) batch id : 0
[96,1] element, prob = 0.0200195    (890,60)-(960,138) batch id : 0
[97,1] element, prob = 0.0200195    (962,65)-(1023,137) batch id : 0
[98,1] element, prob = 0.0200195    (1101,145)-(1183,284) batch id : 0
[99,1] element, prob = 0.0200195    (866,-10)-(1029,107) batch id : 0
[100,1] element, prob = 0.0200195    (1005,179)-(1111,360) batch id : 0
[101,1] element, prob = 0.0200195    (1019,342)-(1283,785) batch id : 0
[102,1] element, prob = 0.019043    (758,2)-(795,45) batch id : 0
[103,1] element, prob = 0.019043    (756,38)-(792,89) batch id : 0
[104,1] element, prob = 0.019043    (968,36)-(1011,82) batch id : 0
[105,1] element, prob = 0.019043    (729,77)-(763,127) batch id : 0
[106,1] element, prob = 0.019043    (1003,109)-(1044,169) batch id : 0
[107,1] element, prob = 0.019043    (1030,190)-(1066,242) batch id : 0
[108,1] element, prob = 0.019043    (887,83)-(971,209) batch id : 0
[109,1] element, prob = 0.019043    (995,99)-(1055,192) batch id : 0
[110,1] element, prob = 0.019043    (902,135)-(963,221) batch id : 0
[ INFO ] Image out.bmp created!
[ INFO ] Execution successful

[ INFO ] This sample is an API example, for any performance measurements please use the dedicated benchmark_app tool
free(): invalid pointer
Aborted

For NCS 1

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➜  samples python ./python/object_detection_sample_ssd/object_detection_sample_ssd.py -m face-detection-adas-0001.xml -d MYRIAD -i me.jpg
[ INFO ] Loading Inference Engine
[ INFO ] Loading network files:
	face-detection-adas-0001.xml
	face-detection-adas-0001.bin
[ INFO ] Device info:
        MYRIAD
        MKLDNNPlugin version ......... 2.1
        Build ........... 2020.3.0-3467-15f2c61a-releases/2020/3
inputs number: 1
input shape: [1, 3, 384, 672]
input key: data
[ INFO ] File was added: 
[ INFO ]         me.jpg
[ WARNING ] Image me.jpg is resized from (384, 672) to (384, 672)
[ INFO ] Preparing input blobs
[ INFO ] Batch size is 1
[ INFO ] Preparing output blobs
[ INFO ] Loading model to the device
[ INFO ] Creating infer request and starting inference
[ INFO ] Processing output blobs
[0,1] element, prob = 0.871094    (420,202)-(745,608) batch id : 0 WILL BE PRINTED!
[1,1] element, prob = 0.0268555    (-54,555)-(234,1100) batch id : 0
[2,1] element, prob = 0.0209961    (21,925)-(239,1340) batch id : 0
[3,1] element, prob = 0.019043    (-18,-144)-(178,583) batch id : 0
[4,1] element, prob = 0.0180664    (636,315)-(757,470) batch id : 0
[5,1] element, prob = 0.0170898    (75,977)-(363,1321) batch id : 0
[6,1] element, prob = 0.0161133    (291,958)-(349,1123) batch id : 0
[7,1] element, prob = 0.015625    (329,962)-(391,1126) batch id : 0
[8,1] element, prob = 0.015625    (832,1121)-(937,1293) batch id : 0
[9,1] element, prob = 0.015625    (-27,670)-(136,1220) batch id : 0
[10,1] element, prob = 0.015625    (76,785)-(357,1160) batch id : 0
[11,1] element, prob = 0.015625    (204,928)-(423,1346) batch id : 0
[12,1] element, prob = 0.0146484    (694,1035)-(732,1141) batch id : 0
[13,1] element, prob = 0.0146484    (219,40)-(388,626) batch id : 0
[14,1] element, prob = 0.0136719    (658,306)-(752,405) batch id : 0
[15,1] element, prob = 0.0136719    (204,1198)-(267,1275) batch id : 0
[16,1] element, prob = 0.0136719    (322,612)-(430,793) batch id : 0
[17,1] element, prob = 0.0136719    (240,779)-(331,1046) batch id : 0
[18,1] element, prob = 0.0136719    (171,245)-(424,798) batch id : 0
[19,1] element, prob = 0.0136719    (443,205)-(802,1021) batch id : 0
[20,1] element, prob = 0.0126953    (694,511)-(731,590) batch id : 0
[21,1] element, prob = 0.0126953    (0,893)-(24,984) batch id : 0
[22,1] element, prob = 0.0126953    (272,962)-(322,1117) batch id : 0
[23,1] element, prob = 0.0126953    (268,487)-(376,688) batch id : 0
[24,1] element, prob = 0.0126953    (323,490)-(422,690) batch id : 0
[25,1] element, prob = 0.0126953    (277,608)-(384,795) batch id : 0
[26,1] element, prob = 0.0126953    (233,710)-(342,923) batch id : 0
[27,1] element, prob = 0.0126953    (256,672)-(406,957) batch id : 0
[28,1] element, prob = 0.0126953    (252,799)-(540,1176) batch id : 0
[29,1] element, prob = 0.0126953    (133,876)-(301,1420) batch id : 0
[30,1] element, prob = 0.0117188    (718,466)-(757,540) batch id : 0
[31,1] element, prob = 0.0117188    (166,527)-(206,594) batch id : 0
[32,1] element, prob = 0.0117188    (673,514)-(709,587) batch id : 0
[33,1] element, prob = 0.0117188    (-1,943)-(22,1038) batch id : 0
[34,1] element, prob = 0.0117188    (670,256)-(753,346) batch id : 0
[35,1] element, prob = 0.0117188    (705,441)-(771,555) batch id : 0
[36,1] element, prob = 0.0117188    (126,625)-(200,718) batch id : 0
[37,1] element, prob = 0.0117188    (164,673)-(251,747) batch id : 0
[38,1] element, prob = 0.0117188    (399,850)-(453,1008) batch id : 0
[39,1] element, prob = 0.0117188    (424,851)-(476,1009) batch id : 0
[40,1] element, prob = 0.0117188    (357,925)-(413,1073) batch id : 0
[41,1] element, prob = 0.0117188    (399,919)-(454,1074) batch id : 0
[42,1] element, prob = 0.0117188    (468,906)-(521,1070) batch id : 0
[43,1] element, prob = 0.0117188    (284,1016)-(357,1186) batch id : 0
[44,1] element, prob = 0.0117188    (269,379)-(374,595) batch id : 0
[45,1] element, prob = 0.0117188    (349,563)-(485,831) batch id : 0
[46,1] element, prob = 0.0117188    (252,1042)-(431,1215) batch id : 0
[47,1] element, prob = 0.0117188    (858,771)-(987,1518) batch id : 0
[48,1] element, prob = 0.0107422    (165,463)-(204,521) batch id : 0
[49,1] element, prob = 0.0107422    (185,477)-(224,537) batch id : 0
[50,1] element, prob = 0.0107422    (214,477)-(250,540) batch id : 0
[51,1] element, prob = 0.0107422    (458,478)-(488,536) batch id : 0
[52,1] element, prob = 0.0107422    (695,466)-(733,539) batch id : 0
[53,1] element, prob = 0.0107422    (460,522)-(488,590) batch id : 0
[54,1] element, prob = 0.0107422    (481,523)-(509,588) batch id : 0
[55,1] element, prob = 0.0107422    (716,512)-(755,592) batch id : 0
[56,1] element, prob = 0.0107422    (150,579)-(179,635) batch id : 0
[57,1] element, prob = 0.0107422    (171,584)-(201,641) batch id : 0
[58,1] element, prob = 0.0107422    (481,582)-(510,647) batch id : 0
[59,1] element, prob = 0.0107422    (652,565)-(689,650) batch id : 0
[60,1] element, prob = 0.0107422    (674,571)-(710,650) batch id : 0
[61,1] element, prob = 0.0107422    (694,571)-(734,648) batch id : 0
[62,1] element, prob = 0.0107422    (142,640)-(188,705) batch id : 0
[63,1] element, prob = 0.0107422    (335,627)-(390,695) batch id : 0
[64,1] element, prob = 0.0107422    (191,744)-(225,803) batch id : 0
[65,1] element, prob = 0.0107422    (370,1005)-(399,1078) batch id : 0
[66,1] element, prob = 0.0107422    (414,1006)-(443,1079) batch id : 0
[67,1] element, prob = 0.0107422    (896,1207)-(924,1280) batch id : 0
[68,1] element, prob = 0.0107422    (86,386)-(160,513) batch id : 0
[69,1] element, prob = 0.0107422    (106,477)-(214,630) batch id : 0
[70,1] element, prob = 0.0107422    (151,501)-(221,610) batch id : 0
[71,1] element, prob = 0.0107422    (428,478)-(479,615) batch id : 0
[72,1] element, prob = 0.0107422    (450,478)-(506,611) batch id : 0
[73,1] element, prob = 0.0107422    (647,498)-(728,605) batch id : 0
[74,1] element, prob = 0.0107422    (148,556)-(220,658) batch id : 0
[75,1] element, prob = 0.0107422    (425,557)-(481,671) batch id : 0
[76,1] element, prob = 0.0107422    (437,510)-(516,700) batch id : 0
[77,1] element, prob = 0.0107422    (463,554)-(527,674) batch id : 0
[78,1] element, prob = 0.0107422    (633,551)-(704,664) batch id : 0
[79,1] element, prob = 0.0107422    (280,628)-(367,711) batch id : 0
[80,1] element, prob = 0.0107422    (416,612)-(488,735) batch id : 0
[81,1] element, prob = 0.0107422    (155,706)-(260,850) batch id : 0
[82,1] element, prob = 0.0107422    (55,828)-(137,932) batch id : 0
[83,1] element, prob = 0.0107422    (451,813)-(498,960) batch id : 0
[84,1] element, prob = 0.0107422    (310,914)-(371,1076) batch id : 0
[85,1] element, prob = 0.0107422    (447,896)-(500,1065) batch id : 0
[86,1] element, prob = 0.0107422    (641,966)-(706,1121) batch id : 0
[87,1] element, prob = 0.0107422    (203,-95)-(283,182) batch id : 0
[88,1] element, prob = 0.0107422    (164,86)-(233,410) batch id : 0
[89,1] element, prob = 0.0107422    (188,498)-(307,688) batch id : 0
[90,1] element, prob = 0.0107422    (205,445)-(357,715) batch id : 0
[91,1] element, prob = 0.0107422    (-22,576)-(71,832) batch id : 0
[92,1] element, prob = 0.0107422    (230,603)-(342,791) batch id : 0
[93,1] element, prob = 0.0107422    (318,698)-(433,909) batch id : 0
[94,1] element, prob = 0.0107422    (273,784)-(390,1021) batch id : 0
[95,1] element, prob = 0.0107422    (175,740)-(444,1237) batch id : 0
[ INFO ] Image out.bmp created!
[ INFO ] Execution successful

[ INFO ] This sample is an API example, for any performance measurements please use the dedicated benchmark_app tool
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➜  samples python ./python/object_detection_sample_ssd/object_detection_sample_ssd.py -m face-detection-adas-0001.xml -d MYRIAD -i parents.jpg
[ INFO ] Loading Inference Engine
[ INFO ] Loading network files:
	face-detection-adas-0001.xml
	face-detection-adas-0001.bin
[ INFO ] Device info:
        MYRIAD
        MKLDNNPlugin version ......... 2.1
        Build ........... 2020.3.0-3467-15f2c61a-releases/2020/3
inputs number: 1
input shape: [1, 3, 384, 672]
input key: data
[ INFO ] File was added: 
[ INFO ]         parents.jpg
[ WARNING ] Image parents.jpg is resized from (384, 672) to (384, 672)
[ INFO ] Preparing input blobs
[ INFO ] Batch size is 1
[ INFO ] Preparing output blobs
[ INFO ] Loading model to the device
[ INFO ] Creating infer request and starting inference
[ INFO ] Processing output blobs
[0,1] element, prob = 1.0    (826,366)-(1026,644) batch id : 0 WILL BE PRINTED!
[1,1] element, prob = 0.996582    (539,173)-(693,429) batch id : 0 WILL BE PRINTED!
[2,1] element, prob = 0.552734    (1094,47)-(1135,95) batch id : 0 WILL BE PRINTED!
[3,1] element, prob = 0.22168    (848,22)-(886,70) batch id : 0
[4,1] element, prob = 0.0537109    (8,784)-(151,956) batch id : 0
[5,1] element, prob = 0.0395508    (1033,78)-(1070,122) batch id : 0
[6,1] element, prob = 0.034668    (1123,75)-(1158,122) batch id : 0
[7,1] element, prob = 0.0322266    (1086,14)-(1138,85) batch id : 0
[8,1] element, prob = 0.03125    (1091,110)-(1130,171) batch id : 0
[9,1] element, prob = 0.0302734    (1108,43)-(1150,93) batch id : 0
[10,1] element, prob = 0.0292969    (1151,83)-(1190,131) batch id : 0
[11,1] element, prob = 0.0273438    (1091,6)-(1133,55) batch id : 0
[12,1] element, prob = 0.0273438    (1064,73)-(1098,121) batch id : 0
[13,1] element, prob = 0.0268555    (1030,2)-(1073,42) batch id : 0
[14,1] element, prob = 0.0268555    (1058,113)-(1096,168) batch id : 0
[15,1] element, prob = 0.0268555    (1047,-3)-(1110,59) batch id : 0
[16,1] element, prob = 0.0268555    (1142,75)-(1211,165) batch id : 0
[17,1] element, prob = 0.0268555    (849,14)-(1030,236) batch id : 0
[18,1] element, prob = 0.0258789    (941,22)-(981,74) batch id : 0
[19,1] element, prob = 0.0258789    (1030,36)-(1074,89) batch id : 0
[20,1] element, prob = 0.0258789    (876,73)-(915,118) batch id : 0
[21,1] element, prob = 0.0258789    (845,112)-(886,167) batch id : 0
[22,1] element, prob = 0.0258789    (1036,110)-(1076,171) batch id : 0
[23,1] element, prob = 0.0258789    (1113,-1)-(1175,59) batch id : 0
[24,1] element, prob = 0.0258789    (1003,41)-(1094,151) batch id : 0
[25,1] element, prob = 0.0249023    (1059,0)-(1098,43) batch id : 0
[26,1] element, prob = 0.0249023    (1002,69)-(1048,123) batch id : 0
[27,1] element, prob = 0.0249023    (1133,105)-(1170,164) batch id : 0
[28,1] element, prob = 0.0239258    (966,1)-(1018,45) batch id : 0
[29,1] element, prob = 0.0239258    (876,29)-(918,79) batch id : 0
[30,1] element, prob = 0.0239258    (1056,145)-(1094,208) batch id : 0
[31,1] element, prob = 0.0239258    (861,2)-(918,63) batch id : 0
[32,1] element, prob = 0.0239258    (1010,84)-(1092,204) batch id : 0
[33,1] element, prob = 0.0239258    (1022,139)-(1076,224) batch id : 0
[34,1] element, prob = 0.0239258    (942,35)-(1072,208) batch id : 0
[35,1] element, prob = 0.0229492    (811,3)-(851,48) batch id : 0
[36,1] element, prob = 0.0229492    (1098,63)-(1131,109) batch id : 0
[37,1] element, prob = 0.0229492    (983,-9)-(1141,105) batch id : 0
[38,1] element, prob = 0.0229492    (921,81)-(1087,304) batch id : 0
[39,1] element, prob = 0.0219727    (796,3)-(835,47) batch id : 0
[40,1] element, prob = 0.0219727    (911,4)-(953,55) batch id : 0
[41,1] element, prob = 0.0219727    (797,29)-(835,79) batch id : 0
[42,1] element, prob = 0.0219727    (1154,27)-(1198,79) batch id : 0
[43,1] element, prob = 0.0219727    (969,80)-(1008,128) batch id : 0
[44,1] element, prob = 0.0219727    (1000,155)-(1046,226) batch id : 0
[45,1] element, prob = 0.0219727    (1086,145)-(1125,208) batch id : 0
[46,1] element, prob = 0.0219727    (1123,186)-(1161,252) batch id : 0
[47,1] element, prob = 0.0219727    (958,20)-(1030,84) batch id : 0
[48,1] element, prob = 0.0219727    (985,28)-(1056,105) batch id : 0
[49,1] element, prob = 0.0219727    (845,56)-(898,134) batch id : 0
[50,1] element, prob = 0.0219727    (934,65)-(986,155) batch id : 0
[51,1] element, prob = 0.0219727    (1046,99)-(1107,178) batch id : 0
[52,1] element, prob = 0.0219727    (1035,116)-(1110,241) batch id : 0
[53,1] element, prob = 0.0219727    (990,166)-(1054,269) batch id : 0
[54,1] element, prob = 0.0219727    (1065,-16)-(1192,124) batch id : 0
[55,1] element, prob = 0.0219727    (971,13)-(1140,235) batch id : 0
[56,1] element, prob = 0.0219727    (980,125)-(1108,286) batch id : 0
[57,1] element, prob = 0.0209961    (991,0)-(1043,41) batch id : 0
[58,1] element, prob = 0.0209961    (1126,0)-(1165,42) batch id : 0
[59,1] element, prob = 0.0209961    (821,35)-(856,79) batch id : 0
[60,1] element, prob = 0.0209961    (910,28)-(945,78) batch id : 0
[61,1] element, prob = 0.0209961    (997,41)-(1038,86) batch id : 0
[62,1] element, prob = 0.0209961    (1068,40)-(1103,86) batch id : 0
[63,1] element, prob = 0.0209961    (754,77)-(789,125) batch id : 0
[64,1] element, prob = 0.0209961    (819,77)-(855,123) batch id : 0
[65,1] element, prob = 0.0209961    (913,79)-(949,128) batch id : 0
[66,1] element, prob = 0.0209961    (821,112)-(861,164) batch id : 0
[67,1] element, prob = 0.0209961    (938,108)-(980,173) batch id : 0
[68,1] element, prob = 0.0209961    (1149,112)-(1189,162) batch id : 0
[69,1] element, prob = 0.0209961    (870,149)-(918,215) batch id : 0
[70,1] element, prob = 0.0209961    (1033,152)-(1070,209) batch id : 0
[71,1] element, prob = 0.0209961    (1064,188)-(1099,246) batch id : 0
[72,1] element, prob = 0.0209961    (1023,63)-(1081,131) batch id : 0
[73,1] element, prob = 0.0209961    (1054,58)-(1110,126) batch id : 0
[74,1] element, prob = 0.0209961    (1041,40)-(1125,143) batch id : 0
[75,1] element, prob = 0.0209961    (1111,99)-(1190,228) batch id : 0
[76,1] element, prob = 0.0209961    (1078,169)-(1130,261) batch id : 0
[77,1] element, prob = 0.0209961    (950,189)-(1072,367) batch id : 0
[78,1] element, prob = 0.0200195    (940,6)-(987,56) batch id : 0
[79,1] element, prob = 0.0200195    (943,81)-(978,130) batch id : 0
[80,1] element, prob = 0.0200195    (878,114)-(919,175) batch id : 0
[81,1] element, prob = 0.0200195    (910,110)-(950,170) batch id : 0
[82,1] element, prob = 0.0200195    (968,104)-(1008,159) batch id : 0
[83,1] element, prob = 0.0200195    (1128,141)-(1165,204) batch id : 0
[84,1] element, prob = 0.0200195    (1147,135)-(1188,199) batch id : 0
[85,1] element, prob = 0.0200195    (1238,903)-(1279,963) batch id : 0
[86,1] element, prob = 0.0200195    (1007,-2)-(1083,55) batch id : 0
[87,1] element, prob = 0.0200195    (1052,161)-(1105,266) batch id : 0
[88,1] element, prob = 0.0200195    (1061,145)-(1140,284) batch id : 0
[89,1] element, prob = 0.0200195    (-16,860)-(79,985) batch id : 0
[90,1] element, prob = 0.0200195    (866,-10)-(1029,107) batch id : 0
[91,1] element, prob = 0.0200195    (1173,-17)-(1280,136) batch id : 0
[92,1] element, prob = 0.0200195    (1072,32)-(1197,189) batch id : 0
[93,1] element, prob = 0.0200195    (1005,179)-(1111,359) batch id : 0
[94,1] element, prob = 0.019043    (758,2)-(795,45) batch id : 0
[95,1] element, prob = 0.019043    (968,35)-(1011,82) batch id : 0
[96,1] element, prob = 0.019043    (1003,109)-(1044,169) batch id : 0
[97,1] element, prob = 0.019043    (930,148)-(973,211) batch id : 0
[98,1] element, prob = 0.019043    (1092,191)-(1128,246) batch id : 0
[99,1] element, prob = 0.019043    (1123,225)-(1161,291) batch id : 0
[100,1] element, prob = 0.019043    (1146,19)-(1208,109) batch id : 0
[101,1] element, prob = 0.019043    (897,59)-(958,143) batch id : 0
[102,1] element, prob = 0.019043    (962,65)-(1023,137) batch id : 0
[103,1] element, prob = 0.019043    (886,83)-(971,209) batch id : 0
[104,1] element, prob = 0.019043    (995,99)-(1055,192) batch id : 0
[105,1] element, prob = 0.019043    (902,135)-(963,221) batch id : 0
[106,1] element, prob = 0.019043    (1101,145)-(1183,284) batch id : 0
[107,1] element, prob = 0.019043    (821,-17)-(946,129) batch id : 0
[108,1] element, prob = 0.019043    (940,-16)-(1076,120) batch id : 0
[109,1] element, prob = 0.019043    (819,34)-(945,205) batch id : 0
[110,1] element, prob = 0.019043    (1019,342)-(1283,785) batch id : 0
[111,1] element, prob = 0.0180664    (1147,7)-(1193,54) batch id : 0
[112,1] element, prob = 0.0180664    (756,38)-(792,89) batch id : 0
[ INFO ] Image out.bmp created!
[ INFO ] Execution successful

[ INFO ] This sample is an API example, for any performance measurements please use the dedicated benchmark_app tool
corrupted double-linked list
Aborted

3.3.4 Results

Parents At First Starbucks Parents Face Detection By Object Detection SSD NCS 1 Parents Face Detection By Object Detection SSD NCS 2
Parents At First Starbucks Parents At First Starbucks - Face Detection By Object Detection SSD NCS 1 Parents At First Starbucks - Face Detection By Object Detection SSD NCS 2
Me Me Face Detection By Object Detection SSD NCS 1 Me Face Detection By OpenCV DNN NCS 2
Me Me - Face Detection By Object Detection SSD NCS 1 Me - Face Detection By Object Detection SSD NCS 2

3.4 Model Optimization

In this section, we are going to test out another OpenVINO example: Image Classification C++ Sample Async. After reading this blog, it wouldn’t be hard for us to notice the MOST important thing we’re missing here is the model file alexnet_fp32.xml. Let’s just keep it in mind for now.

And, let’s review a bit about our previous example: Object Detection – we downloaded face-detection-adas-0001 model from online and use it directly. So, questions:

  • Are we able to download alexnet_fp32.xml from online this time again?
  • Where can we download a whole bunch of open source models?

3.4.1 Open Model Zoo

It wouldn’t be hard for us to google out OpenVINO™ Toolkit - Open Model Zoo repository, under which model face-detection-adas-0001 is just sitting there. However, face-detection-adas-0001.xml and face-detection-adas-0001.bin are missing.

Let’s checkout open_model_zoo and put it under folder /opt/intel.

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➜  intel pwd
/opt/intel
➜  intel ll
total 12K
drwxr-xr-x 8 pi   pi   4.0K Apr  7 06:37 dldt
drwxr-xr-x 9 pi   pi   4.0K Apr  7 14:04 l_openvino_toolkit_runtime_raspbian_p_2020.1.023
drwxr-xr-x 7 pi   pi   4.0K Apr  7 14:04 open_model_zoo
lrwxrwxrwx 1 root root   48 Apr  7 06:29 openvino -> l_openvino_toolkit_runtime_raspbian_p_2020.1.023

Then, let's enter folder face-detection-adas-0001 under open_model_zoo and take a look:

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➜  face-detection-adas-0001 git:(master) pwd
/opt/intel/open_model_zoo/models/intel/face-detection-adas-0001
➜  face-detection-adas-0001 git:(master) ls
description  face-detection-adas-0001.prototxt  model.yml

It seems that each folder under intel and public contains the detailed info of each model. For instance, file intel/face-detection-adas-0001/model.yml contains all the info about model face-detection-adas-0001. However, what we really need are a .xml file and a .bin file. In the following, we are going to generate such 2 files, which are optimized specifically for movidius by following Intel OpenVINO toolkit issue 798441.

3.4.2 Download Caffe Model Files

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➜  downloader git:(master) pwd
/opt/intel/open_model_zoo/tools/downloader
➜  downloader git:(master) ls
caffe2_to_onnx.py  converter.py   info_dumper.py      README.md               requirements.in          tests
common.py          downloader.py  pytorch_to_onnx.py  requirements-caffe2.in  requirements-pytorch.in
➜  downloader git:(master) python downloader.py --name alexnet
################|| Downloading models ||################

========== Downloading /opt/intel/open_model_zoo/tools/downloader/public/alexnet/alexnet.prototxt
... 100%, 3 KB, 10485 KB/s, 0 seconds passed

========== Downloading /opt/intel/open_model_zoo/tools/downloader/public/alexnet/alexnet.caffemodel
... 100%, 238146 KB, 5428 KB/s, 43 seconds passed

################|| Post-processing ||################

========== Replacing text in /opt/intel/open_model_zoo/tools/downloader/public/alexnet/alexnet.prototxt
➜  downloader git:(master) ✗ ll public/alexnet/
total 233M
-rw-r--r-- 1 pi pi 233M Apr  9 14:24 alexnet.caffemodel
-rw-r--r-- 1 pi pi 3.6K Apr  9 14:24 alexnet.prototxt
-rw-r--r-- 1 pi pi 3.6K Apr  9 14:23 alexnet.prototxt.orig

Clearly, three files including a large model file alexnet.caffemodel has been downloaded.

3.4.3 Model Optimization

Now, are are going to optimize the downloaded caffe model and make it feedable to OpenVINO.

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➜  model-optimizer git:(2020) ✗ pwd
/opt/intel/dldt/model-optimizer
➜  model-optimizer git:(2020) ✗ python mo_caffe.py --generate_deprecated_IR_V7 --input_model /opt/intel/open_model_zoo/tools/downloader/public/alexnet/alexnet.caffemodel
[ WARNING ]  Use of deprecated cli option --generate_deprecated_IR_V7 detected. Option use in the following releases will be fatal. 
Model Optimizer arguments:
Common parameters:
	- Path to the Input Model: 	/opt/intel/open_model_zoo/tools/downloader/public/alexnet/alexnet.caffemodel
	- Path for generated IR: 	/opt/intel/dldt/model-optimizer/.
	- IR output name: 	alexnet
	- Log level: 	ERROR
	- Batch: 	Not specified, inherited from the model
	- Input layers: 	Not specified, inherited from the model
	- Output layers: 	Not specified, inherited from the model
	- Input shapes: 	Not specified, inherited from the model
	- Mean values: 	Not specified
	- Scale values: 	Not specified
	- Scale factor: 	Not specified
	- Precision of IR: 	FP32
	- Enable fusing: 	True
	- Enable grouped convolutions fusing: 	True
	- Move mean values to preprocess section: 	False
	- Reverse input channels: 	False
Caffe specific parameters:
	- Path to Python Caffe* parser generated from caffe.proto: 	mo/front/caffe/proto
	- Enable resnet optimization: 	True
	- Path to the Input prototxt: 	/opt/intel/open_model_zoo/tools/downloader/public/alexnet/alexnet.prototxt
	- Path to CustomLayersMapping.xml: 	Default
	- Path to a mean file: 	Not specified
	- Offsets for a mean file: 	Not specified
Model Optimizer version: 	unknown version
Please expect that Model Optimizer conversion might be slow. You are currently using Python protobuf library implementation. 
However you can use the C++ protobuf implementation that is supplied with the OpenVINO toolkitor build protobuf library from sources. 
Navigate to "install_prerequisites" folder and run: python -m easy_install protobuf-3.5.1-py($your_python_version)-win-amd64.egg 
set PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=cpp


 For more information please refer to Model Optimizer FAQ (https://docs.openvinotoolkit.org/latest/_docs_MO_DG_prepare_model_Model_Optimizer_FAQ.html), question #80. 

[ SUCCESS ] Generated IR version 7 model.
[ SUCCESS ] XML file: /opt/intel/dldt/model-optimizer/./alexnet.xml
[ SUCCESS ] BIN file: /opt/intel/dldt/model-optimizer/./alexnet.bin
[ SUCCESS ] Total execution time: 615.36 seconds. 
[ SUCCESS ] Memory consumed: 1982 MB.

And, let's take a look at what's generated under folder /opt/intel/dldt/model-optimizer.

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➜  model-optimizer git:(2020) ✗ pwd
/opt/intel/dldt/model-optimizer
➜  model-optimizer git:(2020) ✗ ls
alexnet.bin      extensions             mo_caffe.py  mo_onnx.py  README.md               requirements_kaldi.txt  requirements_tf.txt
alexnet.mapping  install_prerequisites  mo_kaldi.py  mo.py       requirements_caffe.txt  requirements_mxnet.txt  requirements.txt
alexnet.xml      mo                     mo_mxnet.py  mo_tf.py    requirements_dev.txt    requirements_onnx.txt   tf_call_ie_layer
➜  model-optimizer git:(2020) ✗ ll alexnet*
-rw-r--r-- 1 pi pi 233M Apr  9 15:26 alexnet.bin
-rw-r--r-- 1 pi pi 2.6K Apr  9 15:26 alexnet.mapping
-rw-r--r-- 1 pi pi  25K Apr  9 15:26 alexnet.xml

Note: You may meet the following ERRORs during model optimization.

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[ ERROR ]  
Detected not satisfied dependencies:
	networkx: installed: 2.4, required: 2.4
	protobuf: installed: 3.11.3, required: 3.6.1

Clearly, for networkx, the ERROR message is a kind of ridiculous. Anyway, if you meet the above 2 errors, please DOWNGRADE your packages as follows:

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➜  ~  pip install protobuf==3.6.1 --user
Looking in indexes: https://pypi.org/simple, https://www.piwheels.org/simple
Collecting protobuf==3.6.1
  Downloading https://files.pythonhosted.org/packages/77/78/a7f1ce761e2c738e209857175cd4f90a8562d1bde32868a8cd5290d58926/protobuf-3.6.1-py2.py3-none-any.whl (390kB)
     |████████████████████████████████| 399kB 1.4MB/s 
Requirement already satisfied: six>=1.9 in /home/pi/.local/lib/python3.7/site-packages (from protobuf==3.6.1) (1.13.0)
Requirement already satisfied: setuptools in /home/pi/.local/lib/python3.7/site-packages (from protobuf==3.6.1) (44.0.0)
Installing collected packages: protobuf
  Found existing installation: protobuf 3.11.3
    Uninstalling protobuf-3.11.3:
      Successfully uninstalled protobuf-3.11.3
Successfully installed protobuf-3.6.1
➜  ~  pip install networkx==2.3 --user
Looking in indexes: https://pypi.org/simple, https://www.piwheels.org/simple
Collecting networkx==2.3
  Downloading https://files.pythonhosted.org/packages/85/08/f20aef11d4c343b557e5de6b9548761811eb16e438cee3d32b1c66c8566b/networkx-2.3.zip (1.7MB)
     |████████████████████████████████| 1.8MB 2.3MB/s 
Requirement already satisfied: decorator>=4.3.0 in /home/pi/.local/lib/python3.7/site-packages (from networkx==2.3) (4.4.1)
Building wheels for collected packages: networkx
  Building wheel for networkx (setup.py) ... done
  Created wheel for networkx: filename=networkx-2.3-py2.py3-none-any.whl size=1556408 sha256=9964dc8e3b41e97f0228afb2c1c6ca40a5dfc07dc1a2574b92717114f3d5e533
  Stored in directory: /home/pi/.cache/pip/wheels/de/63/64/3699be2a9d0ccdb37c7f16329acf3863fd76eda58c39c737af
Successfully built networkx
Installing collected packages: networkx
  Found existing installation: networkx 2.4
    Uninstalling networkx-2.4:
      Successfully uninstalled networkx-2.4
Successfully installed networkx-2.3

3.5 Image Classification

3.5.1 C

For NCS 2

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➜  samples ./c/build/armv7l/Release/hello_classification_c /opt/intel/dldt/model-optimizer/alexnet.xml me.jpg HETERO:MYRIAD

Top 10 results:

Image me.jpg

classid probability
------- -----------
838       0.074951
978       0.072632
977       0.072083
975       0.060730
903       0.052948
638       0.040588
976       0.040161
433       0.037842
112       0.032471
639       0.029724
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➜  samples ./c/build/armv7l/Release/hello_classification_c /opt/intel/dldt/model-optimizer/alexnet.xml parents.jpg HETERO:MYRIAD

Top 10 results:

Image parents.jpg

classid probability
------- -----------
707       0.072144
577       0.024185
704       0.022980
955       0.020844
813       0.020432
910       0.016937
515       0.016235
605       0.013985
918       0.013351
523       0.013031

For NCS 1

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➜  samples ./c/build/armv7l/Release/hello_classification_c /opt/intel/dldt/model-optimizer/alexnet.xml me.jpg HETERO:MYRIAD

Top 10 results:

Image me.jpg

classid probability
------- -----------
978       0.082336
977       0.081665
838       0.081055
975       0.061188
903       0.054840
638       0.046387
433       0.040131
976       0.034607
112       0.032349
639       0.031494
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➜  samples ./c/build/armv7l/Release/hello_classification_c /opt/intel/dldt/model-optimizer/alexnet.xml parents.jpg HETERO:MYRIAD

Top 10 results:

Image parents.jpg

classid probability
------- -----------
707       0.059418
577       0.026993
813       0.021347
704       0.020218
910       0.019669
515       0.018204
955       0.018051
523       0.013527
808       0.012856
918       0.012802

3.5.2 C++

For NCS 2

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➜  samples ./cpp/build/armv7l/Release/hello_classification /opt/intel/dldt/model-optimizer/alexnet.xml me.jpg HETERO:MYRIAD

Top 10 results:

Image me.jpg

classid probability
------- -----------
838     0.0749512  
978     0.0726318  
977     0.0720825  
975     0.0607300  
903     0.0529480  
638     0.0405884  
976     0.0401611  
433     0.0378418  
112     0.0324707  
639     0.0297241  

This sample is an API example, for any performance measurements please use the dedicated benchmark_app tool
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➜  samples ./cpp/build/armv7l/Release/hello_classification /opt/intel/dldt/model-optimizer/alexnet.xml parents.jpg HETERO:MYRIAD

Top 10 results:

Image parents.jpg

classid probability
------- -----------
707     0.0721436  
577     0.0241852  
704     0.0229797  
955     0.0208435  
813     0.0204315  
910     0.0169373  
515     0.0162354  
605     0.0139847  
918     0.0133514  
523     0.0130310  

This sample is an API example, for any performance measurements please use the dedicated benchmark_app tool

For NCS 1

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➜  samples ./cpp/build/armv7l/Release/hello_classification /opt/intel/dldt/model-optimizer/alexnet.xml me.jpg HETERO:MYRIAD               

Top 10 results:

Image me.jpg

classid probability
------- -----------
978     0.0823364  
977     0.0816650  
838     0.0810547  
975     0.0611877  
903     0.0548401  
638     0.0463867  
433     0.0401306  
976     0.0346069  
112     0.0323486  
639     0.0314941  

This sample is an API example, for any performance measurements please use the dedicated benchmark_app tool
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➜  samples ./cpp/build/armv7l/Release/hello_classification /opt/intel/dldt/model-optimizer/alexnet.xml parents.jpg HETERO:MYRIAD

Top 10 results:

Image parents.jpg

classid probability
------- -----------
707     0.0594177  
577     0.0269928  
813     0.0213470  
704     0.0202179  
910     0.0196686  
515     0.0182037  
955     0.0180511  
523     0.0135269  
808     0.0128555  
918     0.0128021  

This sample is an API example, for any performance measurements please use the dedicated benchmark_app tool

3.5.3 Python

For NCS 2

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➜  samples python ./python/classification_sample/classification_sample.py -m /opt/intel/dldt/model-optimizer/alexnet.xml -i me.jpg -d HETERO:MYRIAD -nt 10
[ INFO ] Creating Inference Engine
[ INFO ] Loading network files:
	/opt/intel/dldt/model-optimizer/alexnet.xml
	/opt/intel/dldt/model-optimizer/alexnet.bin
[ INFO ] Preparing input blobs
[ WARNING ] Image me.jpg is resized from (1280, 924) to (227, 227)
[ INFO ] Batch size is 1
[ INFO ] Loading model to the plugin
[ INFO ] Starting inference in synchronous mode
[ INFO ] Processing output blob
[ INFO ] Top 10 results: 
Image me.jpg

classid probability
------- -----------
  838    0.0751343
  978    0.0728149
  977    0.0722656
  975    0.0606079
  903    0.0533142
  638    0.0413513
  976    0.0391541
  433    0.0375061
  112    0.0320740
  639    0.0298767


[ INFO ] This sample is an API example, for any performance measurements please use the dedicated benchmark_app tool
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➜  samples python ./python/classification_sample/classification_sample.py -m /opt/intel/dldt/model-optimizer/alexnet.xml -i parents.jpg -d HETERO:MYRIAD -nt 10
[ INFO ] Creating Inference Engine
[ INFO ] Loading network files:
	/opt/intel/dldt/model-optimizer/alexnet.xml
	/opt/intel/dldt/model-optimizer/alexnet.bin
[ INFO ] Preparing input blobs
[ WARNING ] Image parents.jpg is resized from (960, 1280) to (227, 227)
[ INFO ] Batch size is 1
[ INFO ] Loading model to the plugin
[ INFO ] Starting inference in synchronous mode
[ INFO ] Processing output blob
[ INFO ] Top 10 results: 
Image parents.jpg

classid probability
------- -----------
  707    0.0712891
  577    0.0237122
  704    0.0227814
  955    0.0209045
  813    0.0205231
  910    0.0169983
  515    0.0162354
  605    0.0140991
  918    0.0133438
  523    0.0129318


[ INFO ] This sample is an API example, for any performance measurements please use the dedicated benchmark_app tool

For NCS 1

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➜  samples python ./python/classification_sample/classification_sample.py -m /opt/intel/dldt/model-optimizer/alexnet.xml -i me.jpg -d HETERO:MYRIAD -nt 10
[ INFO ] Creating Inference Engine
[ INFO ] Loading network files:
	/opt/intel/dldt/model-optimizer/alexnet.xml
	/opt/intel/dldt/model-optimizer/alexnet.bin
[ INFO ] Preparing input blobs
[ WARNING ] Image me.jpg is resized from (1280, 924) to (227, 227)
[ INFO ] Batch size is 1
[ INFO ] Loading model to the plugin
[ INFO ] Starting inference in synchronous mode
[ INFO ] Processing output blob
[ INFO ] Top 10 results: 
Image me.jpg

classid probability
------- -----------
  978    0.0818481
  977    0.0818481
  838    0.0812378
  975    0.0610657
  903    0.0553894
  638    0.0469971
  433    0.0398865
  976    0.0338440
  112    0.0317993
  639    0.0317993


[ INFO ] This sample is an API example, for any performance measurements please use the dedicated benchmark_app tool
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➜  samples python ./python/classification_sample/classification_sample.py -m /opt/intel/dldt/model-optimizer/alexnet.xml -i parents.jpg -d HETERO:MYRIAD -nt 10
[ INFO ] Creating Inference Engine
[ INFO ] Loading network files:
	/opt/intel/dldt/model-optimizer/alexnet.xml
	/opt/intel/dldt/model-optimizer/alexnet.bin
[ INFO ] Preparing input blobs
[ WARNING ] Image parents.jpg is resized from (960, 1280) to (227, 227)
[ INFO ] Batch size is 1
[ INFO ] Loading model to the plugin
[ INFO ] Starting inference in synchronous mode
[ INFO ] Processing output blob
[ INFO ] Top 10 results: 
Image parents.jpg

classid probability
------- -----------
  707    0.0590515
  577    0.0267334
  813    0.0212250
  704    0.0199432
  910    0.0197906
  955    0.0183716
  515    0.0182190
  523    0.0135040
  808    0.0129242
  918    0.0126648

3.5.4 Results

Classification Result for me.jpg

Result from NCS 2 Result from NCS 1
838: 'sunscreen, sunblock, sun blocker', 978: 'seashore, coast, seacoast, sea-coast',
978: 'seashore, coast, seacoast, sea-coast', 977: 'sandbar, sand bar',
977: 'sandbar, sand bar', 838: 'sunscreen, sunblock, sun blocker',
975: 'lakeside, lakeshore', 975: 'lakeside, lakeshore',
903: 'wig', 903: 'wig',
638: 'maillot', 638: 'maillot',
976: 'promontory, headland, head, foreland', 433: 'bathing cap, swimming cap',
433: 'bathing cap, swimming cap', 976: 'promontory, headland, head, foreland',
112: 'conch', 112: 'conch',
639: 'maillot, tank suit', 639: 'maillot, tank suit',

Classification Result for parents.jpg

Result from NCS 2 Result from NCS 1
707: 'pay-phone, pay-station', 707: 'pay-phone, pay-station',
577: 'gong, tam-tam', 577: 'gong, tam-tam',
704: 'parking meter', 813: 'spatula',
955: 'jackfruit, jak, jack', 704: 'parking meter',
813: 'spatula', 910: 'wooden spoon',
910: 'wooden spoon', 515: 'cowboy hat, ten-gallon hat',
515: 'cowboy hat, ten-gallon hat', 955: 'jackfruit, jak, jack',
605: 'iPod', 523: 'crutch',
918: 'crossword puzzle, crossword', 808: 'sombrero',
523: 'crutch', 918: 'crossword puzzle, crossword',

classid table:

{0: 'tench, Tinca tinca',
1: 'goldfish, Carassius auratus',
2: 'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias',
3: 'tiger shark, Galeocerdo cuvieri',
4: 'hammerhead, hammerhead shark',
5: 'electric ray, crampfish, numbfish, torpedo',
6: 'stingray',
7: 'cock',
8: 'hen',
9: 'ostrich, Struthio camelus',
10: 'brambling, Fringilla montifringilla',
11: 'goldfinch, Carduelis carduelis',
12: 'house finch, linnet, Carpodacus mexicanus',
13: 'junco, snowbird',
14: 'indigo bunting, indigo finch, indigo bird, Passerina cyanea',
15: 'robin, American robin, Turdus migratorius',
16: 'bulbul',
17: 'jay',
18: 'magpie',
19: 'chickadee',
20: 'water ouzel, dipper',
21: 'kite',
22: 'bald eagle, American eagle, Haliaeetus leucocephalus',
23: 'vulture',
24: 'great grey owl, great gray owl, Strix nebulosa',
25: 'European fire salamander, Salamandra salamandra',
26: 'common newt, Triturus vulgaris',
27: 'eft',
28: 'spotted salamander, Ambystoma maculatum',
29: 'axolotl, mud puppy, Ambystoma mexicanum',
30: 'bullfrog, Rana catesbeiana',
31: 'tree frog, tree-frog',
32: 'tailed frog, bell toad, ribbed toad, tailed toad, Ascaphus trui',
33: 'loggerhead, loggerhead turtle, Caretta caretta',
34: 'leatherback turtle, leatherback, leathery turtle, Dermochelys coriacea',
35: 'mud turtle',
36: 'terrapin',
37: 'box turtle, box tortoise',
38: 'banded gecko',
39: 'common iguana, iguana, Iguana iguana',
40: 'American chameleon, anole, Anolis carolinensis',
41: 'whiptail, whiptail lizard',
42: 'agama',
43: 'frilled lizard, Chlamydosaurus kingi',
44: 'alligator lizard',
45: 'Gila monster, Heloderma suspectum',
46: 'green lizard, Lacerta viridis',
47: 'African chameleon, Chamaeleo chamaeleon',
48: 'Komodo dragon, Komodo lizard, dragon lizard, giant lizard, Varanus komodoensis',
49: 'African crocodile, Nile crocodile, Crocodylus niloticus',
50: 'American alligator, Alligator mississipiensis',
51: 'triceratops',
52: 'thunder snake, worm snake, Carphophis amoenus',
53: 'ringneck snake, ring-necked snake, ring snake',
54: 'hognose snake, puff adder, sand viper',
55: 'green snake, grass snake',
56: 'king snake, kingsnake',
57: 'garter snake, grass snake',
58: 'water snake',
59: 'vine snake',
60: 'night snake, Hypsiglena torquata',
61: 'boa constrictor, Constrictor constrictor',
62: 'rock python, rock snake, Python sebae',
63: 'Indian cobra, Naja naja',
64: 'green mamba',
65: 'sea snake',
66: 'horned viper, cerastes, sand viper, horned asp, Cerastes cornutus',
67: 'diamondback, diamondback rattlesnake, Crotalus adamanteus',
68: 'sidewinder, horned rattlesnake, Crotalus cerastes',
69: 'trilobite',
70: 'harvestman, daddy longlegs, Phalangium opilio',
71: 'scorpion',
72: 'black and gold garden spider, Argiope aurantia',
73: 'barn spider, Araneus cavaticus',
74: 'garden spider, Aranea diademata',
75: 'black widow, Latrodectus mactans',
76: 'tarantula',
77: 'wolf spider, hunting spider',
78: 'tick',
79: 'centipede',
80: 'black grouse',
81: 'ptarmigan',
82: 'ruffed grouse, partridge, Bonasa umbellus',
83: 'prairie chicken, prairie grouse, prairie fowl',
84: 'peacock',
85: 'quail',
86: 'partridge',
87: 'African grey, African gray, Psittacus erithacus',
88: 'macaw',
89: 'sulphur-crested cockatoo, Kakatoe galerita, Cacatua galerita',
90: 'lorikeet',
91: 'coucal',
92: 'bee eater',
93: 'hornbill',
94: 'hummingbird',
95: 'jacamar',
96: 'toucan',
97: 'drake',
98: 'red-breasted merganser, Mergus serrator',
99: 'goose',
100: 'black swan, Cygnus atratus',
101: 'tusker',
102: 'echidna, spiny anteater, anteater',
103: 'platypus, duckbill, duckbilled platypus, duck-billed platypus, Ornithorhynchus anatinus',
104: 'wallaby, brush kangaroo',
105: 'koala, koala bear, kangaroo bear, native bear, Phascolarctos cinereus',
106: 'wombat',
107: 'jellyfish',
108: 'sea anemone, anemone',
109: 'brain coral',
110: 'flatworm, platyhelminth',
111: 'nematode, nematode worm, roundworm',
112: 'conch',
113: 'snail',
114: 'slug',
115: 'sea slug, nudibranch',
116: 'chiton, coat-of-mail shell, sea cradle, polyplacophore',
117: 'chambered nautilus, pearly nautilus, nautilus',
118: 'Dungeness crab, Cancer magister',
119: 'rock crab, Cancer irroratus',
120: 'fiddler crab',
121: 'king crab, Alaska crab, Alaskan king crab, Alaska king crab, Paralithodes camtschatica',
122: 'American lobster, Northern lobster, Maine lobster, Homarus americanus',
123: 'spiny lobster, langouste, rock lobster, crawfish, crayfish, sea crawfish',
124: 'crayfish, crawfish, crawdad, crawdaddy',
125: 'hermit crab',
126: 'isopod',
127: 'white stork, Ciconia ciconia',
128: 'black stork, Ciconia nigra',
129: 'spoonbill',
130: 'flamingo',
131: 'little blue heron, Egretta caerulea',
132: 'American egret, great white heron, Egretta albus',
133: 'bittern',
134: 'crane',
135: 'limpkin, Aramus pictus',
136: 'European gallinule, Porphyrio porphyrio',
137: 'American coot, marsh hen, mud hen, water hen, Fulica americana',
138: 'bustard',
139: 'ruddy turnstone, Arenaria interpres',
140: 'red-backed sandpiper, dunlin, Erolia alpina',
141: 'redshank, Tringa totanus',
142: 'dowitcher',
143: 'oystercatcher, oyster catcher',
144: 'pelican',
145: 'king penguin, Aptenodytes patagonica',
146: 'albatross, mollymawk',
147: 'grey whale, gray whale, devilfish, Eschrichtius gibbosus, Eschrichtius robustus',
148: 'killer whale, killer, orca, grampus, sea wolf, Orcinus orca',
149: 'dugong, Dugong dugon',
150: 'sea lion',
151: 'Chihuahua',
152: 'Japanese spaniel',
153: 'Maltese dog, Maltese terrier, Maltese',
154: 'Pekinese, Pekingese, Peke',
155: 'Shih-Tzu',
156: 'Blenheim spaniel',
157: 'papillon',
158: 'toy terrier',
159: 'Rhodesian ridgeback',
160: 'Afghan hound, Afghan',
161: 'basset, basset hound',
162: 'beagle',
163: 'bloodhound, sleuthhound',
164: 'bluetick',
165: 'black-and-tan coonhound',
166: 'Walker hound, Walker foxhound',
167: 'English foxhound',
168: 'redbone',
169: 'borzoi, Russian wolfhound',
170: 'Irish wolfhound',
171: 'Italian greyhound',
172: 'whippet',
173: 'Ibizan hound, Ibizan Podenco',
174: 'Norwegian elkhound, elkhound',
175: 'otterhound, otter hound',
176: 'Saluki, gazelle hound',
177: 'Scottish deerhound, deerhound',
178: 'Weimaraner',
179: 'Staffordshire bullterrier, Staffordshire bull terrier',
180: 'American Staffordshire terrier, Staffordshire terrier, American pit bull terrier, pit bull terrier',
181: 'Bedlington terrier',
182: 'Border terrier',
183: 'Kerry blue terrier',
184: 'Irish terrier',
185: 'Norfolk terrier',
186: 'Norwich terrier',
187: 'Yorkshire terrier',
188: 'wire-haired fox terrier',
189: 'Lakeland terrier',
190: 'Sealyham terrier, Sealyham',
191: 'Airedale, Airedale terrier',
192: 'cairn, cairn terrier',
193: 'Australian terrier',
194: 'Dandie Dinmont, Dandie Dinmont terrier',
195: 'Boston bull, Boston terrier',
196: 'miniature schnauzer',
197: 'giant schnauzer',
198: 'standard schnauzer',
199: 'Scotch terrier, Scottish terrier, Scottie',
200: 'Tibetan terrier, chrysanthemum dog',
201: 'silky terrier, Sydney silky',
202: 'soft-coated wheaten terrier',
203: 'West Highland white terrier',
204: 'Lhasa, Lhasa apso',
205: 'flat-coated retriever',
206: 'curly-coated retriever',
207: 'golden retriever',
208: 'Labrador retriever',
209: 'Chesapeake Bay retriever',
210: 'German short-haired pointer',
211: 'vizsla, Hungarian pointer',
212: 'English setter',
213: 'Irish setter, red setter',
214: 'Gordon setter',
215: 'Brittany spaniel',
216: 'clumber, clumber spaniel',
217: 'English springer, English springer spaniel',
218: 'Welsh springer spaniel',
219: 'cocker spaniel, English cocker spaniel, cocker',
220: 'Sussex spaniel',
221: 'Irish water spaniel',
222: 'kuvasz',
223: 'schipperke',
224: 'groenendael',
225: 'malinois',
226: 'briard',
227: 'kelpie',
228: 'komondor',
229: 'Old English sheepdog, bobtail',
230: 'Shetland sheepdog, Shetland sheep dog, Shetland',
231: 'collie',
232: 'Border collie',
233: 'Bouvier des Flandres, Bouviers des Flandres',
234: 'Rottweiler',
235: 'German shepherd, German shepherd dog, German police dog, alsatian',
236: 'Doberman, Doberman pinscher',
237: 'miniature pinscher',
238: 'Greater Swiss Mountain dog',
239: 'Bernese mountain dog',
240: 'Appenzeller',
241: 'EntleBucher',
242: 'boxer',
243: 'bull mastiff',
244: 'Tibetan mastiff',
245: 'French bulldog',
246: 'Great Dane',
247: 'Saint Bernard, St Bernard',
248: 'Eskimo dog, husky',
249: 'malamute, malemute, Alaskan malamute',
250: 'Siberian husky',
251: 'dalmatian, coach dog, carriage dog',
252: 'affenpinscher, monkey pinscher, monkey dog',
253: 'basenji',
254: 'pug, pug-dog',
255: 'Leonberg',
256: 'Newfoundland, Newfoundland dog',
257: 'Great Pyrenees',
258: 'Samoyed, Samoyede',
259: 'Pomeranian',
260: 'chow, chow chow',
261: 'keeshond',
262: 'Brabancon griffon',
263: 'Pembroke, Pembroke Welsh corgi',
264: 'Cardigan, Cardigan Welsh corgi',
265: 'toy poodle',
266: 'miniature poodle',
267: 'standard poodle',
268: 'Mexican hairless',
269: 'timber wolf, grey wolf, gray wolf, Canis lupus',
270: 'white wolf, Arctic wolf, Canis lupus tundrarum',
271: 'red wolf, maned wolf, Canis rufus, Canis niger',
272: 'coyote, prairie wolf, brush wolf, Canis latrans',
273: 'dingo, warrigal, warragal, Canis dingo',
274: 'dhole, Cuon alpinus',
275: 'African hunting dog, hyena dog, Cape hunting dog, Lycaon pictus',
276: 'hyena, hyaena',
277: 'red fox, Vulpes vulpes',
278: 'kit fox, Vulpes macrotis',
279: 'Arctic fox, white fox, Alopex lagopus',
280: 'grey fox, gray fox, Urocyon cinereoargenteus',
281: 'tabby, tabby cat',
282: 'tiger cat',
283: 'Persian cat',
284: 'Siamese cat, Siamese',
285: 'Egyptian cat',
286: 'cougar, puma, catamount, mountain lion, painter, panther, Felis concolor',
287: 'lynx, catamount',
288: 'leopard, Panthera pardus',
289: 'snow leopard, ounce, Panthera uncia',
290: 'jaguar, panther, Panthera onca, Felis onca',
291: 'lion, king of beasts, Panthera leo',
292: 'tiger, Panthera tigris',
293: 'cheetah, chetah, Acinonyx jubatus',
294: 'brown bear, bruin, Ursus arctos',
295: 'American black bear, black bear, Ursus americanus, Euarctos americanus',
296: 'ice bear, polar bear, Ursus Maritimus, Thalarctos maritimus',
297: 'sloth bear, Melursus ursinus, Ursus ursinus',
298: 'mongoose',
299: 'meerkat, mierkat',
300: 'tiger beetle',
301: 'ladybug, ladybeetle, lady beetle, ladybird, ladybird beetle',
302: 'ground beetle, carabid beetle',
303: 'long-horned beetle, longicorn, longicorn beetle',
304: 'leaf beetle, chrysomelid',
305: 'dung beetle',
306: 'rhinoceros beetle',
307: 'weevil',
308: 'fly',
309: 'bee',
310: 'ant, emmet, pismire',
311: 'grasshopper, hopper',
312: 'cricket',
313: 'walking stick, walkingstick, stick insect',
314: 'cockroach, roach',
315: 'mantis, mantid',
316: 'cicada, cicala',
317: 'leafhopper',
318: 'lacewing, lacewing fly',
319: "dragonfly, darning needle, devil's darning needle, sewing needle, snake feeder, snake doctor, mosquito hawk, skeeter hawk",
320: 'damselfly',
321: 'admiral',
322: 'ringlet, ringlet butterfly',
323: 'monarch, monarch butterfly, milkweed butterfly, Danaus plexippus',
324: 'cabbage butterfly',
325: 'sulphur butterfly, sulfur butterfly',
326: 'lycaenid, lycaenid butterfly',
327: 'starfish, sea star',
328: 'sea urchin',
329: 'sea cucumber, holothurian',
330: 'wood rabbit, cottontail, cottontail rabbit',
331: 'hare',
332: 'Angora, Angora rabbit',
333: 'hamster',
334: 'porcupine, hedgehog',
335: 'fox squirrel, eastern fox squirrel, Sciurus niger',
336: 'marmot',
337: 'beaver',
338: 'guinea pig, Cavia cobaya',
339: 'sorrel',
340: 'zebra',
341: 'hog, pig, grunter, squealer, Sus scrofa',
342: 'wild boar, boar, Sus scrofa',
343: 'warthog',
344: 'hippopotamus, hippo, river horse, Hippopotamus amphibius',
345: 'ox',
346: 'water buffalo, water ox, Asiatic buffalo, Bubalus bubalis',
347: 'bison',
348: 'ram, tup',
349: 'bighorn, bighorn sheep, cimarron, Rocky Mountain bighorn, Rocky Mountain sheep, Ovis canadensis',
350: 'ibex, Capra ibex',
351: 'hartebeest',
352: 'impala, Aepyceros melampus',
353: 'gazelle',
354: 'Arabian camel, dromedary, Camelus dromedarius',
355: 'llama',
356: 'weasel',
357: 'mink',
358: 'polecat, fitch, foulmart, foumart, Mustela putorius',
359: 'black-footed ferret, ferret, Mustela nigripes',
360: 'otter',
361: 'skunk, polecat, wood pussy',
362: 'badger',
363: 'armadillo',
364: 'three-toed sloth, ai, Bradypus tridactylus',
365: 'orangutan, orang, orangutang, Pongo pygmaeus',
366: 'gorilla, Gorilla gorilla',
367: 'chimpanzee, chimp, Pan troglodytes',
368: 'gibbon, Hylobates lar',
369: 'siamang, Hylobates syndactylus, Symphalangus syndactylus',
370: 'guenon, guenon monkey',
371: 'patas, hussar monkey, Erythrocebus patas',
372: 'baboon',
373: 'macaque',
374: 'langur',
375: 'colobus, colobus monkey',
376: 'proboscis monkey, Nasalis larvatus',
377: 'marmoset',
378: 'capuchin, ringtail, Cebus capucinus',
379: 'howler monkey, howler',
380: 'titi, titi monkey',
381: 'spider monkey, Ateles geoffroyi',
382: 'squirrel monkey, Saimiri sciureus',
383: 'Madagascar cat, ring-tailed lemur, Lemur catta',
384: 'indri, indris, Indri indri, Indri brevicaudatus',
385: 'Indian elephant, Elephas maximus',
386: 'African elephant, Loxodonta africana',
387: 'lesser panda, red panda, panda, bear cat, cat bear, Ailurus fulgens',
388: 'giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca',
389: 'barracouta, snoek',
390: 'eel',
391: 'coho, cohoe, coho salmon, blue jack, silver salmon, Oncorhynchus kisutch',
392: 'rock beauty, Holocanthus tricolor',
393: 'anemone fish',
394: 'sturgeon',
395: 'gar, garfish, garpike, billfish, Lepisosteus osseus',
396: 'lionfish',
397: 'puffer, pufferfish, blowfish, globefish',
398: 'abacus',
399: 'abaya',
400: "academic gown, academic robe, judge's robe",
401: 'accordion, piano accordion, squeeze box',
402: 'acoustic guitar',
403: 'aircraft carrier, carrier, flattop, attack aircraft carrier',
404: 'airliner',
405: 'airship, dirigible',
406: 'altar',
407: 'ambulance',
408: 'amphibian, amphibious vehicle',
409: 'analog clock',
410: 'apiary, bee house',
411: 'apron',
412: 'ashcan, trash can, garbage can, wastebin, ash bin, ash-bin, ashbin, dustbin, trash barrel, trash bin',
413: 'assault rifle, assault gun',
414: 'backpack, back pack, knapsack, packsack, rucksack, haversack',
415: 'bakery, bakeshop, bakehouse',
416: 'balance beam, beam',
417: 'balloon',
418: 'ballpoint, ballpoint pen, ballpen, Biro',
419: 'Band Aid',
420: 'banjo',
421: 'bannister, banister, balustrade, balusters, handrail',
422: 'barbell',
423: 'barber chair',
424: 'barbershop',
425: 'barn',
426: 'barometer',
427: 'barrel, cask',
428: 'barrow, garden cart, lawn cart, wheelbarrow',
429: 'baseball',
430: 'basketball',
431: 'bassinet',
432: 'bassoon',
433: 'bathing cap, swimming cap',
434: 'bath towel',
435: 'bathtub, bathing tub, bath, tub',
436: 'beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon',
437: 'beacon, lighthouse, beacon light, pharos',
438: 'beaker',
439: 'bearskin, busby, shako',
440: 'beer bottle',
441: 'beer glass',
442: 'bell cote, bell cot',
443: 'bib',
444: 'bicycle-built-for-two, tandem bicycle, tandem',
445: 'bikini, two-piece',
446: 'binder, ring-binder',
447: 'binoculars, field glasses, opera glasses',
448: 'birdhouse',
449: 'boathouse',
450: 'bobsled, bobsleigh, bob',
451: 'bolo tie, bolo, bola tie, bola',
452: 'bonnet, poke bonnet',
453: 'bookcase',
454: 'bookshop, bookstore, bookstall',
455: 'bottlecap',
456: 'bow',
457: 'bow tie, bow-tie, bowtie',
458: 'brass, memorial tablet, plaque',
459: 'brassiere, bra, bandeau',
460: 'breakwater, groin, groyne, mole, bulwark, seawall, jetty',
461: 'breastplate, aegis, egis',
462: 'broom',
463: 'bucket, pail',
464: 'buckle',
465: 'bulletproof vest',
466: 'bullet train, bullet',
467: 'butcher shop, meat market',
468: 'cab, hack, taxi, taxicab',
469: 'caldron, cauldron',
470: 'candle, taper, wax light',
471: 'cannon',
472: 'canoe',
473: 'can opener, tin opener',
474: 'cardigan',
475: 'car mirror',
476: 'carousel, carrousel, merry-go-round, roundabout, whirligig',
477: "carpenter's kit, tool kit",
478: 'carton',
479: 'car wheel',
480: 'cash machine, cash dispenser, automated teller machine, automatic teller machine, automated teller, automatic teller, ATM',
481: 'cassette',
482: 'cassette player',
483: 'castle',
484: 'catamaran',
485: 'CD player',
486: 'cello, violoncello',
487: 'cellular telephone, cellular phone, cellphone, cell, mobile phone',
488: 'chain',
489: 'chainlink fence',
490: 'chain mail, ring mail, mail, chain armor, chain armour, ring armor, ring armour',
491: 'chain saw, chainsaw',
492: 'chest',
493: 'chiffonier, commode',
494: 'chime, bell, gong',
495: 'china cabinet, china closet',
496: 'Christmas stocking',
497: 'church, church building',
498: 'cinema, movie theater, movie theatre, movie house, picture palace',
499: 'cleaver, meat cleaver, chopper',
500: 'cliff dwelling',
501: 'cloak',
502: 'clog, geta, patten, sabot',
503: 'cocktail shaker',
504: 'coffee mug',
505: 'coffeepot',
506: 'coil, spiral, volute, whorl, helix',
507: 'combination lock',
508: 'computer keyboard, keypad',
509: 'confectionery, confectionary, candy store',
510: 'container ship, containership, container vessel',
511: 'convertible',
512: 'corkscrew, bottle screw',
513: 'cornet, horn, trumpet, trump',
514: 'cowboy boot',
515: 'cowboy hat, ten-gallon hat',
516: 'cradle',
517: 'crane',
518: 'crash helmet',
519: 'crate',
520: 'crib, cot',
521: 'Crock Pot',
522: 'croquet ball',
523: 'crutch',
524: 'cuirass',
525: 'dam, dike, dyke',
526: 'desk',
527: 'desktop computer',
528: 'dial telephone, dial phone',
529: 'diaper, nappy, napkin',
530: 'digital clock',
531: 'digital watch',
532: 'dining table, board',
533: 'dishrag, dishcloth',
534: 'dishwasher, dish washer, dishwashing machine',
535: 'disk brake, disc brake',
536: 'dock, dockage, docking facility',
537: 'dogsled, dog sled, dog sleigh',
538: 'dome',
539: 'doormat, welcome mat',
540: 'drilling platform, offshore rig',
541: 'drum, membranophone, tympan',
542: 'drumstick',
543: 'dumbbell',
544: 'Dutch oven',
545: 'electric fan, blower',
546: 'electric guitar',
547: 'electric locomotive',
548: 'entertainment center',
549: 'envelope',
550: 'espresso maker',
551: 'face powder',
552: 'feather boa, boa',
553: 'file, file cabinet, filing cabinet',
554: 'fireboat',
555: 'fire engine, fire truck',
556: 'fire screen, fireguard',
557: 'flagpole, flagstaff',
558: 'flute, transverse flute',
559: 'folding chair',
560: 'football helmet',
561: 'forklift',
562: 'fountain',
563: 'fountain pen',
564: 'four-poster',
565: 'freight car',
566: 'French horn, horn',
567: 'frying pan, frypan, skillet',
568: 'fur coat',
569: 'garbage truck, dustcart',
570: 'gasmask, respirator, gas helmet',
571: 'gas pump, gasoline pump, petrol pump, island dispenser',
572: 'goblet',
573: 'go-kart',
574: 'golf ball',
575: 'golfcart, golf cart',
576: 'gondola',
577: 'gong, tam-tam',
578: 'gown',
579: 'grand piano, grand',
580: 'greenhouse, nursery, glasshouse',
581: 'grille, radiator grille',
582: 'grocery store, grocery, food market, market',
583: 'guillotine',
584: 'hair slide',
585: 'hair spray',
586: 'half track',
587: 'hammer',
588: 'hamper',
589: 'hand blower, blow dryer, blow drier, hair dryer, hair drier',
590: 'hand-held computer, hand-held microcomputer',
591: 'handkerchief, hankie, hanky, hankey',
592: 'hard disc, hard disk, fixed disk',
593: 'harmonica, mouth organ, harp, mouth harp',
594: 'harp',
595: 'harvester, reaper',
596: 'hatchet',
597: 'holster',
598: 'home theater, home theatre',
599: 'honeycomb',
600: 'hook, claw',
601: 'hoopskirt, crinoline',
602: 'horizontal bar, high bar',
603: 'horse cart, horse-cart',
604: 'hourglass',
605: 'iPod',
606: 'iron, smoothing iron',
607: "jack-o'-lantern",
608: 'jean, blue jean, denim',
609: 'jeep, landrover',
610: 'jersey, T-shirt, tee shirt',
611: 'jigsaw puzzle',
612: 'jinrikisha, ricksha, rickshaw',
613: 'joystick',
614: 'kimono',
615: 'knee pad',
616: 'knot',
617: 'lab coat, laboratory coat',
618: 'ladle',
619: 'lampshade, lamp shade',
620: 'laptop, laptop computer',
621: 'lawn mower, mower',
622: 'lens cap, lens cover',
623: 'letter opener, paper knife, paperknife',
624: 'library',
625: 'lifeboat',
626: 'lighter, light, igniter, ignitor',
627: 'limousine, limo',
628: 'liner, ocean liner',
629: 'lipstick, lip rouge',
630: 'Loafer',
631: 'lotion',
632: 'loudspeaker, speaker, speaker unit, loudspeaker system, speaker system',
633: "loupe, jeweler's loupe",
634: 'lumbermill, sawmill',
635: 'magnetic compass',
636: 'mailbag, postbag',
637: 'mailbox, letter box',
638: 'maillot',
639: 'maillot, tank suit',
640: 'manhole cover',
641: 'maraca',
642: 'marimba, xylophone',
643: 'mask',
644: 'matchstick',
645: 'maypole',
646: 'maze, labyrinth',
647: 'measuring cup',
648: 'medicine chest, medicine cabinet',
649: 'megalith, megalithic structure',
650: 'microphone, mike',
651: 'microwave, microwave oven',
652: 'military uniform',
653: 'milk can',
654: 'minibus',
655: 'miniskirt, mini',
656: 'minivan',
657: 'missile',
658: 'mitten',
659: 'mixing bowl',
660: 'mobile home, manufactured home',
661: 'Model T',
662: 'modem',
663: 'monastery',
664: 'monitor',
665: 'moped',
666: 'mortar',
667: 'mortarboard',
668: 'mosque',
669: 'mosquito net',
670: 'motor scooter, scooter',
671: 'mountain bike, all-terrain bike, off-roader',
672: 'mountain tent',
673: 'mouse, computer mouse',
674: 'mousetrap',
675: 'moving van',
676: 'muzzle',
677: 'nail',
678: 'neck brace',
679: 'necklace',
680: 'nipple',
681: 'notebook, notebook computer',
682: 'obelisk',
683: 'oboe, hautboy, hautbois',
684: 'ocarina, sweet potato',
685: 'odometer, hodometer, mileometer, milometer',
686: 'oil filter',
687: 'organ, pipe organ',
688: 'oscilloscope, scope, cathode-ray oscilloscope, CRO',
689: 'overskirt',
690: 'oxcart',
691: 'oxygen mask',
692: 'packet',
693: 'paddle, boat paddle',
694: 'paddlewheel, paddle wheel',
695: 'padlock',
696: 'paintbrush',
697: "pajama, pyjama, pj's, jammies",
698: 'palace',
699: 'panpipe, pandean pipe, syrinx',
700: 'paper towel',
701: 'parachute, chute',
702: 'parallel bars, bars',
703: 'park bench',
704: 'parking meter',
705: 'passenger car, coach, carriage',
706: 'patio, terrace',
707: 'pay-phone, pay-station',
708: 'pedestal, plinth, footstall',
709: 'pencil box, pencil case',
710: 'pencil sharpener',
711: 'perfume, essence',
712: 'Petri dish',
713: 'photocopier',
714: 'pick, plectrum, plectron',
715: 'pickelhaube',
716: 'picket fence, paling',
717: 'pickup, pickup truck',
718: 'pier',
719: 'piggy bank, penny bank',
720: 'pill bottle',
721: 'pillow',
722: 'ping-pong ball',
723: 'pinwheel',
724: 'pirate, pirate ship',
725: 'pitcher, ewer',
726: "plane, carpenter's plane, woodworking plane",
727: 'planetarium',
728: 'plastic bag',
729: 'plate rack',
730: 'plow, plough',
731: "plunger, plumber's helper",
732: 'Polaroid camera, Polaroid Land camera',
733: 'pole',
734: 'police van, police wagon, paddy wagon, patrol wagon, wagon, black Maria',
735: 'poncho',
736: 'pool table, billiard table, snooker table',
737: 'pop bottle, soda bottle',
738: 'pot, flowerpot',
739: "potter's wheel",
740: 'power drill',
741: 'prayer rug, prayer mat',
742: 'printer',
743: 'prison, prison house',
744: 'projectile, missile',
745: 'projector',
746: 'puck, hockey puck',
747: 'punching bag, punch bag, punching ball, punchball',
748: 'purse',
749: 'quill, quill pen',
750: 'quilt, comforter, comfort, puff',
751: 'racer, race car, racing car',
752: 'racket, racquet',
753: 'radiator',
754: 'radio, wireless',
755: 'radio telescope, radio reflector',
756: 'rain barrel',
757: 'recreational vehicle, RV, R.V.',
758: 'reel',
759: 'reflex camera',
760: 'refrigerator, icebox',
761: 'remote control, remote',
762: 'restaurant, eating house, eating place, eatery',
763: 'revolver, six-gun, six-shooter',
764: 'rifle',
765: 'rocking chair, rocker',
766: 'rotisserie',
767: 'rubber eraser, rubber, pencil eraser',
768: 'rugby ball',
769: 'rule, ruler',
770: 'running shoe',
771: 'safe',
772: 'safety pin',
773: 'saltshaker, salt shaker',
774: 'sandal',
775: 'sarong',
776: 'sax, saxophone',
777: 'scabbard',
778: 'scale, weighing machine',
779: 'school bus',
780: 'schooner',
781: 'scoreboard',
782: 'screen, CRT screen',
783: 'screw',
784: 'screwdriver',
785: 'seat belt, seatbelt',
786: 'sewing machine',
787: 'shield, buckler',
788: 'shoe shop, shoe-shop, shoe store',
789: 'shoji',
790: 'shopping basket',
791: 'shopping cart',
792: 'shovel',
793: 'shower cap',
794: 'shower curtain',
795: 'ski',
796: 'ski mask',
797: 'sleeping bag',
798: 'slide rule, slipstick',
799: 'sliding door',
800: 'slot, one-armed bandit',
801: 'snorkel',
802: 'snowmobile',
803: 'snowplow, snowplough',
804: 'soap dispenser',
805: 'soccer ball',
806: 'sock',
807: 'solar dish, solar collector, solar furnace',
808: 'sombrero',
809: 'soup bowl',
810: 'space bar',
811: 'space heater',
812: 'space shuttle',
813: 'spatula',
814: 'speedboat',
815: "spider web, spider's web",
816: 'spindle',
817: 'sports car, sport car',
818: 'spotlight, spot',
819: 'stage',
820: 'steam locomotive',
821: 'steel arch bridge',
822: 'steel drum',
823: 'stethoscope',
824: 'stole',
825: 'stone wall',
826: 'stopwatch, stop watch',
827: 'stove',
828: 'strainer',
829: 'streetcar, tram, tramcar, trolley, trolley car',
830: 'stretcher',
831: 'studio couch, day bed',
832: 'stupa, tope',
833: 'submarine, pigboat, sub, U-boat',
834: 'suit, suit of clothes',
835: 'sundial',
836: 'sunglass',
837: 'sunglasses, dark glasses, shades',
838: 'sunscreen, sunblock, sun blocker',
839: 'suspension bridge',
840: 'swab, swob, mop',
841: 'sweatshirt',
842: 'swimming trunks, bathing trunks',
843: 'swing',
844: 'switch, electric switch, electrical switch',
845: 'syringe',
846: 'table lamp',
847: 'tank, army tank, armored combat vehicle, armoured combat vehicle',
848: 'tape player',
849: 'teapot',
850: 'teddy, teddy bear',
851: 'television, television system',
852: 'tennis ball',
853: 'thatch, thatched roof',
854: 'theater curtain, theatre curtain',
855: 'thimble',
856: 'thresher, thrasher, threshing machine',
857: 'throne',
858: 'tile roof',
859: 'toaster',
860: 'tobacco shop, tobacconist shop, tobacconist',
861: 'toilet seat',
862: 'torch',
863: 'totem pole',
864: 'tow truck, tow car, wrecker',
865: 'toyshop',
866: 'tractor',
867: 'trailer truck, tractor trailer, trucking rig, rig, articulated lorry, semi',
868: 'tray',
869: 'trench coat',
870: 'tricycle, trike, velocipede',
871: 'trimaran',
872: 'tripod',
873: 'triumphal arch',
874: 'trolleybus, trolley coach, trackless trolley',
875: 'trombone',
876: 'tub, vat',
877: 'turnstile',
878: 'typewriter keyboard',
879: 'umbrella',
880: 'unicycle, monocycle',
881: 'upright, upright piano',
882: 'vacuum, vacuum cleaner',
883: 'vase',
884: 'vault',
885: 'velvet',
886: 'vending machine',
887: 'vestment',
888: 'viaduct',
889: 'violin, fiddle',
890: 'volleyball',
891: 'waffle iron',
892: 'wall clock',
893: 'wallet, billfold, notecase, pocketbook',
894: 'wardrobe, closet, press',
895: 'warplane, military plane',
896: 'washbasin, handbasin, washbowl, lavabo, wash-hand basin',
897: 'washer, automatic washer, washing machine',
898: 'water bottle',
899: 'water jug',
900: 'water tower',
901: 'whiskey jug',
902: 'whistle',
903: 'wig',
904: 'window screen',
905: 'window shade',
906: 'Windsor tie',
907: 'wine bottle',
908: 'wing',
909: 'wok',
910: 'wooden spoon',
911: 'wool, woolen, woollen',
912: 'worm fence, snake fence, snake-rail fence, Virginia fence',
913: 'wreck',
914: 'yawl',
915: 'yurt',
916: 'web site, website, internet site, site',
917: 'comic book',
918: 'crossword puzzle, crossword',
919: 'street sign',
920: 'traffic light, traffic signal, stoplight',
921: 'book jacket, dust cover, dust jacket, dust wrapper',
922: 'menu',
923: 'plate',
924: 'guacamole',
925: 'consomme',
926: 'hot pot, hotpot',
927: 'trifle',
928: 'ice cream, icecream',
929: 'ice lolly, lolly, lollipop, popsicle',
930: 'French loaf',
931: 'bagel, beigel',
932: 'pretzel',
933: 'cheeseburger',
934: 'hotdog, hot dog, red hot',
935: 'mashed potato',
936: 'head cabbage',
937: 'broccoli',
938: 'cauliflower',
939: 'zucchini, courgette',
940: 'spaghetti squash',
941: 'acorn squash',
942: 'butternut squash',
943: 'cucumber, cuke',
944: 'artichoke, globe artichoke',
945: 'bell pepper',
946: 'cardoon',
947: 'mushroom',
948: 'Granny Smith',
949: 'strawberry',
950: 'orange',
951: 'lemon',
952: 'fig',
953: 'pineapple, ananas',
954: 'banana',
955: 'jackfruit, jak, jack',
956: 'custard apple',
957: 'pomegranate',
958: 'hay',
959: 'carbonara',
960: 'chocolate sauce, chocolate syrup',
961: 'dough',
962: 'meat loaf, meatloaf',
963: 'pizza, pizza pie',
964: 'potpie',
965: 'burrito',
966: 'red wine',
967: 'espresso',
968: 'cup',
969: 'eggnog',
970: 'alp',
971: 'bubble',
972: 'cliff, drop, drop-off',
973: 'coral reef',
974: 'geyser',
975: 'lakeside, lakeshore',
976: 'promontory, headland, head, foreland',
977: 'sandbar, sand bar',
978: 'seashore, coast, seacoast, sea-coast',
979: 'valley, vale',
980: 'volcano',
981: 'ballplayer, baseball player',
982: 'groom, bridegroom',
983: 'scuba diver',
984: 'rapeseed',
985: 'daisy',
986: "yellow lady's slipper, yellow lady-slipper, Cypripedium calceolus, Cypripedium parviflorum",
987: 'corn',
988: 'acorn',
989: 'hip, rose hip, rosehip',
990: 'buckeye, horse chestnut, conker',
991: 'coral fungus',
992: 'agaric',
993: 'gyromitra',
994: 'stinkhorn, carrion fungus',
995: 'earthstar',
996: 'hen-of-the-woods, hen of the woods, Polyporus frondosus, Grifola frondosa',
997: 'bolete',
998: 'ear, spike, capitulum',
999: 'toilet tissue, toilet paper, bathroom tissue'}
view raw imagenet1000_clsidx_to_labels.txt hosted with ❤ by GitHub

4. OpenCV DNN with OpenVINO’s Inference Engine

For the example openvino_fd_myriad.py given on Install OpenVINO™ toolkit for Raspbian* OS, the TOUGH thing is how to build OpenCV with OpenVINO’s Inference Engine. NEVER forget to export export InferenceEngine_DIR="/opt/intel/openvino/inference_engine/share" and enable WITH_INF_ENGINE ON to have OpenCV rebuilt. Before moving forward, my modified version of openvino_fd_myriad.py is provided as follows:

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import sys, argparse
import cv2

parser = argparse.ArgumentParser(description='OpenVINO Face Detection on MYRIAD')
parser.add_argument('-x', '--XML', help='Required: model XML file')
parser.add_argument('-b', '--BIN', help='Required: model BIN file')
parser.add_argument('-i', '--input', help='Required: input image')
parser.add_argument('-o', '--output', help='Not Required: output resultant image, by default: out.jpg')


args = parser.parse_args()
xmlfile = args.XML
binfile = args.BIN
inputimage = args.input
outputimage = args.output

if not outputimage:
    outputimage = "out.jpg"

if not xmlfile:
    print(f'openvino_fd_myriad {args.xmlfile}')

if not binfile:
    print(f'openvino_fd_myriad {args.binfile}')
    
if not inputimage:
    print(f'openvino_fd_myriad {args.inputimage}')


       
# Load the model.
net = cv2.dnn.readNet(xmlfile, binfile)
# Specify target device.
net.setPreferableTarget(cv2.dnn.DNN_TARGET_MYRIAD)
# Read an image.
frame = cv2.imread(inputimage)
if frame is None:
    raise Exception('Image not found!')
# Prepare input blob and perform an inference.
print("Frame Size: {}".format(frame.shape))
blob = cv2.dnn.blobFromImage(frame, scalefactor=1.0, size=(224, 224), swapRB=True, ddepth=cv2.CV_32F)
net.setInput(blob)
print("First Blob: {}".format(blob.shape))
out = net.forward()
count = 0
# Draw detected faces on the frame.
for detection in out.reshape(-1, 7):
    confidence = float(detection[2])
    xmin = int(detection[3] * frame.shape[1])
    ymin = int(detection[4] * frame.shape[0])
    xmax = int(detection[5] * frame.shape[1])
    ymax = int(detection[6] * frame.shape[0])
    if confidence > 0.8:
        cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), color=(0, 255, 0))
        count += 1
print(count)
# Save the frame to an image file.
cv2.imwrite(outputimage, frame)

4.1 Intel64

On my laptop, of course, we are building OpenCV for architecture Intel64, with NVidia GPU + CUDA support, for DNN in OpenCV requires either CUDA or OpenCL.

My test result of openvino_fd_myriad.py shows the performance of adopted model face-detection-adas-0001 is NOT as good as expected.

For NCS 2

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➜  OpenVINO_Examples python ./openvino_fd_myriad.py -x /opt/intel/openvino/inference_engine/samples/cpp/build_18.04/intel64/Release/face-detection-adas-0001.xml -b /opt/intel/openvino/inference_engine/samples/cpp/build_18.04/intel64/Release/face-detection-adas-0001.bin -i ./parents.jpg
Frame Size: (960, 1280, 3)
First Blob: (1, 3, 224, 224)
2
➜  OpenVINO_Examples python ./openvino_fd_myriad.py -x /opt/intel/openvino/inference_engine/samples/cpp/build_18.04/intel64/Release/face-detection-adas-0001.xml -b /opt/intel/openvino/inference_engine/samples/cpp/build_18.04/intel64/Release/face-detection-adas-0001.bin -i ./me.jpg
Frame Size: (1280, 924, 3)
First Blob: (1, 3, 224, 224)
1

For NCS 1

1
2
3
4
5
6
7
8
➜  OpenVINO_Examples python ./openvino_fd_myriad.py -x /opt/intel/openvino/inference_engine/samples/cpp/build_18.04/intel64/Release/face-detection-adas-0001.xml -b /opt/intel/openvino/inference_engine/samples/cpp/build_18.04/intel64/Release/face-detection-adas-0001.bin -i ./parents.jpg
Frame Size: (960, 1280, 3)
First Blob: (1, 3, 224, 224)
2
➜  OpenVINO_Examples python ./openvino_fd_myriad.py -x /opt/intel/openvino/inference_engine/samples/cpp/build_18.04/intel64/Release/face-detection-adas-0001.xml -b /opt/intel/openvino/inference_engine/samples/cpp/build_18.04/intel64/Release/face-detection-adas-0001.bin -i ./me.jpg
Frame Size: (1280, 924, 3)
First Blob: (1, 3, 224, 224)
1

Results

Parents At First Starbucks Parents Face Detection By OpenCV DNN NCS 1 Parents Face Detection By OpenCV DNN NCS 2
Parents At First Starbucks Parents At First Starbucks - Face Detection By NCS 1 Parents At First Starbucks - Face Detection By NCS 2
Me Me Face Detection By OpenCV DNN NCS 1 Me Face Detection By OpenCV DNN NCS 2
Me Me - Face Detection By OpenCV DNN NCS 1 Me - Face Detection By OpenCV DNN NCS 2

4.2 armv7l

For NCS 2

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8
➜  Programs python ./openvino_fd_myriad.py -x /opt/intel/openvino/inference_engine/samples/face-detection-adas-0001.xml -b /opt/intel/openvino/inference_engine/samples/face-detection-adas-0001.bin -i ./parents.jpg
Frame Size: (960, 1280, 3)
First Blob: (1, 3, 224, 224)
2
➜  Programs python ./openvino_fd_myriad.py -x /opt/intel/openvino/inference_engine/samples/face-detection-adas-0001.xml -b /opt/intel/openvino/inference_engine/samples/face-detection-adas-0001.bin -i ./me.jpg
Frame Size: (1280, 924, 3)
First Blob: (1, 3, 224, 224)
1

For NCS 1

1
2
3
4
5
6
7
8
➜  Programs python ./openvino_fd_myriad.py -x /opt/intel/openvino/inference_engine/samples/face-detection-adas-0001.xml -b /opt/intel/openvino/inference_engine/samples/face-detection-adas-0001.bin -i ./parents.jpg
Frame Size: (960, 1280, 3)
First Blob: (1, 3, 224, 224)
2
➜  Programs python ./openvino_fd_myriad.py -x /opt/intel/openvino/inference_engine/samples/face-detection-adas-0001.xml -b /opt/intel/openvino/inference_engine/samples/face-detection-adas-0001.bin -i ./me.jpg
Frame Size: (1280, 924, 3)
First Blob: (1, 3, 224, 224)
1

Results

Parents At First Starbucks Parents Face Detection By OpenCV DNN NCS 1 Parents Face Detection By OpenCV DNN NCS 2
Parents At First Starbucks Parents At First Starbucks - Face Detection By NCS 1 Parents At First Starbucks - Face Detection By NCS 2
Me Me Face Detection By OpenCV DNN NCS 1 Me Face Detection By OpenCV DNN NCS 2
Me Me - Face Detection By OpenCV DNN NCS 1 Me - Face Detection By OpenCV DNN NCS 2

5. My Built Raspbian ISO With OpenCV4 + OpenVINO

Finally, you are welcome to try my built image rpi4-raspbian20200213-opencv4.3-openvino2020.1.023-ncsdk2.10.01.01.img, which is about 9.5G and composed of:

Everything has ALREADY been updated and built successfully, ONLY EXCEPT object_detection_sample_ssd. In addition, rpi4-raspbian20200213-opencv4.3-openvino2020.1.023-ncsdk2.10.01.01.img ALSO works properly on my Raspberry Pi 3 Model B Version 1.2 manufactured in 2015.