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【視覺進化論】AI智慧視覺運算技術論壇_2_ChungYeh

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The document discusses the Intel® OpenVINO™ Toolkit, which accelerates the development of AI visual applications, particularly for computer vision and deep learning. It outlines compatibility with various operating systems, hardware acceleration options, and includes resources for building and optimizing applications using machine learning frameworks. Additionally, it emphasizes the importance of model optimization and performance considerations across different Intel architectures.

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Intel® OpenVINO™ Toolkit 如何加速AI視覺應用開發
Internet of Things Group 2 Legalnotices&disclaimersThis document contains information on products, services and/or processes in development. All information provided here is subject to change without notice. Contact your Intel representative to obtain the latest forecast, schedule, specifications and roadmaps. Intel technologies’ features and benefits depend on system configuration and may require enabled hardware, software or service activation. Learn more at intel.com, or from the OEM or retailer. No computer system can be absolutely secure. Tests document performance of components on a particular test, in specific systems. Differences in hardware, software, or configuration will affect actual performance. Consult other sources of information to evaluate performance as you consider your purchase. For more complete information about performance and benchmark results, visit http://www.intel.com/performance. Cost reduction scenarios described are intended as examples of how a given Intel-based product, in the specified circumstances and configurations, may affect future costs and provide cost savings. Circumstances will vary. Intel does not guarantee any costs or cost reduction. Statements in this document that refer to Intel’s plans and expectations for the quarter, the year, and the future, are forward-looking statements that involve a number of risks and uncertainties. A detailed discussion of the factors that could affect Intel’s results and plans is included in Intel’s SEC filings, including the annual report on Form 10-K. The products described may contain design defects or errors known as errata which may cause the product to deviate from published specifications. Current characterized errata are available on request. Performance estimates were obtained prior to implementation of recent software patches and firmware updates intended to address exploits referred to as "Spectre" and "Meltdown." Implementation of these updates may make these results inapplicable to your device or system. No license (express or implied, by estoppel or otherwise) to any intellectual property rights is granted by this document. Intel does not control or audit third-party benchmark data or the web sites referenced in this document. You should visit the referenced web site and confirm whether referenced data are accurate. Results have been estimated or simulated using internal Intel analysis or architecture simulation or modeling, and provided to you for informational purposes. Any differences in your system hardware, software or configuration may affect your actual performance. Intel, the Intel logo, Pentium, Celeron, Atom, Core, Xeon, Movidius, Saffron, OpenVINO, MediaSDK and others are trademarks of Intel Corporation in the U.S. and/or other countries. *Other names and brands may be claimed as the property of others. © 2018 Intel Corporation.
Internet of Things Group 3 https://software.intel.com/en-us/openvino-toolkit
4 Artificial Intelligence is the ability of machines to learn from experience, without explicit programming, in order to perform cognitive functions associated with the human mind ArtificialIntelligence Machinelearning Algorithms whose performance improve as they are exposed to more data over time Deeplearning Subset of machine learning in which multi-layered neural networks learn from vast amounts of data
5 Lots of labeled data! Training Inference Forward Backward Model weights Forward “Bicycle”? “Strawberry” “Bicycle”? Error Human Bicycle Strawberry ?????? DeeplearningBasics Data set size Accuracy Didyouknow? Training with a large data set AND deep (many layered) neural network often leads to the highest accuracy inference
Internet of Things Group 6
7 OS Support CentOS* 7.4 (64 bit) Ubuntu* 16.04.3 LTS (64 bit) Microsoft Windows* 10 (64 bit) Yocto Project* version Poky Jethro v2.0.3 (64 bit) OpenVX and the OpenVX logo are trademarks of the Khronos Group Inc. OpenCL and the OpenCL logo are trademarks of Apple Inc. used by permission by Khronos Intel® Architecture-Based Platforms Support Intel® Deep Learning Deployment Toolkit Traditional Computer Vision Tools & Libraries Model Optimizer Convert & Optimize Inference Engine Optimized InferenceIR OpenCV* OpenVX* Photography Vision Optimized Libraries IR = Intermediate Representation file For Intel® CPU & CPU with integrated graphics Increase Media/Video/Graphics Performance Intel® Media SDK Open Source version OpenCL™ Drivers & Runtimes For CPU with integrated graphics Optimize Intel® FPGA FPGA RunTime Environment (from Intel® FPGA SDK for OpenCL™) Bitstreams FPGA – Linux* only Code Samples & 10 Pre-trained Models Code Samples Openvino™toolkitCross-Platform Tool to Accelerate Computer Vision & Deep Learning Inference Performance software.intel.com/openvino-toolkit All products, computer systems, dates, and figures are preliminary based on current expectations, and are subject to change without notice.
Internet of Things Group 8 Discover Intel® OpenVINO™ Toolkit Capabilities
Internet of Things Group 9 Hardware Acceleration
Internet of Things Group 10 Intel Architecture Multicore/Multithreading/Vectorization https://software.intel.com/en-us/articles/introduction-to-intel-advanced-vector-extensions
Internet of Things Group 11 Intel Integrated Graphics
Internet of Things Group 12 Movidius Neural Compute Stick (Myriad 2)
Internet of Things Group 13 Intel® FPGA Intel Arria 10 GX FPGA • High-performance, multi-gigabit SERDES transceivers up to 15 Gbps • 1,150K logic elements available (-2L speed grade) • 53 Mb of embedded memory On-Board Memory • 8 GB DDR4 memory banks with error correction code (ECC) (2 banks) • 1 Gb Mb (128 MB) flash Interfaces • PCIe x8 Gen3 electrical, x16 mechanical • USB 2.0 interface for debug and programming of FPGA and flash memory • 1X QSFP+ with 4X 10GbE or 40GbE support • Standard height, 1/2 length • Low-profile option upon request
Copyright © 2018, Intel Corporation. All rights reserved. *Other names and brands may be claimed as the property of others. Optimization NoticeOptimization Notice OpenVINO™ toolkit Technical Specifications Intel® Platforms Compatible Operating Systems Target Solution Platforms CPU  6th-8th generation Intel® Xeon® & Core™ processors  Ubuntu* 16.04.3 LTS (64 bit)  Microsoft Windows* 10 (64 bit)  CentOS* 7.4 (64 bit)  Intel® Pentium® processor N4200/5, N3350/5, N3450/5 with Intel® HD Graphics  Yocto Project* Poky Jethro v2.0.3 (64 bit) Iris® Pro & Intel® HD Graphics  6th-8th generation Intel® Core™ processor with Intel® Iris™ Pro graphics & Intel® HD Graphics  6th-8th generation Intel® Xeon® processor with Intel® Iris™ Pro Graphics & Intel® HD Graphics (excluding e5 product family, which does not have graphics1)  Ubuntu 16.04.3 LTS (64 bit)  Windows 10 (64 bit)  CentOS 7.4 (64 bit) FPGA  Intel® Arria® FPGA 10 GX development kit  Intel® Programmable Acceleration Card with Intel® Arria® 10 GX FPGA operating systems  OpenCV* & OpenVX* functions must be run against the CPU or Intel® Processor Graphics (GPU)  Ubuntu 16.04.3 LTS (64 bit)  CentOS 7.4 (64 bit) VPU  Intel Movidius™ Neural Compute Stick  Ubuntu 16.04.3 LTS (64 bit)  CentOS 7.4 (64 bit)  Windows 10 (64 bit) Development Platforms 6 th -8 th generation Intel® Core™ and Intel® Xeon® processors  Ubuntu* 16.04.3 LTS (64 bit)  Windows® 10 (64 bit)  CentOS* 7.4 (64 bit) Additional Software Requirements Linux* build environment required components  OpenCV 3.4 or higher  GNU Compiler Collection (GCC) 3.4 or higher  CMake* 2.8 or higher  Python* 3.4 or higher Microsoft Windows* build environment required components  Intel® HD Graphics Driver (latest version)†  OpenCV 3.4 or higher  Intel® C++ Compiler 2017 Update 4  CMake 2.8 or higher  Python 3.4 or higher  Microsoft Visual Studio* 2015 External Dependencies/Additional Software View Product Site, detailed System Requirements 14 1Graphics drivers are required only if you use Intel® Processor Graphics (GPU).
Internet of Things Group 15 Traditional Computer Vision
Internet of Things Group 16 Typical Deep Learning Video Workload
Internet of Things Group 17 Starting with OpenCV* & OpenVX*  Well established, open source, computer vision library  Wide variety of algorithms and functions available  Includes Intel® Photography Vision Library OpenVX and the OpenVX logo are trademarks of the Khronos Group Inc.  Intel optimized functions for faster performance on Intel hardware  Basic building blocks to speed performance, cut development time & allow customization  All-in-one package  Targeted at real time, low power applications  Graph based representation, optimization & execution  11 samples included
Internet of Things Group 18 OpenCV* Samples • PeopleDetect : demonstrates the use of the HoG descriptor. • Background foreground segmentation : demonstrates background segmentation. • Dense Optical Flow : demonstrates using of dense optical flow algorithms. • PVL Face Detection and Recognition : demonstrate Intel PVA face detection and recognition algorithms. • Colorization : demonstrates recoloring grayscale images with DNN. • Opencl Custom Kernel : demonstrates running custom OpenCL kernels by means of OpenCV T-API interface.
Internet of Things Group 19 OpenVX™ Samples Gstreamer Interoperability Video Stabilization Hetero Basic Camera Tampering Census Transform Custom OpenCL™ Kernel Face Detection Auto- Contrast Lane Detection Motion Detection Color Copy Pipeline
Internet of Things Group 20 Deep Learning Computer Vision
21 Caffe* Tensor Flow* MxNet* .dataIR IR IR = Intermediate Representation format Convert & optimize to fit all targets Load, infer CPU Plugin GPU Plugin FPGA Plugin Myriad Plugin Model Optimizer Convert & Optimize Extendibility C++ Extendibility OpenCL™ Extendibility OpenCL/TBD Extendibility TBD Model Optimizer  What it is: Preparation step -> imports trained models  Why important: Optimizes for performance/space with conservative topology transformations; biggest boost is from conversion to data types matching hardware. Inference Engine  What it is: High-level inference API  Why important: Interface is implemented as dynamically loaded plugins for each hardware type. Delivers best performance for each type without requiring users to implement and maintain multiple code pathways. Trained Model Inference Engine Common API (C++) Optimized cross- platform inference OpenCL and the OpenCL logo are trademarks of Apple Inc. used by permission by Khronos GPU = Intel CPU with integrated graphics processing unit/Intel® Processor Graphics Intel®DeepLearningDeploymentToolkit(DLDT)Take Full Advantage of the Power of Intel® Architecture for Deep Learning All products, computer systems, dates, and figures are preliminary based on current expectations, and are subject to change without notice.
Internet of Things Group 22 Deep Learning Inference Workflow MKL-DNN cl-DNN CPU: Xeon/Core/Atom GPU FPGA VPU DLA NYRIAD 1 Train 2 Prepare model 3 Inference
Internet of Things Group 23 Public Models Image Classification Object Detection Semantic Segmentation Neural Style Transfer 1 Train 2 Prepare model 3 Inference
Intel Confidential 24 Supported Tensorflow* Models Converting Your TensorFlow* Model https://software.intel.com/en-us/articles/OpenVINO-Using-TensorFlow Model Name Slim Model Classification Inception v1 inception_v1_2016_08_28.tar.gz Inception v2 inception_v1_2016_08_28.tar.gz Inception v3 inception_v3_2016_08_28.tar.gz Inception v4 inception_v4_2016_09_09.tar.gz Inception ResNet v2 inception_resnet_v2_2016_08_30.tar.gz MobileNet v1 128 mobilenet_v1_0.25_128.tgz MobileNet v1 160 mobilenet_v1_0.5_160.tgz MobileNet v1 224 mobilenet_v1_1.0_224.tgz MobileNet v2 1.4 224 mobilenet_v2_1.4_224.tgz MobileNet v2 1.0 224 mobilenet_v2_1.0_224.tgz NasNet Large nasnet-a_large_04_10_2017.tar.gz NasNet Mobile nasnet-a_mobile_04_10_2017.tar.gz ResidualNet-50 v1 resnet_v1_50_2016_08_28.tar.gz ResidualNet-50 v2 resnet_v2_50_2017_04_14.tar.gz ResidualNet-101 v1 resnet_v1_101_2016_08_28.tar.gz ResidualNet-101 v2 resnet_v2_101_2017_04_14.tar.gz ResidualNet-152 v1 resnet_v1_152_2016_08_28.tar.gz ResidualNet-152 v2 resnet_v2_152_2017_04_14.tar.gz VGG-16 vgg_16_2016_08_28.tar.gz VGG-19 vgg_19_2016_08_28.tar.gz Model Name TensorFlow Object Detection API Models (Frozen) SSD Mobilenet V1 ssd_mobilenet_v1_coco_2018_01_28.tar.gz SSD MobileNet V1 0.75 Depth ssd_mobilenet_v1_0.75_depth_300x300_coco14_sync_2018_ 07_03.tar.gz SSD MobileNet V1 PPN ssd_mobilenet_v1_ppn_shared_box_predictor_300x300_coco 14_sync_2018_07_03.tar.gz SSD MobileNet V1 FPN ssd_mobilenet_v1_fpn_shared_box_predictor_640x640_coco1 4_sync_2018_07_03.tar.gz SSD ResNet50 FPN ssd_resnet50_v1_fpn_shared_box_predictor_640x640_coco14 _sync_2018_07_03.tar.gz SSD MobileNet V2 ssd_mobilenet_v2_coco_2018_03_29.tar.gz SSD Inception V2 ssd_inception_v2_coco_2018_01_28.tar.gz Faster R-CNN Inception V2 faster_rcnn_inception_v2_coco_2018_01_28.tar.gz Faster R-CNN ResNet 50 faster_rcnn_resnet50_coco_2018_01_28.tar.gz Faster R-CNN ResNet 50 Low Proposals faster_rcnn_resnet50_lowproposals_coco_2018_01_28.tar.gz Faster R-CNN ResNet 101 faster_rcnn_resnet101_coco_2018_01_28.tar.gz Faster R-CNN ResNet 101 Low Proposals faster_rcnn_resnet101_lowproposals_coco_2018_01_28.tar.gz Faster R-CNN Inception ResNet V2 faster_rcnn_inception_resnet_v2_atrous_coco_2018_01_28.tar. gz Faster R-CNN Inception ResNet V2 Low Proposals faster_rcnn_inception_resnet_v2_atrous_lowproposals_coco_2 018_01_28.tar.gz Faster R-CNN NasNet faster_rcnn_nas_coco_2018_01_28.tar.gz Faster R-CNN NasNet Low Proposals faster_rcnn_nas_lowproposals_coco_2018_01_28.tar.gz Mask R-CNN Inception ResNet V2 mask_rcnn_inception_resnet_v2_atrous_coco_2018_01_28.tar. gz Mask R-CNN Inception V2 mask_rcnn_inception_v2_coco_2018_01_28.tar.gz Mask R-CNN ResNet 101 mask_rcnn_resnet101_atrous_coco_2018_01_28.tar.gz Mask R-CNN ResNet 50 mask_rcnn_resnet50_atrous_coco_2018_01_28.tar.gz 1 Train 2 Prepare model 3 Inference
Internet of Things Group 25 Run Model Optimizer python mo.py --input_model alexnet.caffemodel --input data --input_shape [1,3,227,227] --data_type FP32 --log_level DEBUG 1 Train 2 Prepare model 3 Inference alexnet.xml alexnet.bin
Intel ConfidentialIntel Confidential 26 python mo.py --input_model inception_v3_frozen.pb --input_shape [1,299,299,3] -- mean_values [127.5,127.5,127.5] –scale_values [127.5,127.5,127.5] Model Optimizer arguments: Common parameters: - Path to the Input Model: TF_inception_v3/inception_v3_frozen.pb - Path for generated IR: TF_inception_v3/FP32 - IR output name: inception_v3_frozen - 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: [1,299,299,3] - Mean values: [127.5,127.5,127.5] - Scale values: [127.5,127.5,127.5] - 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 TensorFlow specific parameters: - Input model in text protobuf format: False - Offload unsupported operations: False - Path to model dump for TensorBoard: None - Update the configuration file with input/output node names: None - Operations to offload: None - Patterns to offload: None - Use the config file: None Model Optimizer version: 1.2.110.59f62983 [ SUCCESS ] Generated IR model. [ SUCCESS ] XML file: TF_inception_v3/FP32/inception_v3_frozen.xml [ SUCCESS ] BIN file: TF_inception_v3/FP32/inception_v3_frozen.bin [ SUCCESS ] Total execution time: 4.97 seconds. TensorFlow* Inception V3 Model classification_sample.exe -m inception_v3_frozen.xml -i .democar.png [ INFO ] InferenceEngine: API version ............ 1.1 Build .................. 11653 [ INFO ] Parsing input parameters [ INFO ] Loading plugin API version ............ 1.1 Build .................. win_20180511 Description ....... MKLDNNPlugin [ INFO ] Loading network files: inception_v3_frozen.xml inception_v3_frozen.bin [ INFO ] Preparing input blobs [ WARNING ] Image is resized from (787, 259) to (224, 224) [ INFO ] Batch size is 1 [ INFO ] Preparing output blobs [ INFO ] Loading model to the plugin [ INFO ] Starting inference (1 iterations) [ INFO ] Average running time of one iteration: 91.8117 ms [ INFO ] Processing output blobs Top 10 results: Image democar.png 752 0.2250938 label racer, race car, racing car 818 0.1749035 label sports car, sport car 512 0.1381157 label convertible 628 0.0980638 label limousine, limo 437 0.0751940 label beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon 480 0.0682043 label car wheel 582 0.0508757 label grille, radiator grille 706 0.0157829 label passenger car, coach, carriage 469 0.0073989 label cab, hack, taxi, taxicab 865 0.0056826 label tow truck, tow car, wrecker [ INFO ] Execution successful 1 Train 2 Prepare model 3 Inference
Internet of Things Group 27 Pretrained Models 1 Train 2 Prepare model 3 Inference
Internet of Things Group 28 person-detection-retail-0001 This model is for a pedestrian detector used for Retail scenarios. The model is based on a backbone with hyper-feature + R-FCN. Metric Value AP 80.91% Pose coverage Standing upright, parallel to the image plane Support of occluded pedestrians YES Occlusion coverage <50% Min pedestrian height 80 pixels (on 1080p) Max objects to detect 200 GFlops 12.584487 MParams 3.243982 Source framework Caffe* Caffe* CPU Intel® MKL Inference Engine Intel® MKL-DNN Inference Engine clDNN FP16 Inference Engine clDNN FP32 OpenCV* CPU 3.80 17.45 15.25 10.29 9.27 Average Precision (AP) is defined as an area under the precision/recall curve. The validation dataset consists of about 50,000 images from about 100 scenes. Performance (FPS) and System Configuration Ubuntu* 16.04 Intel® Core™ i5-6500 CPU @ 2.90GHz fixed GPU GT2 @ 1.00GHz fixed DDR4 PC17000/2133MHz 1 Train 2 Prepare model 3 Inference
Internet of Things Group 29 IR Read network description + weights Load Network Create Engine Instance w/ HW plugin load input blobs + infer CNNNetworkReader network_reader; network_reader.ReadNetwork(“Model.xml”); Network_reader.ReadWeights(“Model.bin") InferenceEngine::InferenceEnginePluginPtr engine_ptr = InferenceEngine::PluginDispatcher(pluginDirs).getSuitablePlugin(TargetDevice::eGPU); InferencePlugin plugin(engine_ptr); auto network = network_reader.getNetwork(); auto executable_network = plugin.LoadNetwork(network, {}); auto infer_request = executable_network.CreateInferRequest(); for (auto & item : inputInfo) { auto input_name = item->first; auto input = infer_request.GetBlob(input_name); } infer_request->infer(); UsingtheInferenceEngineAPI 1 Train 2 Prepare model 3 Inference
Internet of Things Group 30 Deep Learning Object Classification Output is an array of category possibilities 1 Train 2 Prepare model 3 Inference 928-0.543935-ice cream, icecream // Read Network, Create Engine and Load Netowrk Mat frame,frame2; for (;;) { cap >> frame; //OpenCV video capture //resize to expected size (in IR .xml) resize(frame,frame2,Size(227,227)); //run inference long unsigned framesize= frame2.rows*frame2.step1(); ConvertImageToInput(frame2.data, framesize, *input); sts = _plugin->Infer(*input, *output, &dsc); //get top classifier label int blobsize=output->size(); float *data=output->data(); float max=0; int maxidx=0; for (int i1=0; i1<blobsize; i1++) { if (data[i1]>max) { max=data[i1]; maxidx=i1; } } // do something with classification data imshow( "frame", frame2 ); if (waitKey(30) >= 0) break; }
Internet of Things Group 31 Run Inference 1: Model vehicle-license-plate- detection-barrier-0007 Detects Vehicles Run Inference 2: Model vehicle-attributes- recognition-barrier-0010 Classifies vehicle attributes Run Inference 3: Model license-plate-recognition- barrier-0001 Detects License Plates Load Input Image(s) Display Results Asynchronous and Heterogeneous 1 Train 2 Prepare model 3 Inference
Internet of Things Group 32 OpenVINO Documentation
33 OpenVINO Reference Documents IE document (local) <openvino>/deployment_tools/documentation/index.html OpenVINO online documents https://software.intel.com/en-us/openvino-toolkit/documentation/featured OpenVINO Get Started Guide https://software.intel.com/en-us/openvino-toolkit/documentation/get-started
34 Model / Layer Support information • Open IE document (local) • Go to “Converting Your XXXX Model”
35 Inference Engine API Reference Manual Open IE document (local) Enter keywords to search box and incremental search result will be displayed
Copyright © 2018, Intel Corporation. All rights reserved. *Other names and brands may be claimed as the property of others. Optimization NoticeOptimization Notice 36 Benefits of the OpenVINO™ toolkit Reduce time using a library of optimized OpenCV* & OpenVX* functions, & 15+ samples. Develop once, deploy for current & future Intel-based devices. Use the increasing repository of OpenCL™ starting points in OpenCV* to add your own unique code. Access Intel computer vision accelerators. Speed code performance. Supports heterogeneous processing & asynchronous execution. AcceleratePerformance OpenVX and the OpenVX logo are trademarks of the Khronos Group Inc. OpenCL and the OpenCL logo are trademarks of Apple Inc. used by permission by Khronos IntegrateDeeplearning Unleash convolutional neural network (CNN) based deep learning inference Up to 19.9x increase1 1Performance increase comparing certain standard framework models vs. Intel-optimized models in the Intel® Deep Learning Deployment Toolkit. Performance results are based on testing as of June 13, 2018 and may not reflect all publicly available security updates. See configuration disclosure for details. No product can be absolutely secure. For more complete information about performance and benchmark results, visit www.intel.com/benchmarks. Testing by Intel as of June 13, 2018. See Benchmark slide 14 for configuration details. Harness the Power of Intel® Processors: CPU, CPU with Integrated Graphics, FPGA,VPU speeddevelopment Innovate&customize using a common API, pre-trained models & computer vision algorithms.
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【視覺進化論】AI智慧視覺運算技術論壇_2_ChungYeh

  • 2. Internet of Things Group 2 Legalnotices&disclaimersThis document contains information on products, services and/or processes in development. All information provided here is subject to change without notice. Contact your Intel representative to obtain the latest forecast, schedule, specifications and roadmaps. Intel technologies’ features and benefits depend on system configuration and may require enabled hardware, software or service activation. Learn more at intel.com, or from the OEM or retailer. No computer system can be absolutely secure. Tests document performance of components on a particular test, in specific systems. Differences in hardware, software, or configuration will affect actual performance. Consult other sources of information to evaluate performance as you consider your purchase. For more complete information about performance and benchmark results, visit http://www.intel.com/performance. Cost reduction scenarios described are intended as examples of how a given Intel-based product, in the specified circumstances and configurations, may affect future costs and provide cost savings. Circumstances will vary. Intel does not guarantee any costs or cost reduction. Statements in this document that refer to Intel’s plans and expectations for the quarter, the year, and the future, are forward-looking statements that involve a number of risks and uncertainties. A detailed discussion of the factors that could affect Intel’s results and plans is included in Intel’s SEC filings, including the annual report on Form 10-K. The products described may contain design defects or errors known as errata which may cause the product to deviate from published specifications. Current characterized errata are available on request. Performance estimates were obtained prior to implementation of recent software patches and firmware updates intended to address exploits referred to as "Spectre" and "Meltdown." Implementation of these updates may make these results inapplicable to your device or system. No license (express or implied, by estoppel or otherwise) to any intellectual property rights is granted by this document. Intel does not control or audit third-party benchmark data or the web sites referenced in this document. You should visit the referenced web site and confirm whether referenced data are accurate. Results have been estimated or simulated using internal Intel analysis or architecture simulation or modeling, and provided to you for informational purposes. Any differences in your system hardware, software or configuration may affect your actual performance. Intel, the Intel logo, Pentium, Celeron, Atom, Core, Xeon, Movidius, Saffron, OpenVINO, MediaSDK and others are trademarks of Intel Corporation in the U.S. and/or other countries. *Other names and brands may be claimed as the property of others. © 2018 Intel Corporation.
  • 3. Internet of Things Group 3 https://software.intel.com/en-us/openvino-toolkit
  • 4. 4 Artificial Intelligence is the ability of machines to learn from experience, without explicit programming, in order to perform cognitive functions associated with the human mind ArtificialIntelligence Machinelearning Algorithms whose performance improve as they are exposed to more data over time Deeplearning Subset of machine learning in which multi-layered neural networks learn from vast amounts of data
  • 5. 5 Lots of labeled data! Training Inference Forward Backward Model weights Forward “Bicycle”? “Strawberry” “Bicycle”? Error Human Bicycle Strawberry ?????? DeeplearningBasics Data set size Accuracy Didyouknow? Training with a large data set AND deep (many layered) neural network often leads to the highest accuracy inference
  • 7. 7 OS Support CentOS* 7.4 (64 bit) Ubuntu* 16.04.3 LTS (64 bit) Microsoft Windows* 10 (64 bit) Yocto Project* version Poky Jethro v2.0.3 (64 bit) OpenVX and the OpenVX logo are trademarks of the Khronos Group Inc. OpenCL and the OpenCL logo are trademarks of Apple Inc. used by permission by Khronos Intel® Architecture-Based Platforms Support Intel® Deep Learning Deployment Toolkit Traditional Computer Vision Tools & Libraries Model Optimizer Convert & Optimize Inference Engine Optimized InferenceIR OpenCV* OpenVX* Photography Vision Optimized Libraries IR = Intermediate Representation file For Intel® CPU & CPU with integrated graphics Increase Media/Video/Graphics Performance Intel® Media SDK Open Source version OpenCL™ Drivers & Runtimes For CPU with integrated graphics Optimize Intel® FPGA FPGA RunTime Environment (from Intel® FPGA SDK for OpenCL™) Bitstreams FPGA – Linux* only Code Samples & 10 Pre-trained Models Code Samples Openvino™toolkitCross-Platform Tool to Accelerate Computer Vision & Deep Learning Inference Performance software.intel.com/openvino-toolkit All products, computer systems, dates, and figures are preliminary based on current expectations, and are subject to change without notice.
  • 8. Internet of Things Group 8 Discover Intel® OpenVINO™ Toolkit Capabilities
  • 9. Internet of Things Group 9 Hardware Acceleration
  • 10. Internet of Things Group 10 Intel Architecture Multicore/Multithreading/Vectorization https://software.intel.com/en-us/articles/introduction-to-intel-advanced-vector-extensions
  • 11. Internet of Things Group 11 Intel Integrated Graphics
  • 12. Internet of Things Group 12 Movidius Neural Compute Stick (Myriad 2)
  • 13. Internet of Things Group 13 Intel® FPGA Intel Arria 10 GX FPGA • High-performance, multi-gigabit SERDES transceivers up to 15 Gbps • 1,150K logic elements available (-2L speed grade) • 53 Mb of embedded memory On-Board Memory • 8 GB DDR4 memory banks with error correction code (ECC) (2 banks) • 1 Gb Mb (128 MB) flash Interfaces • PCIe x8 Gen3 electrical, x16 mechanical • USB 2.0 interface for debug and programming of FPGA and flash memory • 1X QSFP+ with 4X 10GbE or 40GbE support • Standard height, 1/2 length • Low-profile option upon request
  • 14. Copyright © 2018, Intel Corporation. All rights reserved. *Other names and brands may be claimed as the property of others. Optimization NoticeOptimization Notice OpenVINO™ toolkit Technical Specifications Intel® Platforms Compatible Operating Systems Target Solution Platforms CPU  6th-8th generation Intel® Xeon® & Core™ processors  Ubuntu* 16.04.3 LTS (64 bit)  Microsoft Windows* 10 (64 bit)  CentOS* 7.4 (64 bit)  Intel® Pentium® processor N4200/5, N3350/5, N3450/5 with Intel® HD Graphics  Yocto Project* Poky Jethro v2.0.3 (64 bit) Iris® Pro & Intel® HD Graphics  6th-8th generation Intel® Core™ processor with Intel® Iris™ Pro graphics & Intel® HD Graphics  6th-8th generation Intel® Xeon® processor with Intel® Iris™ Pro Graphics & Intel® HD Graphics (excluding e5 product family, which does not have graphics1)  Ubuntu 16.04.3 LTS (64 bit)  Windows 10 (64 bit)  CentOS 7.4 (64 bit) FPGA  Intel® Arria® FPGA 10 GX development kit  Intel® Programmable Acceleration Card with Intel® Arria® 10 GX FPGA operating systems  OpenCV* & OpenVX* functions must be run against the CPU or Intel® Processor Graphics (GPU)  Ubuntu 16.04.3 LTS (64 bit)  CentOS 7.4 (64 bit) VPU  Intel Movidius™ Neural Compute Stick  Ubuntu 16.04.3 LTS (64 bit)  CentOS 7.4 (64 bit)  Windows 10 (64 bit) Development Platforms 6 th -8 th generation Intel® Core™ and Intel® Xeon® processors  Ubuntu* 16.04.3 LTS (64 bit)  Windows® 10 (64 bit)  CentOS* 7.4 (64 bit) Additional Software Requirements Linux* build environment required components  OpenCV 3.4 or higher  GNU Compiler Collection (GCC) 3.4 or higher  CMake* 2.8 or higher  Python* 3.4 or higher Microsoft Windows* build environment required components  Intel® HD Graphics Driver (latest version)†  OpenCV 3.4 or higher  Intel® C++ Compiler 2017 Update 4  CMake 2.8 or higher  Python 3.4 or higher  Microsoft Visual Studio* 2015 External Dependencies/Additional Software View Product Site, detailed System Requirements 14 1Graphics drivers are required only if you use Intel® Processor Graphics (GPU).
  • 15. Internet of Things Group 15 Traditional Computer Vision
  • 16. Internet of Things Group 16 Typical Deep Learning Video Workload
  • 17. Internet of Things Group 17 Starting with OpenCV* & OpenVX*  Well established, open source, computer vision library  Wide variety of algorithms and functions available  Includes Intel® Photography Vision Library OpenVX and the OpenVX logo are trademarks of the Khronos Group Inc.  Intel optimized functions for faster performance on Intel hardware  Basic building blocks to speed performance, cut development time & allow customization  All-in-one package  Targeted at real time, low power applications  Graph based representation, optimization & execution  11 samples included
  • 18. Internet of Things Group 18 OpenCV* Samples • PeopleDetect : demonstrates the use of the HoG descriptor. • Background foreground segmentation : demonstrates background segmentation. • Dense Optical Flow : demonstrates using of dense optical flow algorithms. • PVL Face Detection and Recognition : demonstrate Intel PVA face detection and recognition algorithms. • Colorization : demonstrates recoloring grayscale images with DNN. • Opencl Custom Kernel : demonstrates running custom OpenCL kernels by means of OpenCV T-API interface.
  • 19. Internet of Things Group 19 OpenVX™ Samples Gstreamer Interoperability Video Stabilization Hetero Basic Camera Tampering Census Transform Custom OpenCL™ Kernel Face Detection Auto- Contrast Lane Detection Motion Detection Color Copy Pipeline
  • 20. Internet of Things Group 20 Deep Learning Computer Vision
  • 21. 21 Caffe* Tensor Flow* MxNet* .dataIR IR IR = Intermediate Representation format Convert & optimize to fit all targets Load, infer CPU Plugin GPU Plugin FPGA Plugin Myriad Plugin Model Optimizer Convert & Optimize Extendibility C++ Extendibility OpenCL™ Extendibility OpenCL/TBD Extendibility TBD Model Optimizer  What it is: Preparation step -> imports trained models  Why important: Optimizes for performance/space with conservative topology transformations; biggest boost is from conversion to data types matching hardware. Inference Engine  What it is: High-level inference API  Why important: Interface is implemented as dynamically loaded plugins for each hardware type. Delivers best performance for each type without requiring users to implement and maintain multiple code pathways. Trained Model Inference Engine Common API (C++) Optimized cross- platform inference OpenCL and the OpenCL logo are trademarks of Apple Inc. used by permission by Khronos GPU = Intel CPU with integrated graphics processing unit/Intel® Processor Graphics Intel®DeepLearningDeploymentToolkit(DLDT)Take Full Advantage of the Power of Intel® Architecture for Deep Learning All products, computer systems, dates, and figures are preliminary based on current expectations, and are subject to change without notice.
  • 22. Internet of Things Group 22 Deep Learning Inference Workflow MKL-DNN cl-DNN CPU: Xeon/Core/Atom GPU FPGA VPU DLA NYRIAD 1 Train 2 Prepare model 3 Inference
  • 23. Internet of Things Group 23 Public Models Image Classification Object Detection Semantic Segmentation Neural Style Transfer 1 Train 2 Prepare model 3 Inference
  • 24. Intel Confidential 24 Supported Tensorflow* Models Converting Your TensorFlow* Model https://software.intel.com/en-us/articles/OpenVINO-Using-TensorFlow Model Name Slim Model Classification Inception v1 inception_v1_2016_08_28.tar.gz Inception v2 inception_v1_2016_08_28.tar.gz Inception v3 inception_v3_2016_08_28.tar.gz Inception v4 inception_v4_2016_09_09.tar.gz Inception ResNet v2 inception_resnet_v2_2016_08_30.tar.gz MobileNet v1 128 mobilenet_v1_0.25_128.tgz MobileNet v1 160 mobilenet_v1_0.5_160.tgz MobileNet v1 224 mobilenet_v1_1.0_224.tgz MobileNet v2 1.4 224 mobilenet_v2_1.4_224.tgz MobileNet v2 1.0 224 mobilenet_v2_1.0_224.tgz NasNet Large nasnet-a_large_04_10_2017.tar.gz NasNet Mobile nasnet-a_mobile_04_10_2017.tar.gz ResidualNet-50 v1 resnet_v1_50_2016_08_28.tar.gz ResidualNet-50 v2 resnet_v2_50_2017_04_14.tar.gz ResidualNet-101 v1 resnet_v1_101_2016_08_28.tar.gz ResidualNet-101 v2 resnet_v2_101_2017_04_14.tar.gz ResidualNet-152 v1 resnet_v1_152_2016_08_28.tar.gz ResidualNet-152 v2 resnet_v2_152_2017_04_14.tar.gz VGG-16 vgg_16_2016_08_28.tar.gz VGG-19 vgg_19_2016_08_28.tar.gz Model Name TensorFlow Object Detection API Models (Frozen) SSD Mobilenet V1 ssd_mobilenet_v1_coco_2018_01_28.tar.gz SSD MobileNet V1 0.75 Depth ssd_mobilenet_v1_0.75_depth_300x300_coco14_sync_2018_ 07_03.tar.gz SSD MobileNet V1 PPN ssd_mobilenet_v1_ppn_shared_box_predictor_300x300_coco 14_sync_2018_07_03.tar.gz SSD MobileNet V1 FPN ssd_mobilenet_v1_fpn_shared_box_predictor_640x640_coco1 4_sync_2018_07_03.tar.gz SSD ResNet50 FPN ssd_resnet50_v1_fpn_shared_box_predictor_640x640_coco14 _sync_2018_07_03.tar.gz SSD MobileNet V2 ssd_mobilenet_v2_coco_2018_03_29.tar.gz SSD Inception V2 ssd_inception_v2_coco_2018_01_28.tar.gz Faster R-CNN Inception V2 faster_rcnn_inception_v2_coco_2018_01_28.tar.gz Faster R-CNN ResNet 50 faster_rcnn_resnet50_coco_2018_01_28.tar.gz Faster R-CNN ResNet 50 Low Proposals faster_rcnn_resnet50_lowproposals_coco_2018_01_28.tar.gz Faster R-CNN ResNet 101 faster_rcnn_resnet101_coco_2018_01_28.tar.gz Faster R-CNN ResNet 101 Low Proposals faster_rcnn_resnet101_lowproposals_coco_2018_01_28.tar.gz Faster R-CNN Inception ResNet V2 faster_rcnn_inception_resnet_v2_atrous_coco_2018_01_28.tar. gz Faster R-CNN Inception ResNet V2 Low Proposals faster_rcnn_inception_resnet_v2_atrous_lowproposals_coco_2 018_01_28.tar.gz Faster R-CNN NasNet faster_rcnn_nas_coco_2018_01_28.tar.gz Faster R-CNN NasNet Low Proposals faster_rcnn_nas_lowproposals_coco_2018_01_28.tar.gz Mask R-CNN Inception ResNet V2 mask_rcnn_inception_resnet_v2_atrous_coco_2018_01_28.tar. gz Mask R-CNN Inception V2 mask_rcnn_inception_v2_coco_2018_01_28.tar.gz Mask R-CNN ResNet 101 mask_rcnn_resnet101_atrous_coco_2018_01_28.tar.gz Mask R-CNN ResNet 50 mask_rcnn_resnet50_atrous_coco_2018_01_28.tar.gz 1 Train 2 Prepare model 3 Inference
  • 25. Internet of Things Group 25 Run Model Optimizer python mo.py --input_model alexnet.caffemodel --input data --input_shape [1,3,227,227] --data_type FP32 --log_level DEBUG 1 Train 2 Prepare model 3 Inference alexnet.xml alexnet.bin
  • 26. Intel ConfidentialIntel Confidential 26 python mo.py --input_model inception_v3_frozen.pb --input_shape [1,299,299,3] -- mean_values [127.5,127.5,127.5] –scale_values [127.5,127.5,127.5] Model Optimizer arguments: Common parameters: - Path to the Input Model: TF_inception_v3/inception_v3_frozen.pb - Path for generated IR: TF_inception_v3/FP32 - IR output name: inception_v3_frozen - 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: [1,299,299,3] - Mean values: [127.5,127.5,127.5] - Scale values: [127.5,127.5,127.5] - 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 TensorFlow specific parameters: - Input model in text protobuf format: False - Offload unsupported operations: False - Path to model dump for TensorBoard: None - Update the configuration file with input/output node names: None - Operations to offload: None - Patterns to offload: None - Use the config file: None Model Optimizer version: 1.2.110.59f62983 [ SUCCESS ] Generated IR model. [ SUCCESS ] XML file: TF_inception_v3/FP32/inception_v3_frozen.xml [ SUCCESS ] BIN file: TF_inception_v3/FP32/inception_v3_frozen.bin [ SUCCESS ] Total execution time: 4.97 seconds. TensorFlow* Inception V3 Model classification_sample.exe -m inception_v3_frozen.xml -i .democar.png [ INFO ] InferenceEngine: API version ............ 1.1 Build .................. 11653 [ INFO ] Parsing input parameters [ INFO ] Loading plugin API version ............ 1.1 Build .................. win_20180511 Description ....... MKLDNNPlugin [ INFO ] Loading network files: inception_v3_frozen.xml inception_v3_frozen.bin [ INFO ] Preparing input blobs [ WARNING ] Image is resized from (787, 259) to (224, 224) [ INFO ] Batch size is 1 [ INFO ] Preparing output blobs [ INFO ] Loading model to the plugin [ INFO ] Starting inference (1 iterations) [ INFO ] Average running time of one iteration: 91.8117 ms [ INFO ] Processing output blobs Top 10 results: Image democar.png 752 0.2250938 label racer, race car, racing car 818 0.1749035 label sports car, sport car 512 0.1381157 label convertible 628 0.0980638 label limousine, limo 437 0.0751940 label beach wagon, station wagon, wagon, estate car, beach waggon, station waggon, waggon 480 0.0682043 label car wheel 582 0.0508757 label grille, radiator grille 706 0.0157829 label passenger car, coach, carriage 469 0.0073989 label cab, hack, taxi, taxicab 865 0.0056826 label tow truck, tow car, wrecker [ INFO ] Execution successful 1 Train 2 Prepare model 3 Inference
  • 27. Internet of Things Group 27 Pretrained Models 1 Train 2 Prepare model 3 Inference
  • 28. Internet of Things Group 28 person-detection-retail-0001 This model is for a pedestrian detector used for Retail scenarios. The model is based on a backbone with hyper-feature + R-FCN. Metric Value AP 80.91% Pose coverage Standing upright, parallel to the image plane Support of occluded pedestrians YES Occlusion coverage <50% Min pedestrian height 80 pixels (on 1080p) Max objects to detect 200 GFlops 12.584487 MParams 3.243982 Source framework Caffe* Caffe* CPU Intel® MKL Inference Engine Intel® MKL-DNN Inference Engine clDNN FP16 Inference Engine clDNN FP32 OpenCV* CPU 3.80 17.45 15.25 10.29 9.27 Average Precision (AP) is defined as an area under the precision/recall curve. The validation dataset consists of about 50,000 images from about 100 scenes. Performance (FPS) and System Configuration Ubuntu* 16.04 Intel® Core™ i5-6500 CPU @ 2.90GHz fixed GPU GT2 @ 1.00GHz fixed DDR4 PC17000/2133MHz 1 Train 2 Prepare model 3 Inference
  • 29. Internet of Things Group 29 IR Read network description + weights Load Network Create Engine Instance w/ HW plugin load input blobs + infer CNNNetworkReader network_reader; network_reader.ReadNetwork(“Model.xml”); Network_reader.ReadWeights(“Model.bin") InferenceEngine::InferenceEnginePluginPtr engine_ptr = InferenceEngine::PluginDispatcher(pluginDirs).getSuitablePlugin(TargetDevice::eGPU); InferencePlugin plugin(engine_ptr); auto network = network_reader.getNetwork(); auto executable_network = plugin.LoadNetwork(network, {}); auto infer_request = executable_network.CreateInferRequest(); for (auto & item : inputInfo) { auto input_name = item->first; auto input = infer_request.GetBlob(input_name); } infer_request->infer(); UsingtheInferenceEngineAPI 1 Train 2 Prepare model 3 Inference
  • 30. Internet of Things Group 30 Deep Learning Object Classification Output is an array of category possibilities 1 Train 2 Prepare model 3 Inference 928-0.543935-ice cream, icecream // Read Network, Create Engine and Load Netowrk Mat frame,frame2; for (;;) { cap >> frame; //OpenCV video capture //resize to expected size (in IR .xml) resize(frame,frame2,Size(227,227)); //run inference long unsigned framesize= frame2.rows*frame2.step1(); ConvertImageToInput(frame2.data, framesize, *input); sts = _plugin->Infer(*input, *output, &dsc); //get top classifier label int blobsize=output->size(); float *data=output->data(); float max=0; int maxidx=0; for (int i1=0; i1<blobsize; i1++) { if (data[i1]>max) { max=data[i1]; maxidx=i1; } } // do something with classification data imshow( "frame", frame2 ); if (waitKey(30) >= 0) break; }
  • 31. Internet of Things Group 31 Run Inference 1: Model vehicle-license-plate- detection-barrier-0007 Detects Vehicles Run Inference 2: Model vehicle-attributes- recognition-barrier-0010 Classifies vehicle attributes Run Inference 3: Model license-plate-recognition- barrier-0001 Detects License Plates Load Input Image(s) Display Results Asynchronous and Heterogeneous 1 Train 2 Prepare model 3 Inference
  • 32. Internet of Things Group 32 OpenVINO Documentation
  • 33. 33 OpenVINO Reference Documents IE document (local) <openvino>/deployment_tools/documentation/index.html OpenVINO online documents https://software.intel.com/en-us/openvino-toolkit/documentation/featured OpenVINO Get Started Guide https://software.intel.com/en-us/openvino-toolkit/documentation/get-started
  • 34. 34 Model / Layer Support information • Open IE document (local) • Go to “Converting Your XXXX Model”
  • 35. 35 Inference Engine API Reference Manual Open IE document (local) Enter keywords to search box and incremental search result will be displayed
  • 36. Copyright © 2018, Intel Corporation. All rights reserved. *Other names and brands may be claimed as the property of others. Optimization NoticeOptimization Notice 36 Benefits of the OpenVINO™ toolkit Reduce time using a library of optimized OpenCV* & OpenVX* functions, & 15+ samples. Develop once, deploy for current & future Intel-based devices. Use the increasing repository of OpenCL™ starting points in OpenCV* to add your own unique code. Access Intel computer vision accelerators. Speed code performance. Supports heterogeneous processing & asynchronous execution. AcceleratePerformance OpenVX and the OpenVX logo are trademarks of the Khronos Group Inc. OpenCL and the OpenCL logo are trademarks of Apple Inc. used by permission by Khronos IntegrateDeeplearning Unleash convolutional neural network (CNN) based deep learning inference Up to 19.9x increase1 1Performance increase comparing certain standard framework models vs. Intel-optimized models in the Intel® Deep Learning Deployment Toolkit. Performance results are based on testing as of June 13, 2018 and may not reflect all publicly available security updates. See configuration disclosure for details. No product can be absolutely secure. For more complete information about performance and benchmark results, visit www.intel.com/benchmarks. Testing by Intel as of June 13, 2018. See Benchmark slide 14 for configuration details. Harness the Power of Intel® Processors: CPU, CPU with Integrated Graphics, FPGA,VPU speeddevelopment Innovate&customize using a common API, pre-trained models & computer vision algorithms.
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