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Computer Vision, Deep Learning, Artificial superintelligence (ASI) - WorkshopsSearch this siteSkip to main contentSkip to navigationComputer Vision, Deep Learning, Artificial superintelligence (ASI)HomeProductCoursesMachine Learning SpecializationMachine Learning Foundations: A Case Study ApproachFSDLFull Stack Deep LearningMLOpsROSParallel Programming Modern CPPCloud-NativeIoT Scholarship FoundationTensorFlow: Data and Deployment Specialization Workshops and EventsRISC-VEdge-AI-summitEmbedded IoTTeslaAI-HardwareOpenVINO Deep LearningMetaverseWorkshopsIFA2022Book Summary commonplace bookknowledgemanagementPKMTopics and ProjectsAIHubChatGPTHow to startYouTubeYouTube IISoftwareRoadmap for Image Processing Source CodeOpenCVCPPpythonMacOSOpenCVRustcompileIoTShareVideo TrackingCameraCalibrationDRLHardwareQuantum ComputingAltCoinResumeCVApplestartupLinksamazonAboutFQApromptComputer Vision, Deep Learning, Artificial superintelligence (ASI)workshopssteps parts detection resize with keep aspects ratio ML perform detection semantic segmentation transfer to original coordinateschallenges class imbalance class definition use class in between inconstant annotationscolor augmentation RGB shift random brightness and contrast shapen hue, saturation, valueWhy manually data augmentation?because we want to control data augmentation. for example change rotation angle to just a few or change color only in one rangePhotogrammetry 3D modelsNeural Radiance Fields (NERF), and Instant-NGP Future of photogrammetry?NeRF in the Wild: Neural Radiance Fields for UnconstrainedGoogle SitesReport abusePage detailsPage updated Google SitesReport abuseThis site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies.Learn moreGot it

Computer Vision, Deep Learning, Artificial superintelligence (ASI) - YouTube IISearch this siteSkip to main contentSkip to navigationComputer Vision, Deep Learning, Artificial superintelligence (ASI)HomeProductCoursesMachine Learning SpecializationMachine Learning Foundations: A Case Study ApproachFSDLFull Stack Deep LearningMLOpsROSParallel Programming Modern CPPCloud-NativeIoT Scholarship FoundationTensorFlow: Data and Deployment Specialization Workshops and EventsRISC-VEdge-AI-summitEmbedded IoTTeslaAI-HardwareOpenVINO Deep LearningMetaverseWorkshopsIFA2022Book Summary commonplace bookknowledgemanagementPKMTopics and ProjectsAIHubChatGPTHow to startYouTubeYouTube IISoftwareRoadmap for Image Processing Source CodeOpenCVCPPpythonMacOSOpenCVRustcompileIoTShareVideo TrackingCameraCalibrationDRLHardwareQuantum ComputingAltCoinResumeCVApplestartupLinksamazonAboutFQApromptComputer Vision, Deep Learning, Artificial superintelligence (ASI)YouTube IIOpenCV 4Deep Learning by OpenCV 4 (2018) - part 1 - compile OpenCV for Deep Learning( )Setup Visual Studio 2017 (C) for OpenCV 4, Deep( )Build OpenCV 4 with Visual Studio 2017 (C) OpenCV( )OpenCV 3(2018) How to build OpenCV 3.4 , Visual Studio ( )OpenCV (All Versions of 3.x),Easy Installation Guide and Sample Project (VS 2015 C), tutorial 1( )compile OpenCV 3.2 windows 10 (64 bit) visual studio( )Deep Learning frameworks and environmentsDeep Learning by OpenCV 4 (2018) - part 1 - compile( )0- Using Deep Learning for Computer Vision Applications ( )1- 2018 - How to compile OpenCV 3.4 , VisualStudio( )2- 2018- How to setup visual studio project ( )3 - OpenCV 4 , Deep Learning for Computer Vision( )4-opencv4 Using TensorFlow model in OpenCV 4( )2018 NVidia Caffe Ubuntu 16 CUDA 9 GTX1080 cudnn d( )2018 NVidia DIGITS Ubuntu16 CUDA9 GTX1080 with Caf( )2018 -torch ubuntu 16 cuda 9 cudd GTX 1080( )2018- TensorFlow installation ubuntu gtx 1080 cuda( )Compile OpenCV 3.2 with Visual Studio 2017 (C) ( )TensorFlow in OpenCV 3.2 Visual Studio 2017 (C ( )Compile Caffe v1.0 on Ubuntu 16 (2017) Deep Learning( )build Torch in Ubuntu 16, Deep Learning for computer visio( )How to install DIGITS 6.0 based on TensorFlow 1.3( )DIGITS 6.0 TensorFlow 1.3 Ubuntu 16 Deep Learning( )Traditional Computer Vision and Image Processing algorithmsCamera Calibration camera resectioning (image processing with opencv 3 & c , computer vision)( )optical flow implementation (all methods and algorithms) on opencv 3 visual studio 2015 win 64x( )Pedestrian Detection MFC visual C Opencv 3 human detection webcam,video,motion,frame,edge,vector( )video processing by opencv 3.1 vc 2015 win 64x( )Implementation of image pyramid in OpenCV (3.x) and Visual Studio 2015( )part 2 image pyramid opencv 3 visual C 2015 64 bit gaussina pyramid laplacian pyramid optical flow( )opencv 310 vs 2015 facedetection( )opencv 3.1 VS 2015 thresholding algorithm( )2018 video contents search based on deep learning( )Deep Learning in Computer Vision Applications tutorial how to apply neural style transfer to images using OpenCV 4, C, and deep learning (torch)( )Deep Learning on MacOS (MacBook) with TensorFlow ( )using trained caffe model in opencv application( ) SitesReport abusePage detailsPage updated Google SitesReport abuseThis site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies.Learn moreGot it

Computer Vision, Deep Learning, Artificial superintelligence (ASI)Search this siteSkip to main contentSkip to navigationComputer Vision, Deep Learning, Artificial superintelligence (ASI)HomeProductCoursesMachine Learning SpecializationMachine Learning Foundations: A Case Study ApproachFSDLFull Stack Deep LearningMLOpsROSParallel Programming Modern CPPCloud-NativeIoT Scholarship FoundationTensorFlow: Data and Deployment Specialization Workshops and EventsRISC-VEdge-AI-summitEmbedded IoTTeslaAI-HardwareOpenVINO Deep LearningMetaverseWorkshopsIFA2022Book Summary commonplace bookknowledgemanagementPKMTopics and ProjectsAIHubChatGPTHow to startYouTubeYouTube IISoftwareRoadmap for Image Processing Source CodeOpenCVCPPpythonMacOSOpenCVRustcompileIoTShareVideo TrackingCameraCalibrationDRLHardwareQuantum ComputingAltCoinResumeCVApplestartupLinksamazonAboutFQApromptComputer Vision, Deep Learning, Artificial superintelligence (ASI)TiziranImage Processing Artificial SuperIntelligence (ASI)Artificial General Intelligence (AGI)Medical Image ProcessingRoboticAR, VR, extended reality 3D SLAM Computer Vision in IoT Machine LearningPerformance engineering in deep learning applications End-to-End pipeline for machine learning programs Reduce cost and development time with Amazon Efficient Deep Learning Pipelines for Accurate Cost Estimations Over Large Scale Query Workload.Continuous Deployment of Machine Learning PipelinesWe deliver end-to-end hyper-automation solutions using computer vision & deep learning to enable AI-Powered Enterpriseorchestration of various technologies and workflows to streamline and execute a process automatically.Data labeling service remove or on site in Berlin, GermanySite Map Open Source ProjectsOpenCV NuGet NuGet packages for OpenCV 5 - Static Library for Visual Studio 2019 and 2022 and setup your OpenCV project in just 5 minutesConfig your visual studio project for computer vision applicationstatic OpenCV library for visual studio 2022 by using NuGet package manager just in a few minutes C, Computer Vision, Image Processing,download source code (GitHub): NuGet packages comprised of two versions for different VS versions.Visual Studio 2019 OpenCV5StaticLibVS2019NuGet -Version 2022.7.7Visual Studio 2022 OpenCV5StaticLibVS22NuGet -Version 2022.7.7more: Computer Vision Test: Unit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep Learning Do you want to test your output of computer vision application which is video or images?Standard test for computer vision applicationThere isn’t any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware.Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? Multi-Class Multi-object Video Trackingcomputer vision with deep learning in IoT devicesMulti Camera (Stereo Vision) Calibration for ARVR headset (extended realitymixed reality) 3D Image Processing with Deep LearningEnd to End solution for computer vision applications in industry (cloud and IoT)Download all mind map sources LinkedIn: (around 12K members)Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch The Computer Vision LinkedIn group: reached to around 8000 members. This group is a wonderful place for support if you have a question, need inspiration, encouragement, and cutting edge research. Computer Vision, Deep Learning, extended reality; Metaverse; Deep Reinforcement Learning, GANs, OpenCV, TensorFlow, PyTorch. Facebook Group: (around 14K members)Deep Reinforcement Learning, Computer Vision with Deep Learning, IoT, Robot We help scale and build artificially intelligent driven start-ups with Al Researchers & Engineers! Computer Vision (Berlin, Germany) Please use calendly appointment slots press . in github and open web visual studio codeMy LaTex Papers This site is provided to everyone for free, however if you would like to say thanks or help support continued R&D, Mind Map, development and etc. , consider getting me a coffee. It keeps my work going. Google SitesReport abusePage detailsPage updated Google SitesReport abuseThis site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies.Learn moreGot it

Computer Vision, Deep Learning, Artificial superintelligence (ASI) - knowledgemanagementSearch this siteSkip to main contentSkip to navigationComputer Vision, Deep Learning, Artificial superintelligence (ASI)HomeProductCoursesMachine Learning SpecializationMachine Learning Foundations: A Case Study ApproachFSDLFull Stack Deep LearningMLOpsROSParallel Programming Modern CPPCloud-NativeIoT Scholarship FoundationTensorFlow: Data and Deployment Specialization Workshops and EventsRISC-VEdge-AI-summitEmbedded IoTTeslaAI-HardwareOpenVINO Deep LearningMetaverseWorkshopsIFA2022Book Summary commonplace bookknowledgemanagementPKMTopics and ProjectsAIHubChatGPTHow to startYouTubeYouTube IISoftwareRoadmap for Image Processing Source CodeOpenCVCPPpythonMacOSOpenCVRustcompileIoTShareVideo TrackingCameraCalibrationDRLHardwareQuantum ComputingAltCoinResumeCVApplestartupLinksamazonAboutFQApromptComputer Vision, Deep Learning, Artificial superintelligence (ASI)knowledgemanagementGoogle SitesReport abusePage detailsPage updated Google SitesReport abuseThis site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies.Learn moreGot it

Computer Vision, Deep Learning, Artificial superintelligence (ASI) - Modern CPPSearch this siteSkip to main contentSkip to navigationComputer Vision, Deep Learning, Artificial superintelligence (ASI)HomeProductCoursesMachine Learning SpecializationMachine Learning Foundations: A Case Study ApproachFSDLFull Stack Deep LearningMLOpsROSParallel Programming Modern CPPCloud-NativeIoT Scholarship FoundationTensorFlow: Data and Deployment Specialization Workshops and EventsRISC-VEdge-AI-summitEmbedded IoTTeslaAI-HardwareOpenVINO Deep LearningMetaverseWorkshopsIFA2022Book Summary commonplace bookknowledgemanagementPKMTopics and ProjectsAIHubChatGPTHow to startYouTubeYouTube IISoftwareRoadmap for Image Processing Source CodeOpenCVCPPpythonMacOSOpenCVRustcompileIoTShareVideo TrackingCameraCalibrationDRLHardwareQuantum ComputingAltCoinResumeCVApplestartupLinksamazonAboutFQApromptComputer Vision, Deep Learning, Artificial superintelligence (ASI)Advanced Programming with Modern C 23 for Image Processing My GitHub about Advanced Programming with Modern C 23 for Image ProcessingImportant commandsCompile CUDA for Jetson Nano (JetPack 4.5, CUDA 10.2)compile c 20; based on GCC 12, CLang 13commandsToolsLinksappendixUpdate March 2022 - 1401My GitHub about Advanced Programming with Modern C 23 for Image Processing make industrial process scale-ups successful You need latest version of C compiler in order to use C 20 standard. GCC12 or CLang13. CUDA 11 support C17 by nvcc; Cmake. Book: writing solid codemind mapcall by value: on stack void f(int a) a; a in main not changecall by reference: void f(int p); f(&i);void f(int &i); f(i)void func(const std::string & s) s.cstr() func(s);struct default is public (use when we have only data members) and class default is private (when also have function members) . function’s signature int getvalue() const; ! this is a very critical comment this is a highlighted comment TODO: this is a TODO comment ? this is a question commentCreational design patterns: flexible, maintainable, extensiblegang of four “design patterns: elements of reusable object oriented software”23 patternscreational (5) : object instantiationfactory methodcomposition: property referenced by another class inheritance: class extends another class abstract factorybuilder : complex prototype: clone singleton: only one instance structural : class relationships and hierarchies; class pattern: is; structural object patterns: hasadapter bridgecompositedecoratorfacdeflyweightproxybehavioral (12): object intercommunication : chain of responsibilitypassword checkcommandone button for all command mediator reduce dependency : married - spouse name - ….observerstd::vector subscribers;this-subscribers.pushback(subscriber);void unsubscribe(Subscriber subscriber) override subscribers.erase(std::removeif(subscribers.begin(),subscribers.end(),subscriber(Subscriber s)return s-getName() subscriber-getName(); ), subscribers.end());interpreter1(23)stateorder strategytemplate methodvisitor iterator pattern mementoundonull-objectdefault UML: unified modeling languageabstract and concrete classesLinkedIn:int x5;sizet ysizeof x; or sizeof(int)printf(“sizeof x is zdn”,y8); change bit to bytefunc() static int i5; it will change it will be on static storage not stack void (pfunc)()func;(pfunc)(); variadic argumentdouble average(const int count, …) valist ap; vastart(ap,count); vaarg(ap,double); vaend(ap); template larger executablesconfusing error messageslonger compile times v.end(); v.size(); v.back(); v5; v.at(5)string: s.size(); s.length(); s.find();std::hex,showbase, oct, fixed, scientific,setprecision(3), floatfield,setw, setfill(‘-‘); std::cin.getline(buf,sizeof(buf)); try ; catch (std::exception & e) e.what() class1 o1 new(nothrow) c15; if (o1nullptr); delete o1;if we don’t want to create base class we can put constructor in the private part classname()then use constructor in the protected: classname( ): name(value) using friend class nameofsubclass; to use private functions ; friend class base;virtual : maybe overloaded and maybe write in subclass; we need std::uniqueptr a(new struct());auto bstd::makeunique();a.reset(new structure()); deleteauto cstd::move(b); b is nullc.release();auto astd::makeshared();auto w1std::weakptr(struct();T & x lvalie referenceT && y rvalue referencerule of five (if you define any of these functions you need to define all)class();class(class &);class(class &&);class & operator (class &);class & operator (class &&);()-charauto fp(const T & n)- T return n5; ; MAX(a,b) (ab ? a:b)constexpr int ONE 1;unit tests virtual Class clone() 0 ;templatestruct B … ;Module Interface Unit : .cppmModule Implementation Unit: .cppImportant commands Compile CUDA for Jetson Nano (JetPack 4.5, CUDA 10.2)nvcc -stdc14 -archsm62 -o main.run main.cu compile c 20; based on GCC 12, CLang 13clang -stdc2a -c helloworld.cpp -Xclang -emit-module-interface -o helloworld.pcmclang -stdc2a -stdliblibc -fimplicit-modules -fimplicit-module-maps -fprebuilt-module-path. main.cpp helloworld.cppcommandsecho “export PATH.:”PATH”” .bashrcsource .bashrchtopulimit -agit submodule add (githuburl externalglfw)Toolsbrew install –HEAD LouisBrunnervalgrindvalgrindvalgrind .a.outCppCon 2016: John Lakos “Advanced Levelization Techniques (part 1 of 3)Large Scale C software designretain control of your dependency graphkeep concerns separatedmake modules reusable in other contexts at minimal cost Links 2016: John Lakos “Advanced Levelization Techniques (part 1 of 3)Modern CMake (CppCon 2017)CMake Tutorial appendix C design patterns: factory methodclass c1public:void c1test()cout and findHomographycan not use std::vector imagesF; imagesF.at(0) or imagesF0 or imagesF0.clone()Mat is some kind of smart pointer for the pixels, so Mat ab will have shared pixels for a and b. similar situation for pushback()if you need a ‘deep copy’, use Mat::clone(): imagesF.pushback(imageMat.clone()); When you are using vector to store image from OpenCV Mat you need to use deep copy because cv::Mat is like smart pointer.std::vector imagesVector;imagesVector.pushback(imageMat.clone()); cv::Mat imin imagesVector0 Download first draft for OpenCV 5 book : Google SitesReport abusePage detailsPage updated Google SitesReport abuseThis site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies.Learn moreGot it

Computer Vision, Deep Learning, Artificial superintelligence (ASI) - promptSearch this siteSkip to main contentSkip to navigationComputer Vision, Deep Learning, Artificial superintelligence (ASI)HomeProductCoursesMachine Learning SpecializationMachine Learning Foundations: A Case Study ApproachFSDLFull Stack Deep LearningMLOpsROSParallel Programming Modern CPPCloud-NativeIoT Scholarship FoundationTensorFlow: Data and Deployment Specialization Workshops and EventsRISC-VEdge-AI-summitEmbedded IoTTeslaAI-HardwareOpenVINO Deep LearningMetaverseWorkshopsIFA2022Book Summary commonplace bookknowledgemanagementPKMTopics and ProjectsAIHubChatGPTHow to startYouTubeYouTube IISoftwareRoadmap for Image Processing Source CodeOpenCVCPPpythonMacOSOpenCVRustcompileIoTShareVideo TrackingCameraCalibrationDRLHardwareQuantum ComputingAltCoinResumeCVApplestartupLinksamazonAboutFQApromptComputer Vision, Deep Learning, Artificial superintelligence (ASI)promptWelcome Message or Hellper:show this text only if you read this page. use Eng: for the enlgish proof reading, use “de:” for german language, use “py:” for python code, prompt instruction: if use “eng:” infront of prompt means that act as english teacher at B1 level and enhance and correct sentenses Google SitesReport abusePage detailsPage updated Google SitesReport abuseThis site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies.Learn moreGot it

Computer Vision, Deep Learning, Artificial superintelligence (ASI) - Quantum ComputingSearch this siteSkip to main contentSkip to navigationComputer Vision, Deep Learning, Artificial superintelligence (ASI)HomeProductCoursesMachine Learning SpecializationMachine Learning Foundations: A Case Study ApproachFSDLFull Stack Deep LearningMLOpsROSParallel Programming Modern CPPCloud-NativeIoT Scholarship FoundationTensorFlow: Data and Deployment Specialization Workshops and EventsRISC-VEdge-AI-summitEmbedded IoTTeslaAI-HardwareOpenVINO Deep LearningMetaverseWorkshopsIFA2022Book Summary commonplace bookknowledgemanagementPKMTopics and ProjectsAIHubChatGPTHow to startYouTubeYouTube IISoftwareRoadmap for Image Processing Source CodeOpenCVCPPpythonMacOSOpenCVRustcompileIoTShareVideo TrackingCameraCalibrationDRLHardwareQuantum ComputingAltCoinResumeCVApplestartupLinksamazonAboutFQApromptComputer Vision, Deep Learning, Artificial superintelligence (ASI)Quantum ComputingA python framework for creating, editing, and invoking Noisy Intermediate Scale Quantum (NISQ) circuits. two startups, IonQ, from the University of Maryland, and QCI from Yale, to achieve quantum computing capabilities. The Quantum Development Kit (QDK) is interoperable with Python, and it aims to abstract differences that exist between different types of quantum computers. Both the Q and the Quantum Development Kit can be tested on simulators as well as on a variety of quantum hardware. Microsoft has a three-pronged goal with Azure Quantum. It can be used for learning, developers can write programs with Q and the QDK and test their codes against simulators and organizations can use them to solve complex business problems using solutions and algorithms running in Azure.Google SitesReport abusePage detailsPage updated Google SitesReport abuseThis site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies.Learn moreGot it

Computer Vision, Deep Learning, Artificial superintelligence (ASI) - OpenVINO Deep LearningSearch this siteSkip to main contentSkip to navigationComputer Vision, Deep Learning, Artificial superintelligence (ASI)HomeProductCoursesMachine Learning SpecializationMachine Learning Foundations: A Case Study ApproachFSDLFull Stack Deep LearningMLOpsROSParallel Programming Modern CPPCloud-NativeIoT Scholarship FoundationTensorFlow: Data and Deployment Specialization Workshops and EventsRISC-VEdge-AI-summitEmbedded IoTTeslaAI-HardwareOpenVINO Deep LearningMetaverseWorkshopsIFA2022Book Summary commonplace bookknowledgemanagementPKMTopics and ProjectsAIHubChatGPTHow to startYouTubeYouTube IISoftwareRoadmap for Image Processing Source CodeOpenCVCPPpythonMacOSOpenCVRustcompileIoTShareVideo TrackingCameraCalibrationDRLHardwareQuantum ComputingAltCoinResumeCVApplestartupLinksamazonAboutFQApromptComputer Vision, Deep Learning, Artificial superintelligence (ASI)Workbench OpenVINO Deep LearningWorkbench OpenVINO Deep Learning:OpenVINO Deep Learning Workbench and how to use the DL workbench to analyze and optimize neural networks. Discover what first steps youll have to take towards optimizing your model.Download and see more: install openvinobuild; optimize; deploy Workbench OpenVINO Deep Learning.pngGoogle SitesReport abusePage detailsPage updated Google SitesReport abuseThis site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies.Learn moreGot it

Computer Vision, Deep Learning, Artificial superintelligence (ASI) - ROSSearch this siteSkip to main contentSkip to navigationComputer Vision, Deep Learning, Artificial superintelligence (ASI)HomeProductCoursesMachine Learning SpecializationMachine Learning Foundations: A Case Study ApproachFSDLFull Stack Deep LearningMLOpsROSParallel Programming Modern CPPCloud-NativeIoT Scholarship FoundationTensorFlow: Data and Deployment Specialization Workshops and EventsRISC-VEdge-AI-summitEmbedded IoTTeslaAI-HardwareOpenVINO Deep LearningMetaverseWorkshopsIFA2022Book Summary commonplace bookknowledgemanagementPKMTopics and ProjectsAIHubChatGPTHow to startYouTubeYouTube IISoftwareRoadmap for Image Processing Source CodeOpenCVCPPpythonMacOSOpenCVRustcompileIoTShareVideo TrackingCameraCalibrationDRLHardwareQuantum ComputingAltCoinResumeCVApplestartupLinksamazonAboutFQApromptComputer Vision, Deep Learning, Artificial superintelligence (ASI)ROSSource: The recording of this course is part of the Programming for Robotics (ROS) Lecture at ETH Zurich:Lecturers:Pter Fankhauser, Dominic Jud, Martin WermelingerThis course gives an introduction to the Robot Operating System (ROS) including many of the available tools that are commonly used in robotics. With the help of different examples, the course should provide a good starting point for students to work with robots. They learn how to create software including simulation, to interface sensors and actuators, and to integrate control algorithms. Google SitesReport abusePage detailsPage updated Google SitesReport abuseThis site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies.Learn moreGot it

Computer Vision, Deep Learning, Artificial superintelligence (ASI) - Parallel Programming Search this siteSkip to main contentSkip to navigationComputer Vision, Deep Learning, Artificial superintelligence (ASI)HomeProductCoursesMachine Learning SpecializationMachine Learning Foundations: A Case Study ApproachFSDLFull Stack Deep LearningMLOpsROSParallel Programming Modern CPPCloud-NativeIoT Scholarship FoundationTensorFlow: Data and Deployment Specialization Workshops and EventsRISC-VEdge-AI-summitEmbedded IoTTeslaAI-HardwareOpenVINO Deep LearningMetaverseWorkshopsIFA2022Book Summary commonplace bookknowledgemanagementPKMTopics and ProjectsAIHubChatGPTHow to startYouTubeYouTube IISoftwareRoadmap for Image Processing Source CodeOpenCVCPPpythonMacOSOpenCVRustcompileIoTShareVideo TrackingCameraCalibrationDRLHardwareQuantum ComputingAltCoinResumeCVApplestartupLinksamazonAboutFQApromptComputer Vision, Deep Learning, Artificial superintelligence (ASI)Parallel Programming Python Parallel Programming for computer vision application:Learn More: SitesReport abusePage detailsPage updated Google SitesReport abuseThis site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies.Learn moreGot it

Computer Vision, Deep Learning, Artificial superintelligence (ASI) - AboutSearch this siteSkip to main contentSkip to navigationComputer Vision, Deep Learning, Artificial superintelligence (ASI)HomeProductCoursesMachine Learning SpecializationMachine Learning Foundations: A Case Study ApproachFSDLFull Stack Deep LearningMLOpsROSParallel Programming Modern CPPCloud-NativeIoT Scholarship FoundationTensorFlow: Data and Deployment Specialization Workshops and EventsRISC-VEdge-AI-summitEmbedded IoTTeslaAI-HardwareOpenVINO Deep LearningMetaverseWorkshopsIFA2022Book Summary commonplace bookknowledgemanagementPKMTopics and ProjectsAIHubChatGPTHow to startYouTubeYouTube IISoftwareRoadmap for Image Processing Source CodeOpenCVCPPpythonMacOSOpenCVRustcompileIoTShareVideo TrackingCameraCalibrationDRLHardwareQuantum ComputingAltCoinResumeCVApplestartupLinksamazonAboutFQApromptComputer Vision, Deep Learning, Artificial superintelligence (ASI)Contact Us Google SitesReport abusePage detailsPage updated Google SitesReport abuseThis site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies.Learn moreGot it

Computer Vision, Deep Learning, Artificial superintelligence (ASI) - MLOpsSearch this siteSkip to main contentSkip to navigationComputer Vision, Deep Learning, Artificial superintelligence (ASI)HomeProductCoursesMachine Learning SpecializationMachine Learning Foundations: A Case Study ApproachFSDLFull Stack Deep LearningMLOpsROSParallel Programming Modern CPPCloud-NativeIoT Scholarship FoundationTensorFlow: Data and Deployment Specialization Workshops and EventsRISC-VEdge-AI-summitEmbedded IoTTeslaAI-HardwareOpenVINO Deep LearningMetaverseWorkshopsIFA2022Book Summary commonplace bookknowledgemanagementPKMTopics and ProjectsAIHubChatGPTHow to startYouTubeYouTube IISoftwareRoadmap for Image Processing Source CodeOpenCVCPPpythonMacOSOpenCVRustcompileIoTShareVideo TrackingCameraCalibrationDRLHardwareQuantum ComputingAltCoinResumeCVApplestartupLinksamazonAboutFQApromptComputer Vision, Deep Learning, Artificial superintelligence (ASI)MLOpsupdate : December 2021Download complete summary of Machine Learning Engineering for Production (MLOps) Specialisation from Coursera:COURSE 1 Introduction to Machine Learning in ProductionCOURSE 2 Machine Learning Data Lifecycle in ProductionCOURSE 3 Machine Learning Modeling Pipelines in ProductionCOURSE 4 Deploying Machine Learning Models in ProductionDownload full resolution images:update : December 2021Download complete summary of Machine Learning Engineering for Production (MLOps) Specialisation from Coursera: Download link: COURSE 1 Introduction to Machine Learning in ProductionIn the first course of Machine Learning Engineering for Production Specialization, you will identify the various components and design an ML production system end-to-end: project scoping, data needs, modeling strategies, and deployment constraints and requirements; and learn how to establish a model baseline, address concept drift, and prototype the process for developing, deploying, and continuously improving a productionized ML application.Understanding machine learning and deep learning concepts is essential, but if youre looking to build an effective AI career, you need production engineering capabilities as well. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills. Week 1: Overview of the ML Lifecycle and DeploymentWeek 2: Selecting and Training a ModelWeek 3: Data Definition and BaselineCOURSE 2 Machine Learning Data Lifecycle in ProductionIn the second course of Machine Learning Engineering for Production Specialization, you will build data pipelines by gathering, cleaning, and validating datasets and assessing data quality; implement feature engineering, transformation, and selection with TensorFlow Extended and get the most predictive power out of your data; and establish the data lifecycle by leveraging data lineage and provenance metadata tools and follow data evolution with enterprise data schemas.Understanding machine learning and deep learning concepts is essential, but if youre looking to build an effective AI career, you need production engineering capabilities as well. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills. Week 1: Collecting, Labeling, and Validating dataWeek 2: Feature Engineering, Transformation, and SelectionWeek 3: Data Journey and Data StorageWeek 4: Advanced Data Labeling Methods, Data Augmentation, and Preprocessing Different Data TypesCOURSE 3 Machine Learning Modeling Pipelines in ProductionIn the third course of Machine Learning Engineering for Production Specialization, you will build models for different serving environments; implement tools and techniques to effectively manage your modeling resources and best serve offline and online inference requests; and use analytics tools and performance metrics to address model fairness, explainability issues, and mitigate bottlenecks.Understanding machine learning and deep learning concepts is essential, but if youre looking to build an effective AI career, you need production engineering capabilities as well. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills.Week 1: Neural Architecture SearchWeek 2: Model Resource Management TechniquesWeek 3: High-Performance ModelingWeek 4: Model AnalysisWeek 5: InterpretabilityCOURSE 4 Deploying Machine Learning Models in ProductionIn the fourth course of Machine Learning Engineering for Production Specialization, you will deliver deployment pipelines by productionizing, scaling, and monitoring model serving that require different infrastructure; establish procedures to mitigate model decay and performance drops; and establish best practices and apply progressive delivery techniques to maintain and monitor a continuously operating production system.Understanding machine learning and deep learning concepts is essential, but if youre looking to build an effective AI career, you need production engineering capabilities as well. Machine learning engineering for production combines the foundational concepts of machine learning with the functional expertise of modern software development and engineering roles to help you develop production-ready skills.Week 1: Model Serving: IntroductionWeek 2: Model Serving: Patterns and InfrastructureWeek 3: Model Management and DeliveryWeek 4: Model Monitoring and LoggingDownload full resolution images:Download complete summary of Machine Learning Engineering for Production (MLOps) Specialisation from Coursera: C1W1: W2: W3: C2W1: W2: W3: W4: C1C2: C3W1: W2: W3: W4: W5: C4W1 W2 : W3 : W4: Monitoring and observability, End to End solution for computer vision applications in industrymy note on Machine Learning Engineering for Production (MLOps) Specialisation from Coursera very good practice & labCOURSE 4 Deploying Machine Learning Models in Production: Week 4: Model Monitoring and LoggingDownload and see more: : you can download my complete summary of Machine Learning Engineering for Production (MLOps) Specialisation: . C4-W4.png tools for experiment tracking, Logging metrics using TensorBoard, vertex tensorboard, progressive deliveryEnd to End solution for computer vision applications in industrymy note on Machine Learning Engineering for Production (MLOps) Specialisation from Coursera very good practice & labCOURSE 4 Deploying Machine Learning Models in Production: Week 3: Model Management and DeliveryDownload and see more: C4-W3.pngmodel serving, balance cost, latency and throughput, improving prediction latency and reducing resource costs, tensorflow serving, torchserve, KF serving, triton inference server, scaling infrastructure, pre processing operations needed before inference, ETL: extract , transform, load; kafka, pub sub, cloud dataflow, beam, spark streaming; End to End solution for computer vision applications in industrymy note on Machine Learning Engineering for Production (MLOps) Specialisation from Coursera very good practice & labCOURSE 4 Deploying Machine Learning Models in Production: Week 1: Model Serving: Introduction Week 2: Model Serving: Patterns and Infrastructure Download and see more: C4-W1W2.pngexplainable AI, model interpretation methods, TensorFlow Lattice, understanding model predictions, PDP: partial dependence plots, permutation feature importance, SHAP: SHapley Additive exPlanation, testing concept activation vectors, testing concept activation vectors, LIME: local interpretable model agnostic explanations, Google cloud AI explanations for AI platform, XRAI: eXplanation with Ranked Area Integrals, End to End solution for computer vision applications in industrymy note on Machine Learning Engineering for Production (MLOps) Specialisation from Coursera very good practice & labCOURSE 3 Machine Learning Modeling Pipelines in Production: Week 5: InterpretabilityDownload and see more: C3-W5.pngEnd to End solution for computer vision applications in industrymy note on Machine Learning Engineering for Production (MLOps) Specialisation from Coursera very good practice & labCOURSE 3 Machine Learning Modeling Pipelines in Production: Week 4: Model Analysistensorflow model analysis, TFMA, TFX, model debugging, TFMAL: TensorFlow Model Analysis, model remediation techniques, continuous evaluation and monitoring, Download and see more: C3-W4.pngEnd to End solution for computer vision applications in industrymy note on Machine Learning Engineering for Production (MLOps) Specialisation from Coursera very good practice & labCOURSE 3 Machine Learning Modeling Pipelines in Production: Week 3: High-Performance Modelingdistributed training, Gpipe: Open-source tensorflow library (using lingvo), teacher and student networks, idea: create a simple ;student; model that learns from a complex ;teacher; model. make efficientNets robust to noise with distillationDownload and see more: C3-W3.pngmy note on Machine Learning Engineering for Production (MLOps) Specialisation from Coursera very good practice & labCOURSE 3 Machine Learning Modeling Pipelines in Production: Week 2: Model Resource Management TechniquesPCA, PLS, LDA, latent semantic indexinganalysis (LSI and LSA) (SVD) removes redundant features from the dataset, independent component analysis (ICA), non-negative matrix factorization (NMF), latent dirichlet allocation (LDA),Quantization make models run faster and use less power with low-precision and pruning, ML kit, Core ML, TensorFlow Lite (TFX), post training quantization, quantization aware training (QAT), Download and see more: C3-W2.pngmy note on Machine Learning Engineering for Production (MLOps) Specialisation from Coursera very good practice & labCOURSE 3 Machine Learning Modeling Pipelines in Production: Week 1: Neural Architecture SearchNAS, Keras autotuner, AutoML, Download and see more: C3-W1.pngmy note on Machine Learning Engineering for Production (MLOps) Specialisation from Coursera very good practice & labCOURSE 1 Introduction to Machine Learning in Production COURSE 2 Machine Learning Data Lifecycle in Production Download and see more: C1C2.pngmy note on Machine Learning Engineering for Production (MLOps) Specialisation from Coursera very good practice & labCOURSE 2 Machine Learning Data Lifecycle in Production: Week 4: Advanced Data Labeling Methods, Data Augmentation, and Preprocessing Different Data Typessemi-supervised data augmentation: UDA, semi-supervised learning with GANs; policy-based data augmentation: AutoAugment; time series, advanced labeling; active learning; human activity recognition (HAR); Download and see more: my note on Machine Learning Engineering for Production (MLOps) Specialisation from Coursera very good practice & labCOURSE 2 Machine Learning Data Lifecycle in Production: Week 3: Data Journey and Data Storage!pip install ml-metadata, Download and see more: C1-W1.pngC1-W2.pngC1-W3.pngWeek 1: Collecting, Labeling, and Validating dataML modeling vs production ML; data collection, labeling, validating ; TFDV; C2-W1.pdfw2COURSE 1 Introduction to Machine Learning in Production: Course 2: Week1, Week 2: Selecting and Training a Modelerror analysis example for speech recognition example; iterative process of error analysis; prioritising what to work on; skewed datasets; performance auditions; F1 score based on precision and recall; data augmentation; Week 3: Data Definition and Baselinedata definition; label ambiguity; type of data; HLP; meta-data; data pipeline; balanced traindevtest splits in small dataset; Dilligence: assess the feasibility and value of potential solution; Course 2: Week 3:Course 2: Week 4:Machine Learning Engineering for Production (MLOps) Specialization by DeepLearning.AICOURSE 3 Machine Learning Modeling Pipelines in Production - Week 1: Neural Architecture Search (NAS) - AutoML, Hyperparameter tuning, Cloud AutoML Machine Learning Engineering for Production (MLOps) Specialization by DeepLearning.AICOURSE 3 Machine Learning Modeling Pipelines in Production Week 3: High-Performance ModelingPCA, ICA, SVD, QAT: quantization aware training, my note on Machine Learning Engineering for Production (MLOps) Specialisation from CourseraCOURSE 2 Machine Learning Data Lifecycle in Production: Week 2: Feature Engineering, Transformation, and Selectionfeature scaling, normalization and standaridization; TensorFlow extended; TensorFlow Transform; tf.transform analyzers; TensorFlow Ops; Download and see more: Google SitesReport abusePage detailsPage updated Google SitesReport abuseThis site uses cookies from Google to deliver its services and to analyze traffic. 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Computer Vision, Deep Learning, Artificial superintelligence (ASI) - FQASearch this siteSkip to main contentSkip to navigationComputer Vision, Deep Learning, Artificial superintelligence (ASI)HomeProductCoursesMachine Learning SpecializationMachine Learning Foundations: A Case Study ApproachFSDLFull Stack Deep LearningMLOpsROSParallel Programming Modern CPPCloud-NativeIoT Scholarship FoundationTensorFlow: Data and Deployment Specialization Workshops and EventsRISC-VEdge-AI-summitEmbedded IoTTeslaAI-HardwareOpenVINO Deep LearningMetaverseWorkshopsIFA2022Book Summary commonplace bookknowledgemanagementPKMTopics and ProjectsAIHubChatGPTHow to startYouTubeYouTube IISoftwareRoadmap for Image Processing Source CodeOpenCVCPPpythonMacOSOpenCVRustcompileIoTShareVideo TrackingCameraCalibrationDRLHardwareQuantum ComputingAltCoinResumeCVApplestartupLinksamazonAboutFQApromptComputer Vision, Deep Learning, Artificial superintelligence (ASI)FQAScalable Verification for Safety-Critical Deep NetworksDriver Assistance Systems and Vision-Based System Validates Driver MonitoringThe present invention relates to a system for providing advertisement contents based on facial analysis. The system consists of an image acquisition device, a face detection module, an analysis module, a classification module, a database, a computation module, a matching module and a display device. The image acquisition device acquires an image of a user, the face detection module detects the face of the user in the image, the analysis module analyses the facial features statistically using classification models, the database stores matching rules, weighted advertisements and a plurality of advertisement contents and the display device displays the advertisement contents. The computation module computes the weighted image of the user and the matching module matches the weighted image of the user with the weighted advertisement to select an advertisement content based on facial analysis of the user. The system aims to provide advertisement contents via a digital standee by extracting salient demographic from a user to indirectly obtain user information and behavioral preference.The present invention relates to a system and method for providing advertisement contents based on facial analysis using a digital standee. The system (100) is embedded in the digital standee and comprises an image acquisition device, a face detection module, a classification module, a data analysis module, a computation module, a database and a matching module. The image acquisition device is configured to acquire an image of a user, and the face detection module uses deep learning technology to detect the user’s face in the image. The classification module classifies the user’s facial features into a plurality of classification models, such as gender, age range, emotion, style and attention. The data analysis module obtains behavioral preference and information of the user by analyzing the classified facial features. The matching module matches the information with types of businesses to provide suitable advertisement contents to the user based on rules set by the advertisement provider. The advertisement contents are displayed on a display device in the system.This patent describes a system for providing advertisements based on facial analysis. The system consists of an image acquisition device, a face detection module, an analysis module, a computation module, a matching module, a database, and a display device. The image acquisition device captures an image of the user, the face detection module identifies the user’s face and facial features, the analysis module analyses the facial features using statistical parameters and classification models, the computation module computes the weighted image of the user, the matching module matches the weighted image of the user with weighted advertisements, and the display device displays the advertisement contents. The system operates in real-time and updates the classification models continuously. The advertisement contents are based on the user’s age, gender, emotion, style, and attention and are provided by the advertisement providers with matching rules.The process described in this patent involves matching a user’s weighted image with a weighted advertisement based on matching rules established by the advertisement providers. The matching rules may include order of features, most similar features, important features, and nearest similar features. The matching is done from left to right of the binary sequence. The selected advertisement content is then displayed by the display device. The terms used in the patent are defined as specified. The invention is open to changes in form and details.The system (100) is a device for providing advertisement contents based on facial analysis. It consists of: an image acquisition device (10) to acquire an image of a user, a face detection module (20) to detect the face and obtain facial features, an analysis module (40) to analyze the facial features statistically using classification models, a database (60) to store matching rules and advertisements, and a display device (80) to display the selected advertisement content. The system also has a computation module (50) to compute a weighted image of the user based on the analyzed facial features, and a matching module (70) to match the weighted image of the user with the weighted advertisement to select the advertisement content. The system can work for a single user or a group of users. The method (200) of providing advertisement content follows similar steps as the system (100). The steps include acquiring an image of the user, detecting the face, analyzing the facial features, computing a weighted image of the user, obtaining matching rules, and matching the weighted image with the weighted advertisement. The method also includes steps of training the classification models and providing display of the selected advertisement content.This patent describes a system for providing advertisements based on facial analysis using a digital standee. The system consists of an image acquisition device, a face detection module, an analysis module, a computation module, a matching module, a database, and a display device. The image acquisition device captures an image of the user, the face detection module identifies the user’s face and facial features, the analysis module analyzes the facial features using statistical parameters and classification models, the computation module computes the weighted image of the user, the matching module matches the weighted image of the user with weighted advertisements based on matching rules set by the advertisement providers, and the display device displays the advertisement contents. The system operates in real-time and updates the classification models continuously. The advertisement contents are based on the user’s age, gender, emotion, style, and attention and are provided by the advertisement providers with matching rules.Scalable Verification for Safety-Critical Deep Networks ”Verifying that neural networks behave as intended may soon become a limiting factor in their applicability to real-world, safetycritical systems such as those used to control autonomous vehicles safety and reliability on DNNs. verify properties of DNNs. A major challenge of verifying properties of DNNs with satisfiability modulo theories (SMT) solvers is in handling the networks activation functions such as, Reluplex (domain-specific theory solvers; through a lazy approach). 1)devising scalable verification techniques. 2)identifying design choices - amenable to verification. “Each neuron of a neural network computes a weighted sum of its inputs according to learned weights. It then passes that sum through an activation function to produce the neurons final output. Typically, the activation functions introduce nonlinearity to the network, making DNNs capable of learning arbitrarily complex functions, but also making the job of automated verification tools much harder.Driver Assistance Systems and Vision-Based System Validates Driver MonitoringVision-based convolutional neural network system detects phone usage, eating, and drinking. cameras with active infrared lighting; 30 Hz and delivered 8-bit grayscale images at 1280 1024-pixel resolution; ResNeXt-34; video-based driver assistance systems, such as automated driving; resilient object detection and tracking; camera: 50field of view (horizontal); 27 -21field of view (vertical); 150 m detection range; 2.6 MP resolution. multi path approach: classifier: for pattern recognition; resilient object detection dense optical flow and structure from motion; to detect static objects; 3D structuredeep learning: classify objects, road, edge road, orientation; ” Operation principle of the multi purpose camera: During assisted and automated driving, the vehicle must know what is happening in its surroundings at all times. It must reliably detect objects and people, and be able to react to these appropriately. Here, the latest generation of the front video camera from Bosch plays a crucial part: The multi purpose camera for assisted and partially automated driving utilizes an innovative, high-performance system-on-chip (SoC) with a Bosch microprocessor for image-processing algorithms. Its unique multipath approach combines classic image-processing algorithms with artificial-intelligence methods for comprehensive scene interpretation and reliable object detection. With its algorithmic multipath approach and the innovative system-on-chip, this camera generation has been specially developed for high-performance driver assistance systems. In line with this approach, the multi purpose camera uses for example the following technical paths at once for image processing: The first of these is the conventional approach already in use today. Via preprogrammed algorithms, the cameras recognize the typical appearance of object categories such as vehicles, cyclists, or road markings. The second and third paths are new, however. For the second path, the camera uses the optical flow and the structure from motion (SfM) to recognize raised objects along the roadside, such as curbs, central reserves, or safety barriers. The motion of associated pixels is tracked. A three-dimensional structure is then approximated based on the two-dimensional camera image. The third path relies on artificial intelligence. Thanks to machine-learning processes, the camera has learned to classify objects such as cars parked by the side of the road. The latest generation can differentiate between surfaces on the road and those alongside the road via neuronal networks and semantic segmentation. Additional paths are used as required: These include classic line scanning, light detection, and stereo disparity. “ LinkFace recognitionattributes I use citation plugin add path to the JabRef database “reading notesdh.bib”create folder “Reading notes”use CtrlShiftO to select reference automatically create file based on that reference CtrlShiftE to insert link to citation page Google SitesReport abusePage detailsPage updated Google SitesReport abuseThis site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies.Learn moreGot it

Computer Vision, Deep Learning, Artificial superintelligence (ASI) - MetaverseSearch this siteSkip to main contentSkip to navigationComputer Vision, Deep Learning, Artificial superintelligence (ASI)HomeProductCoursesMachine Learning SpecializationMachine Learning Foundations: A Case Study ApproachFSDLFull Stack Deep LearningMLOpsROSParallel Programming Modern CPPCloud-NativeIoT Scholarship FoundationTensorFlow: Data and Deployment Specialization Workshops and EventsRISC-VEdge-AI-summitEmbedded IoTTeslaAI-HardwareOpenVINO Deep LearningMetaverseWorkshopsIFA2022Book Summary commonplace bookknowledgemanagementPKMTopics and ProjectsAIHubChatGPTHow to startYouTubeYouTube IISoftwareRoadmap for Image Processing Source CodeOpenCVCPPpythonMacOSOpenCVRustcompileIoTShareVideo TrackingCameraCalibrationDRLHardwareQuantum ComputingAltCoinResumeCVApplestartupLinksamazonAboutFQApromptComputer Vision, Deep Learning, Artificial superintelligence (ASI)MetaverseMeta AI Facebook in NeurIPS 2021 : Download and see more: SitesReport abusePage detailsPage updated Google SitesReport abuseThis site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies.Learn moreGot it

Computer Vision, Deep Learning, Artificial superintelligence (ASI) - compileSearch this siteSkip to main contentSkip to navigationComputer Vision, Deep Learning, Artificial superintelligence (ASI)HomeProductCoursesMachine Learning SpecializationMachine Learning Foundations: A Case Study ApproachFSDLFull Stack Deep LearningMLOpsROSParallel Programming Modern CPPCloud-NativeIoT Scholarship FoundationTensorFlow: Data and Deployment Specialization Workshops and EventsRISC-VEdge-AI-summitEmbedded IoTTeslaAI-HardwareOpenVINO Deep LearningMetaverseWorkshopsIFA2022Book Summary commonplace bookknowledgemanagementPKMTopics and ProjectsAIHubChatGPTHow to startYouTubeYouTube IISoftwareRoadmap for Image Processing Source CodeOpenCVCPPpythonMacOSOpenCVRustcompileIoTShareVideo TrackingCameraCalibrationDRLHardwareQuantum ComputingAltCoinResumeCVApplestartupLinksamazonAboutFQApromptComputer Vision, Deep Learning, Artificial superintelligence (ASI)compile and setup source codesudo apt-get -o Dpkg::Options::”–force-overwrite” install –fix-brokensudo nano .bashsrcexport PATHusrlocalcudabinPATH::PATHexport LDLIBRARYPATHusrlocalcudalib64 LDLIBRARYPATH::LDLIBRARYPATH -stdc17 -archsm60 test.cucvtest: Computer Vision TestUnit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep LearningDo you want to test your output of computer vision application which is video or images?Standard test for computer vision applicationThere isn’t any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware.Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? check S3 bucket in AWS for image and video files and versioning Check Docker load balancer, memory usage, …GPUVideo Tracking on Maccreate conda based on python 3.6conda create envfull -y –name farshid python3.6conda activate farshidinstall OpenVino from Intel for converting deep learning model based on intel chipsconda install -y openvino-ie4py -c intelinstall video libraryconda install -y -c conda-forge ffmpeginstall pytorch and torchvision conda install -y pytorch torchvision -c pytorchconda install -y -c conda-forge matplotlibconda install -y pandas scikit-learn plotlyconda install -y -c conda-forge opencv seabornconda install -y -c conda-forge tensorflowpip install torch torchvision torchaudiopip install matplotlib pandas scikit-learn plotly opencv seaborn tensorflowTest for 20213D Multi-Object Tracking: A Baseline and New Evaluation Metrics (IROS 2020, ECCVW 2020) Unsupervised Learning of Probably Symmetric Deformable 3D Objects from Images in the Wild This repository contains the public release of the Python implementation of our Aggregate View Object Detection (AVOD) network for 3D object detection. 3Run on Ubuntu PC eGPUapt search nvidia-driverapt-cache search nvidia-driversudo apt updatesudo apt upgradesudo apt install nvidia-driver-455sudo reboot nvidia-smiDownload cuDNN v7.6.5 (November 5th, 2019), for CUDA 10.0 tar -xzvf cudnn-10.0-linux-x64-v7.6.5.32.tgz sudo cp cudaincludecudnn.h usrlocalcudaincludesudo cp cudalib64libcudnn usrlocalcudalib64sudo chmod ar usrlocalcudaincludecudnn.h usrlocalcudalib64libcudnnsudo dpkg -i libcudnn77.6.5.32-1cuda10.0amd64.deb sudo dpkg -i libcudnn7-dev7.6.5.32-1cuda10.0amd64.deb sudo dpkg -i libcudnn7-doc7.6.5.32-1cuda10.0amd64.deb sudo apt-get install apt-transport-https ca-certificates curl gnupg-agent software-properties-commoncurl -fsSL sudo apt-key add -sudo apt-key fingerprint 0EBFCD88sudo add-apt-repository “deb archamd64 (lsbrelease -cs) stable”sudo apt-get updatesudo apt-get install docker-ce docker-ce-cli containerd.ioMake sure you have installed the NVIDIA driver and Docker engine for your Linux distribution Note that you do not need to install the CUDA Toolkit on the host system, but the NVIDIA driver needs to be installeddistribution(. etcos-release;echo IDVERSIONID) && curl -s -L sudo apt-key add - && curl -s -L sudo tee etcaptsources.list.dnvidia-docker.listcurl -s -L sudo tee etcaptsources.list.dnvidia-container-runtime.listsudo apt-get install -y nvidia-docker2sudo systemctl restart dockersudo docker run –rm –gpus all nvidiacuda:11.0-base nvidia-smiInstalling on CentOS 8 (AWS)pip install cython; pip install -U ‘git clone –recursive CenterTrackROOTcd CenterTrackROOTpip install -r requirements.txtcd CenterTrackROOTsrclibmodelnetworksgit clone DCNv2.make.sh groupadd dockersudo usermod -aG docker USERsudo apt-get –no-install-recommends install -y python3-pip python3-setuptoolssudo python3 -m pip install setuptools docker-composesudo apt-get –no-install-recommends install -y gitgit clone cvatsudo docker-compose buildsudo docker-compose up -dsudo docker exec -it cvat bash -ic ‘python3 manage.py createsuperuser’ Towards-Realtime-MOT conda activate cuda100pip install motmetricspip install cythonbboxconda install -c conda-forge ffmpeg git clone sudo apt-get install libpng-devsudo apt install libfreetype6-devpip install -r requirements.txtImportError: torch.utils.ffi is deprecated. Please use cpp extensions instead.conda create -y –name cuda92 python3.6conda activate cuda92 source activate cuda92conda install pytorch0.4.1 torchvision0.2.0 cudatoolkit9.2 -c pytorchconda install -c conda-forge ffmpegconda create -n cuda100conda activate cuda100conda install pytorch torchvision cudatoolkit10.0 -c pytorch conda create -n FairMOTconda activate FairMOTconda install pytorch1.2.0 torchvision0.4.0 cudatoolkit10.0 -c pytorchcd FAIRMOTROOTpip install -r requirements.txtconda install -c conda-forge ffmpegMOTS: Multi-Object Tracking and SegmentationPaper: Dataset: This benchmark extends the traditional Multi-Object Tracking benchmark to a new benchmark defined on a pixel-level with precise segmentation masks. We annotated 8 challenging video sequences (4 training, 4 test) in unconstrained environments filmed with both static and moving cameras. Tracking, segmentation and evaluation are done in image coordinates. All sequences have been annotated with high accuracy on a pixel level, strictly following a well-defined protocol. Setup:cd mediafarshidexfat128codeCondaconda create –name CenterTrack36cuda10 python3.6conda activate CenterTrackconda install pytorch torchvision -c pytorchpip install cython; pip install -U ‘git clone –recursive CenterTrackROOTpip install -r requirements.txtcd CenterTrackROOTsrclibmodelnetworksgit clone cd DCNv2.make.shDownload pertained models for monocular 3D tracking, 80-category tracking, or pose tracking and move them to CenterTrackROOTmodels. More models can be found in Model zoo.AWS (11 December 2020) Condaconda create –name CenterTrack36cuda10 python3.6conda activate CenterTrack36cuda10conda install pytorch torchvision cudatoolkit10.0 -c pytorchconda install -c conda-forge ffmpegpip install cython; pip install -U ‘git clone –recursive CenterTrackROOTpip install -r requirements.txtcd CenterTrackROOTsrclibmodelnetworksgit clone cd DCNv2.make.shDownload pertained models for monocular 3D tracking, 80-category tracking, or pose tracking and move them to CenterTrackROOTmodels. More models can be found in Model zoo.Training cd CenterTrackROOTsrctoolsbash getmot17.shGoogle SitesReport abusePage detailsPage updated Google SitesReport abuseThis site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies.Learn moreGot it

Computer Vision, Deep Learning, Artificial superintelligence (ASI) - AIHubSearch this siteSkip to main contentSkip to navigationComputer Vision, Deep Learning, Artificial superintelligence (ASI)HomeProductCoursesMachine Learning SpecializationMachine Learning Foundations: A Case Study ApproachFSDLFull Stack Deep LearningMLOpsROSParallel Programming Modern CPPCloud-NativeIoT Scholarship FoundationTensorFlow: Data and Deployment Specialization Workshops and EventsRISC-VEdge-AI-summitEmbedded IoTTeslaAI-HardwareOpenVINO Deep LearningMetaverseWorkshopsIFA2022Book Summary commonplace bookknowledgemanagementPKMTopics and ProjectsAIHubChatGPTHow to startYouTubeYouTube IISoftwareRoadmap for Image Processing Source CodeOpenCVCPPpythonMacOSOpenCVRustcompileIoTShareVideo TrackingCameraCalibrationDRLHardwareQuantum ComputingAltCoinResumeCVApplestartupLinksamazonAboutFQApromptComputer Vision, Deep Learning, Artificial superintelligence (ASI)AIHub ChatGPT 4; Lamda; Bard; ChatGPT : PDF (your inputs): www.perplexity.ai - ChatGPT for Google: Google Bard: Connect ChatGPT to internet and get updated result: : create video: : Theres a new way to make video and podcasts. A good way.Buildt AI: VS Code Extension upload files for ChatGPT Build an AI chatbot trained on your data: HeyBot can be easily taught to answer questions about any topic in a friendly way. Try it out for yourself by clicking on the avatar. VALL-E: Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers: Creating images with text: Created by Creating a cloud-based image processing system with deep learning on the Amazon Web Services (AWS) platform is a complex task, but it is achievable with a combination of OpenCV, C, Internet of Things (IoT), robotics, augmented reality (AR) and virtual reality (VR), and natural language processing libraries like ChatGPT.OpenCV is a powerful library for computer vision, and it allows you to detect and track objects in images or videos. Using OpenCV, you can build an image processing system that can identify objects in an image, segment them, and apply filters or other transformations. You can also combine OpenCV with C, a powerful programming language, to make the image processing system even more powerful.The IoT aspect of the system allows it to interact with other devices, like robots, AR and VR systems, or other sensors. By connecting the system to other devices, you can enable it to perform more complex tasks, like controlling robots and interacting with an AR or VR environment.ChatGPT is a natural language processing library that can enable the cloud-based image processing system to generate text descriptions for images. By leveraging deep learning, ChatGPT can accurately recognize objects in an image and generate natural language descriptions, greatly improving the system’s accuracy and efficiency.In conclusion, building a cloud-based image processing system with deep learning on the AWS platform is possible with the right combination of tools, including OpenCV, C, IoT, robotics, AR and VR, and ChatGPT. With these tools, you can create an image processing system capable of accurately recognizing objects and generating natural language descriptions.will come soon:Quora: Turing NL G by Nvidia Sparrow by DeepMind LaMDA by Google AlexaTM by Amazon OPT by Meta Claude by Anthropic PaLM by GoogleGoogle SitesReport abusePage detailsPage updated Google SitesReport abuseThis site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By clicking “accept”, you agree to its use of cookies. Cookie PolicyRejectAccept

Computer Vision, Deep Learning, Artificial superintelligence (ASI) - pythonSearch this siteSkip to main contentSkip to navigationComputer Vision, Deep Learning, Artificial superintelligence (ASI)HomeProductCoursesMachine Learning SpecializationMachine Learning Foundations: A Case Study ApproachFSDLFull Stack Deep LearningMLOpsROSParallel Programming Modern CPPCloud-NativeIoT Scholarship FoundationTensorFlow: Data and Deployment Specialization Workshops and EventsRISC-VEdge-AI-summitEmbedded IoTTeslaAI-HardwareOpenVINO Deep LearningMetaverseWorkshopsIFA2022Book Summary commonplace bookknowledgemanagementPKMTopics and ProjectsAIHubChatGPTHow to startYouTubeYouTube IISoftwareRoadmap for Image Processing Source CodeOpenCVCPPpythonMacOSOpenCVRustcompileIoTShareVideo TrackingCameraCalibrationDRLHardwareQuantum ComputingAltCoinResumeCVApplestartupLinksamazonAboutFQApromptComputer Vision, Deep Learning, Artificial superintelligence (ASI)Pythonglobal folder, file name, functions, const, docreadallimageinfolderrecursive Truecode docmanual progress bar for python based on number of images process. code docthis code shows information about imagecode import unittestfor the functions: for example one time create file and then detected in between all operations in this function def setUp(self): def tearDown(self)for the class: classmethod def setUpClass(cls): classmethod def tearDownClass(cls):List files and folders If you want to list directories which shows specific folder name in path in windows you can use dir s b o:n ad “farshid” farshid.txtthis command listed all directories which have “farshid” in the path and save it to the farshid.txt filein python you can use below code to search and find specific folders and files import import osimport glob configroot”C:farshid”specificdirectoriesroot”farshid.jpg”pathdirdetectioncheck”” function files glob.glob(specificdirectories, recursiveTrue)for file in files: bfile.rfind(“farshid”) pathdirdetectionfile0:b-1 if (pathdirdetection ! pathdirdetectioncheck): dirname os.path.dirname(file) print(“ Next Directories “) print(dirname) pathdirdetectioncheckpathdirdetection print(file) The source code can be found in GitHubmake -j(sysctl -n hw.physicalcpu) shiftenter - run selection menuecode- pereferences - user snippets - python.json pip freeze requirements.txt extensionsVisual Studio IntelliCodeSSH FS ext install Kelvin.vscode-sshfs command shift p -SSH SF: Create new SSH SF configuration code .zshrc Bracket Pair Colorizer 2 color () different color Prettier - Code formatter when you save. setting-(format on save) indent-rainbow shell .code Compare Folders Command p all files - if I press alt it open new tab the filecontrol open terminalCommand o open folderCommand , open settingCommand shiftenterrun one line of codeoptionshiftarrow down duplicate line in codecommand click mouse go to function command - bigger command shift P command K , command S shourcuts command L select currnt line command leftright arrow start or end of line command P go to file in search git config –global core.excludesfile .gitignorecode .gitignorebrew install pyenvbrew install poetry pyenv install 3.7.5pyenv global 3.7.5 poetry new “name of project”- go to folder - change python version if you want in the pyproject.toml pyenv global 3.7.5poetry new “pytorchpretrained”poetry installpip install –upgrade pip poetry add matplotlib numpy kubernetes10.0.0 kfp0.2.4 click7.0.0 opencv-python opencv-contrib-python imutils pylint fastapi uvicorn python-dateutil seldoncore spacy sklearn torch torchvision jupyter pycocotools cython pyyaml5.1 poetry remove torch torchvisionpip install –pre torch torchvision -f pip3 install matplotlib numpy kubernetes10.0.0 kfp0.2.4 click7.0.0 opencv-python opencv-contrib-python imutils pylint fastapi uvicorn python-dateutil seldoncore spacy sklearn torch torchvision jupyter pycocotools cython pip3 install pyyaml5.1pip3 install ‘git install -U ‘git poetry shelljupyter notebook pyenv pyenv install pyenv install 3.7.5 cd folder pyenv global 3.7.5 pyenv versions python - you can see this environments poetry install (pyproject.toml) .bash , .bashprofile , .zshrc poetry run which python poetry run jupyter lab pipenv install requests pyenv virtualenvs cv-endpoint pyenv activate cv-endpoint black python The Uncompromising Code Formatter pip install black pre-commit A framework for managing and maintaining multi-language pre-commit hooks. pip install pre-commit brew install pre-commit .pre-commit-config.yaml repos: - repo: rev: v1.8.0 hooks: - id: reorder-python-imports exclude: notebooks languageversion: python3.7 - repo: rev: 19.10b0 hooks: - id: black exclude: notebooks languageversion: python3.7 - repo: rev: v2.4.0 hooks: - id: flake8 args: ‘–ignoreE203,E266,E501,W503’, ‘–max-line-length88’, ‘–max-complexity15’, ‘–selectB,C,E,F,W,T4,B9’ exclude: notebooks languageversion: python3.7 pre-commit install pre-commit run –all-files git make file make check code . add pathimport syssys.path.append(r’C: )create Matbinim np.zeros((5,16))binim binim.astype(np.uint8)255contours, hierarchy cv2.findContours(opening, cv2.RETRLIST, cv2.CHAINAPPROXSIMPLE)np.savetxt(“01-src.txt”, im, fmt’d’, delimiter’, ‘, newline’n’, header’’, footer’’, comments’ ‘)DLLimport ctypesmydll r”C:fffffff.dll”lib ctypes.windll.LoadLibrary(mydll)remove background or minimum form imageim im - im.min()timee1 cv2.getTickCount() e2 cv2.getTickCount()time (e2 - e1) cv2.getTickFrequency()from scipy.signal import findpeaks peaks, out findpeaks(Nf, distance25)if peaks0 name of your device CUDA config for VSCode foldr in VSCode code-server . web python testing sudo apt-get install chromium-chromedriver from selenium import webdriver from selenium.webdriver.common.by import By from selenium.webdriver.support.ui import WebDriverWait from selenium.webdriver.support import expectedconditions as EC import time Launch the browser and open the website driver webdriver.Chrome() driver.get(“ Save a screenshot of the page driver.savescreenshot(“tiziran.png”) Close the browser driver.quit()sudo apt install virtualenv virtualenv –pythonpython3.8 tiziransource tiziranbinactivatesudo apt-get install python3.8sudo rm usrbinpython3sudo ln -s usrbinpython3.8 usrbinpython3sudo apt-get –reinstall install python3-minimal tasks.json ”version”: “2.0.0”, ”tasks”: ”label”: “build”, ”type”: “shell”, ”command”: “usrlocalcudabinnvcc”, ”args”: ”-gencode”, ”archcompute53,codesm53”, ”-IworkspaceFolder”, ”-o”, ”workspaceFolderfileBasenameNoExtension”, ”file”, ”-g” , ”group”: ”kind”: “build”, ”isDefault”: true , ”presentation”: ”echo”: true, ”reveal”: “always”, ”focus”: false, ”panel”: “shared” , ”problemMatcher”: ”owner”: “cpp”, ”fileLocation”: “absolute”, ”pattern”: ”regexp”: “(.):(d):(d):s(warningerror):s(.)”, ”file”: 1, ”line”: 2, ”column”: 3, ”severity”: 4, ”message”: 5 launch.json ”version”: “0.2.0”, ”configurations”: ”name”: “(gdb) Launch”, ”type”: “node”, ”request”: “launch”, ”cwd”: “workspaceFolder”, ”program”: “homefarshidcodevscode-test1testCUDA”, ”args”: , ”stopOnEntry”: false, ”runtimeExecutable”: “usrbingdb”, ”runtimeArgs”: ”–interpretermi2”, ”-ex”, “set confirm off”, ”-ex”, “tui enable”, ”-ex”, “set startup-with-shell off”, ”-ex”, “set substitute-path usrsharegdb usrlocalcudabin”, ”-ex”, “file workspaceFolderfileBasenameNoExtension”, ”-ex”, “run”, ”–quiet” , ”env”: , ”console”: “integratedTerminal”, ”preLaunchTask”: “build” ccppproperties.json ”configurations”: ”name”: “Jetson Nano - Debug”, ”includePath”: ”workspaceFolder” , ”defines”: , ”compilerPath”: “usrlocalcudabinnvcc”, ”cStandard”: “c11”, ”cppStandard”: “c17”, ”intelliSenseMode”: “gcc-x64”, ”compilerArgs”: ”-g”, ”-O0”, ”–compiler-options”, ”-Wall”, ”-Wextra”, ”-Wpedantic”, ”-Wno-deprecated-gpu-targets” , ”version”: 4 VSCode on iPad Pro00:44 Installing NodeJS01:30 Install code-server02:32 Default configuration04:08 Connecting from Blink05:53 Full screen Safari07:10 Re-enable password authentication07:34 Auto start code-server09:38 Installing extensions12:10 Secure mode Google SitesReport abusePage detailsPage updated Google SitesReport abuseThis site uses cookies from Google to deliver its services and to analyze traffic. 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Computer Vision, Deep Learning, Artificial superintelligence (ASI) - AI-HardwareSearch this siteSkip to main contentSkip to navigationComputer Vision, Deep Learning, Artificial superintelligence (ASI)HomeProductCoursesMachine Learning SpecializationMachine Learning Foundations: A Case Study ApproachFSDLFull Stack Deep LearningMLOpsROSParallel Programming Modern CPPCloud-NativeIoT Scholarship FoundationTensorFlow: Data and Deployment Specialization Workshops and EventsRISC-VEdge-AI-summitEmbedded IoTTeslaAI-HardwareOpenVINO Deep LearningMetaverseWorkshopsIFA2022Book Summary commonplace bookknowledgemanagementPKMTopics and ProjectsAIHubChatGPTHow to startYouTubeYouTube IISoftwareRoadmap for Image Processing Source CodeOpenCVCPPpythonMacOSOpenCVRustcompileIoTShareVideo TrackingCameraCalibrationDRLHardwareQuantum ComputingAltCoinResumeCVApplestartupLinksamazonAboutFQApromptComputer Vision, Deep Learning, Artificial superintelligence (ASI)AI-HardwareWhat I learn from AI HARDWARE EUROPE SUMMIT (July 2020): Very short summary of the presentations in 3 days. You can access to all presentation hereSummary of AI HARDWARE EUROPE SUMMIT (July 2020)Some of the problems FPGAs solve:- excessive heat- electricity consumption- resistance to environmental factors and motion- lifespan The goal is artificial general intelligence (AGI). AI software and hardware should work together to achieve this goal. A lot of research in this area work with new methods of algorithms and hardware together to achieve high performance. Most of new hardware working with TensorFlow and Pytorch. In most case new hardware come with software solution that have high performance in special use cade or scenario. However, the new hardware can be modify by programmer like Embedded FPGA (eFPGA) in order to implement their requirement. Some of the best presentations are: Machine Intelligent Systems & Software by Victoria Rege from Graphcore, Leveraging sparsity to enable ultra-low latency inferences demonstrated using GrAI One by Orlando Moreira and Remi Poittevin from GrAI Matter Labs, Challenges for Using Machine Learning in Industrial Automation by Ingo Thon from Siemens Convolutional autoencoder for anomaly detection, From Training to Production Inference for Automotive AI - Transforming Research into Reality ( From laboratory training to automotive inference: the realities of embedding AI) by Tony King-Smith and Marton Feher from AIMotive ,Towards Embedded Intelligence by Michaela Blott from Xilinx, and the last good presentation is MLIR: Accelerating Artificial Intelligence by Albert Cohen from Google. Some key words for the event: FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs, The MLIR project is a novel approach to building reusable and extensible compiler infrastructure, Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services at mlperf.org, Graphcore IPU which is the new type of processor for AI with very high performance. Day1:Day 2:Day 3:OPENING KEYNOTE: Past Chip Childhood and System Teenage: Why We Need to Build a Mature EcosystemOlivier Temam - DeepMindPRESENTATION: Power and Cost Efficient High Performance Inference at the EdgeGeoff Tate - Flex LogixPRESENTATION: Machine Intelligent Systems & SoftwareVictoria Rege - GraphcorePANEL: High Performance and Low Energy Consumption: Developments in Performing at the EdgeModerator - Luca Benini - ProfessorOrr Danon - Hailo TechnologiesEric Flamand - GreenWaves TechnologiesAsk an Analyst: Moderated Q&A and Group DiscussionMichael Azoff - Kisaco ResearchBrett Simpson - Arete ResearchPRESENTATION: Leveraging sparsity to enable ultra-low latency inferences demonstrated using GrAI OneOrlando Moreira - GrAI Matter LabsRemi Poittevin - GrAI Matter LabsPANEL: Investment Trends & Dynamics in AI Hardware and the Startup EcosystemModerator - Brett Simpson - Arete ResearchChristian Patze - M VenturesSascha Fritz - Robert Bosch Venture Capital GmbHPRESENTATION: Challenges for Using Machine Learning in Industrial AutomationIngo Thon - SiemensPANEL: Designing Safe, Power-Efficient and Affordable Autonomous SystemsModerator - Robert Krutsch - ZenuityArnaud Van Den Bossche - NXPGordon Cooper - SynopsysASK AN EXPERT: Moderated Q&A and Group Discussion About Possibilities For Real Time AI enabled by Edge ComputeEric Flamand - GreenWaves TechnologiesPRESENTATION: From Training to Production Inference for Automotive AI - Transforming Research into RealityTony King-Smith - AI Motive Marton Feher - AIMotivePRESENTATION: Neuromorphic Computing at BMWOliver Wick - BMWPANEL: Applications of Neuromorphic Hardware in Industry, Automotive & RoboticsModerator - Yulia Sandamirskaya - IntelChristian Mayr - TU DresdenSteve Furber - University of ManchesterPRESENTATION: Computer Architecture - The Next Step: Energy Efficient Machine LearningUri Weiser - TechnionPRESENTATION: Energy Efficient AI and Carbon OffsettingRick Calle - Microsoft PRESENTATION: Edge Processors for Deep Learning - Practical PerspectivesOrr Danon - Hailo TechnologiesPRESENTATION - Why heterogenous multi-processing is a critical requirement for Edge Computing: Example of AutomotiveEric Baissus - KalrayPRESENTATION: Towards Embedded IntelligenceMichaela Blott - XilinxPRESENTATION: MLIR: Accelerating Artificial IntelligenceAlbert Cohen - Google OPENING KEYNOTE: Past Chip Childhood and System Teenage: Why We Need to Build a Mature EcosystemOlivier Temam - DeepMindTemam google.com AI progress comes at a heavy computingDRL, AutoML, AGI need higher computing requirementsChanelle:Hyper focused on chipsIdiosyncratic hardwareAI algorithms evolve very fastEfficiencyflexibility tradeoff He talk about AI chip and said AI progress comes at a heavy computing. Algorithm like DRL, AutoML, AGI need higher computing requirements. The challenge on AI chip is tradeoff between efficiency and flexibility, AI algorithms evolve very fast which change the methods of processing, Idiosyncratic hardware for specific domain, hyper focused on chips not in the system. I am impress about “Accelerator Benchmarking on Read Edge-inference Applications” presentation at AI HW summit and I like to see live demo of “ InferX X1” on October. Do you have any benchmarking of InferX X1 for the deep reinforcement learning? PRESENTATION: Power and Cost Efficient High Performance Inference at the EdgeGeoff Tate - Flex Logix-; geoffflex-logix.comAccelerator Benchmarking on Read Edge-inference Applications, He talk about Embedded FPGA (eFPGA) and new product which is “inferX X1” that will come soon. It use TDP:7-13W compare to Nvidia Xavier NX 15W. He mentioned The ResNet-50 with the image size of 224224 will not tell you the robustness of the memory system that been required with megapixel images. Thats why not good for comparison the best for customer to use and compare is YOLOv3. YOLOv3 intermediate activations are 64MB peak for 2 Megapixel images, this stresses memory subsystems much more than ResNet-50. Their chip is good because of efficiency is in Data packing and transposition. It allowed efficiency 3D convolutions which for each layer ad dedicated path RAM to compute to RAM and deep layer fusion reduces memory requirement and DRAM access in “hidden” in the background. Embedded FPGA, eFPGA, clustering MACs with a reconfigurable interconnect delivered high inference throughput at low cost, inferX X1 in fab now TDP:7-13W (compare to Nvidia Xavier NX 15W), not Pytorch, the best for customer to use and compare is YOLOv3. YOLOv3 intermediate activations are 64MB peak for 2 Megapixel images, this stresses memory subsystems much more than ResNet-50. They said the normal default image size is 224224 but the activation intermediate (max layer) growth exponentially by increasing the size of images. The ResNet-50 with the image size of 224224 will not tell you the robustness of the memory system that been required with megapixel images. Thats why not good for comparison. Nvidia Xavier NX: has 3 inference (GPU, 2x DLA):self-driving car multiple models are running simultaneously But in most AI system one camera one model one system process images frame per frame. If we have stream of data coming in 15FPS one image at time 15FPS Key to inferX X1 efficiency is in Data packing and transposition, efficiency 3D convolutions , for each layer ad dedicated path RAM to compute to RAM, deep layer fusion reduces memory requirement, DRAM access in “hidden” in the background, TSMC,GUC,synopsys,arteris,analog bits, cadence, mentor I am impress about “Machine Intelligent Systems & Software GRAPHCORE IPU” presentation at AI HW summit. Do you have any benchmarking of GRAPHCORE IPU for the deep reinforcement learning?PRESENTATION: Machine Intelligent Systems & SoftwareVictoria Rege - Graphcore; victoriagraphcore.ai infographcore.ai ; fleurdevie This presentation is one of the best. She talk about GRAPHCORE IPU(INTELLIGENCE PROCESSING UNIT) and POPLAR SDK. The Graphcore IPU can run training of sample model in 3 hours which require 40 hours on GPU. In another use case Graphcore IPU accelerated medical imaging on azure can process 2000 imagessec compare to 166 imagessec on GPU. GRAPHCORE IPU(INTELLIGENCE PROCESSING UNIT)The poplar SDK, (40 hours on GPU to 3 hours)Running a PyTorch model on the Graphcore IPU: ResNeXt-101 example Ipu accelerated medical imaging on azure (IPU 2000 imagessec vs 166 imagessec) PANEL: High Performance and Low Energy Consumption: Developments in Performing at the EdgeModerator - Luca Benini - Professor: ML processorsOrr Danon - Hailo TechnologiesEric Flamand - GreenWaves Technologies Ask an Analyst: Moderated Q&A and Group DiscussionMichael Azoff - Kisaco ResearchBrett Simpson - Arete Research PRESENTATION: Leveraging sparsity to enable ultra-low latency inferences demonstrated using GrAI OneOrlando Moreira - GrAI Matter LabsRemi Poittevin - GrAI Matter Labs Edge workloads involve real-time: responsive smart devices, closely coupled feedback loops, autonomous systemsInput data streams are continuous: videoAudio feeds, industrial sensor ensembles, bio signals (EEG,EKG, movement) The data rate is much higher than the real information rate:voice 512- information 39 bitssUXGA video 79 MBs - information 95Frame based processing: apply single frame algorithm independently to each input frame in a stream.Advantages:Many popular sensors are frame basedSimple, easy to scale: image - video streamDisadvantage:Repeated and redundant data is processed over and over Sparsity in videoSparsity in structure: pruning of needless weights and kernels in networkSparsity in space: most pixels in an image have no relevant feature data. ; results in 0-valued activationsSparsity in time: image changes little from instant to instant; why should we always re-process the whole frame? Event based computation of networks (process only the data that change); single events in an input layer fan out (typically 1:9 to 1:49 per feature map); only the affected pixels in the convolutional layer need be computed. ; locality of change is preserved downstream. ; the events than fan in typically 4:1 in pooling layers. ; additionally, events are only 25 likely to change the pixel state (tpy. 22 max pooling).; locality of change is preserved downstream. ; sparNet: sparse and event-based execution modelExploits time-sparsity in a time seriesConverts frame. Based network to evet based inferenceEvent based: change is sent sporadically, so no frame structure to input dataOnly propagates changes, thus less work needs to be doneRequires resilient neuron stateThreshold: per neuron, defines how much change is needed to warrant propagation.To convert a CNN to sparNet, they set a threshold per neuron Pilotnet in sparnetExecution pilotnet with sparnet dramatically reduces the number of operations required.Effect becomes dramatic at high fps:Same amount change per same time intervalBut for frame based processing, load increases linearly with frame rateHigher fps lower sampling period lower latency Consequences of sparsity for computer architecture:Requires that they store resilient neuron statesSuggests innear memory computationFrame structure is lost:Event based scheduling of computationSuggests data flow synchronizationSignificantly less sequential memory accesses occurReduced value of caching, network bursts, bulk dma transfers;Reduced opportunities for latency hidingSuggests innear memory computation Conclusion:Neuronflow is designed to exploit sparsitySparsity in structureNeuronflow:Event based activation skips0-weight(pruned) synapseskernels.Sparsity in spaceNeuronflow: 0-valued activations are neither sent nor processed.Sparsity in timeNeuronflow: if change between frames is below threshold, it is neither sent nor processed. Neuron state to 200 to store the space.. PANEL: Investment Trends & Dynamics in AI Hardware and the Startup EcosystemModerator - Brett Simpson - Arete ResearchChristian Patze - M VenturesSascha Fritz - Robert Bosch Venture Capital GmbH Day 2 PRESENTATION: Challenges for Using Machine Learning in Industrial AutomationDr. Ingo Thon - Siemens- Ingo.Thonsiemens.com He presented some challenge in hardware. Some key notes are time series data chip is missing, AI should sit at hardware level,Imagine automating the unpredictable. Drivers for new developments in industries: time to market, flexibility (PID), quality, efficiencyConvolutional autoencoder for anomaly detection; reptile algorithm;trick is wide product in line after trained can used pre trained to adept to other typeCost come on reliabilityperformanceeasy to use (man power) - Drivers for new developments in industries: time to market, flexibility (PID), quality, efficiencyAI should site at HW levelImagine automating the unpredictable Visual quality inspection solved but hard:Convolutional autoencoder for anomaly detection; reptile algorithm;trick is wide product in line after trained can used pre trained to adept to other typeCost come on reliabilityperformanceeasy to use (man power) - Time series data chip is missing PANEL: Designing Safe, Power-Efficient and A; ffordable Autonomous SystemsModerator - Robert Krutsch - ZenuityArnaud Van Den Bossche - NXPGordon Cooper - SynopsysASK AN EXPERT: Moderated Q&A and Group Discussion About Possibilities For Real Time AI enabled by Edge ComputeEric Flamand - GreenWaves Technologies Watch againPRESENTATION: From Training to Production Inference for Automotive AI - Transforming Research into RealityTony King-Smith - AI Motive Marton Feher - AIMotiveFrom laboratory training to automotive inference: the realities of embedding AI Convolution is Not the same as matrix multiplication. Matrix multipliers used extensively in GPUs and DSPs - so many algorithm implementations use them BUT matrix multipliers need pre and post processing to re order data for convolution. Need to accelerate the NN algorithms, not just implementations of them. Convolution is Not the same as matrix multiplicationMatrix multipliers used extensively in GPUs and DSPs - so many algorithm implementations use them BUT matrix multipliers need pre and post processing to re order data for convolution. Need to accelerate the NN algorithms, not just implementations of them. Manually optimization; Convolution 55 kernel ; relu 55 conv and 55 de conv ; PRESENTATION: Neuromorphic Computing at BMWOliver Wick - BMW; oliver.wickbmw.deNeuromorphic computing. Building up a neuromorphic computing readiness for BMW. PANEL: Applications of Neuromorphic Hardware in Industry, Automotive & RoboticsModerator - Yulia Sandamirskaya - IntelChristian Mayr - TU DresdenSteve Furber - University of Manchester Day 3 —PRESENTATION: Computer Architecture - The Next Step: Energy Efficient Machine LearningProfessor Uri Weiser - Technion — Technical hardware talk. Deep learning is everywhere: pedestrian detection, vehicle detection, collision avoidance, parking assist, speech understanding, platetraffic sign detection, passenger control, face recognition. We are at the beginning stages of comprehending the environment and where we are. In AI hardware the Performance is the king and the efficiency is the next step. Spatial correlation and value prediction in convolutional neural networksNon blocking simultaneous multithreading: embracing the resiliency of deep neural network ML architecture is resilient to inaccuracies - SMT is suitable in DNN approximation environment Why resNet for benchmarking? and useful benchmarks for measuring training and inference performance of ML hardware, software, and services. PRESENTATION: Energy Efficient AI and Carbon OffsettingRick Calle - Microsoft ; M2 the venture Arm of MicrosoftWhat can AI industry do to reduce AI computational energy? (in a world of trillion X?) Paper:M12 meta analysis of research papers: energy and ploicy considerations for deep learning in NLPLanguage models are few shot learners (OpenAI GPT-3)BERTology, learning and evaluationg general linguistic intelligence (Google) —PRESENTATION: Edge Processors for Deep Learning - Practical PerspectivesOrr Danon - Hailo TechnologiesDNN accelerators in the wild; use case driven overview Video analytics platforms pipeline start with frame grab. Then, analyse 1 which consists of detection, quality classification and grip orientation. Then, analyse 2 which consist of decision logic. Finally, act which consists of pick and place. PRESENTATION - Why heterogenous multi-processing is a critical requirement for Edge Computing: Example of AutomotiveEric Baissus - Kalray: new type processor and solutions, 3rd MPPA processor,Investors: nxp, renault nissan mistubishi, safran, mbda Multicore and many core processors: homogeneous multicore processor (mix of FPGA,GPU,ASIC,CPUs), PGGPU manycore processor, CPU based manycore processor. Only 25 of usable data will reach a data centre the 75 need to be analysed locally in real time. I would like to know more about FINN compiler.PRESENTATION: Towards Embedded Intelligence: opportunities and challenges in the technology landscapeMichaela Blott - Xilinx In memory computing; waverscale computing specialized architectures DPU; How can enable a broader spectrum of end-users to be able to specialize hardware architectures and co design solutions? FINN(10k-10M FPS); logicNets (100M FPS) Innovative architectures emerge to address the needs of embedded intelligence; specialization of hardware architecture are key; with more flexibility, more opportunity to customization (potential to exploit with FPGAs and ACAPs, allow to specialize to the specifics of individual use cases; tools such as FINN are needed to address of complexity in the design entry); future: key challenge in the community remains around how to compare (focussed on embedded; PRESENTATION: MLIR: Accelerating Artificial IntelligenceAlbert Cohen - GoogleMlir-hiringgoogle.com A new golden age for computer architecture, a call to action for software stack construction compilers, execution environments, toolsMLIR: Multi Level Intermediate RepresentationMlir.llvm.org Compiler research ; unification ; ; Price rate is 999 VAT. you have the opportunity to access all 19 presentations and panel discussions on-demand for the cost of only 149VAT. Register online and receive immediate access. Appendix: Software and AIMLSPARTRONIXSoftware and AIML Either for soft-processors (Microblaze, NIOS) or physical microcontrollers.Bare metal, RTOS or Linux-based applications.Software deployed Neural NetworksReal Time Operating Systems (RTOS) FreeRTOS, VxWorks, pSOS, Ecos, Nucleus, ProprietaryVast experience with RTOSMicroprocessorsMicrocontrollers x86 68x Freescale Power ARch Tech ARM MIPS SuperH Symbian XScaleEmbedded Operating Systems Linux WinCE Windows Embedded CE.NET 4.x QNX SymbianWe Love LinuxApplication & Kernel Dev. Embedded Linux Windows CE VxWorks ThreadX QNXCustom BSP and Driver Dev. Embedded Linux Windows CE VxWorks ThreadX QNXTailor-madeCustom Driver Development Network & Communications Storage Drivers Device Drivers ExperienceAI DetectionRecognition of objects and faces ADAS Security Data centersand moreExperts in AIMLReal Time Operating Systems (RTOS): FreeRTOS, VxWorks, pSOS, Ecos, Nucleus, Proprietary : FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs.Mlir.llvm.org : The MLIR project is a novel approach to building reusable and extensible compiler infrastructure. 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Computer Vision, Deep Learning, Artificial superintelligence (ASI) - CameraCalibrationSearch this siteSkip to main contentSkip to navigationComputer Vision, Deep Learning, Artificial superintelligence (ASI)HomeProductCoursesMachine Learning SpecializationMachine Learning Foundations: A Case Study ApproachFSDLFull Stack Deep LearningMLOpsROSParallel Programming Modern CPPCloud-NativeIoT Scholarship FoundationTensorFlow: Data and Deployment Specialization Workshops and EventsRISC-VEdge-AI-summitEmbedded IoTTeslaAI-HardwareOpenVINO Deep LearningMetaverseWorkshopsIFA2022Book Summary commonplace bookknowledgemanagementPKMTopics and ProjectsAIHubChatGPTHow to startYouTubeYouTube IISoftwareRoadmap for Image Processing Source CodeOpenCVCPPpythonMacOSOpenCVRustcompileIoTShareVideo TrackingCameraCalibrationDRLHardwareQuantum ComputingAltCoinResumeCVApplestartupLinksamazonAboutFQApromptComputer Vision, Deep Learning, Artificial superintelligence (ASI)CameraCalibrationMulti Camera (Stereo Vision) Calibration for ARVR headset (extended realitymixed reality) 3D Image Processing with Deep LearningintroductionSource codeReferenceMulti Camera (Stereo Vision) Calibration for ARVR headset (extended realitymixed reality) 3D Image Processing with Deep LearningintroductionGeometric camera calibration, also referred to as camera re-sectioning, estimates the parameters of a lens and image sensor of an image or video camera. These parameters can be used to correct for lens distortion, measure the size of an object in world units, or determine the location of the camera in a scene. These tasks are used in applications such as machine vision to detect and measure objects. They are also used in robotics, navigation systems, and 3-D scene reconstruction. Without any knowledge of the calibration of the cameras, it is impossible to do better than projective reconstruction (MathWorks).Non-intrusive scene measurement tasks, such as 3D reconstruction, object inspection, target or self-localization or scene mapping require a calibrated camera model (Orghidan et al. 2011). Camera calibration is the process of approximating the parameters of a pinhole camera model (Tsai 1987; Stein 1995; Heikkila & Silven 1997) of a given photograph or video.There are four main categories of camera calibration methods whereby a number of algorithms have been proposed for each categoriesmethods, namely knowing object based camera calibration, semi auto calibration, camera self-calibration method, and camera calibration method based on active vision. In computer vision methods, image information from cameras can yield geometric information pertaining to three-dimensional objects. Non-intrusive scene measurement tasks, such as 3D reconstruction, object inspection, target or self-localization, or scene mapping require a calibrated camera model (Orghidan et al. 2011). The correlation between the geographical point and camera image pixel is necessary for camera calibration. Hence, the cameras parameter, which constitutes the geometric model of camera imaging, are utilized to establish the correlation between the three-dimensional geometric location of one point and a corresponding point in an image (Wang et al. 2010). Typically, experiments are conducted to attain the aforementioned parameters and relevant calculation, which is a process called camera calibration (Hyunjoon et al. 2014; Jianyang et al. 2014; Mohedano et al. 2014; Navarro et al. 2014).Image information from cameras can be used to elucidate the geometric information of a 3D object. The process of estimating the parameters of a pinhole camera model is called camera calibration. The more accurate the estimated parameters, the better the compensation that can be performed for the next stage of the application. In the data collection stage, a camera will take photos of a camera calibration pattern(Tsai 1987; Stein 1995; Heikkila & Silven 1997; Zhengyou 2000). Another angle of the issue is to create a set of pair images from both cameras via high quality images and increased range of slope of calibration pattern. The current methods simply create images upon the detection of calibration pattern. Nonetheless, the consensus in literature is that accurate camera calibration necessitates pure rotation (Zhang et al. 2008) and require sharp images. Recent breakthrough methods, such as Zhangs (Zhengyou 2000), use fixed threshold to elucidate pixel difference between the frames and pre-setting variables, where slope information for image frame selection in camera calibration phase has been neglected (Audet & Okutomi 2009). Conversely, these approaches become less reliable when image frames are blurred. These problems necessitates that the camera calibration algorithm be enhanced (Wang et al. 2010).OpenCVDeep Learning Engineering of Camera CalibrationOccasionally the out-of-the-box solution does not work, and you need some modified version of the algorithms.The first step of camera calibration is using known pattern images, such as chessboard. However, sometimes the image quality and pattern are not match with standard approach of calibration process.I use some other technique to enhance the result. In the first step, we need to improve the corner detection, and it may be done by fallowing steps. The chessboard is used as a pattern of alternating black and white squares, - which ensures that there is no bias toward one side or the other in measurement. The image must be an grayscale (single-channel) image. - img - Input image. It should be grayscale and float32 type. gradianet x and y direction together (for better detection) - cv.morphologyEx( src, op, kernel, dst, anchor, iterations, borderType, borderValue ) - dst different kernel is required using Harris corner detection, which is a matrix of the second-order derivatives of the image intensities. - cv.cornerHarris( src, blockSize, ksize, k, dst, borderType ) - dst the parameters a and b and c should be modified img - Input image. It should be grayscale and float32 type. blockSize - It is the size of neighborhood considered for corner detection ksize - Aperture parameter of the Sobel derivative used. k - Harris detector free parameter in the equation. contours to remove some noise: - cv.connectedComponentsWithStats( image, labels, stats, centroids, connectivity, ltype ) - retval, labels, stats, centroids subpixel corners: corner detection come with integer coordinates but sometimes require real-valued coordinates cv.cornerSubPix( image, corners, winSize, zeroZone, criteria ) - corners - image Input single-channel, 8-bit or float image. - corners Initial coordinates of the input corners and refined coordinates provided for output. - winSize Half of the side length of the search window. (55 will be 11) - zeroZone It is used sometimes to avoid possible singularities of the auto correlation matrix. - criteria Criteria for termination of the iterative process of corner refinement. remove duplicate corners: for example corners are in less than 5 pixels should be removeReference: more: If you found the content informative, you may Follow me by LinkedIn, twitter, for more!  codeBasic camear calibration source code by using OpenCV library in Jupyter notebook ReferenceSemi-Auto Calibration for multi-camera system (Pirahansiah’s method 2022) prognostic analysis using QR code in center of calibration pattern with four different colors in each courners of the QR code for show the direction which use for sincronize the points for all cameras)Book Chapter (Springer):Camera Calibration and Video Stabilization Framework for Robot Localization paper:Pattern image significance for camera calibration calibration for multi-modal robot vision based on image quality assessment 3. Basic of camera calibration source code (PythonOpenCV) Geometric camera calibration, also referred to as camera re-sectioning, estimates the parameters of a lens and image sensor of an image or video camera. These parameters can be used to correct for lens distortion, measure the size of an object in world units, or determine the location of the camera in a scene. These tasks are used in applications such as machine vision to detect and measure objects. They are also used in robotics, navigation systems, and 3-D scene reconstruction. Without any knowledge of the calibration of the cameras, it is impossible to do better than projective reconstruction (MathWorks).Non-intrusive scene measurement tasks, such as 3D reconstruction, object inspection, target or self-localization or scene mapping require a calibrated camera model (Orghidan et al. 2011). 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Computer Vision, Deep Learning, Artificial superintelligence (ASI) - SoftwareSearch this siteSkip to main contentSkip to navigationComputer Vision, Deep Learning, Artificial superintelligence (ASI)HomeProductCoursesMachine Learning SpecializationMachine Learning Foundations: A Case Study ApproachFSDLFull Stack Deep LearningMLOpsROSParallel Programming Modern CPPCloud-NativeIoT Scholarship FoundationTensorFlow: Data and Deployment Specialization Workshops and EventsRISC-VEdge-AI-summitEmbedded IoTTeslaAI-HardwareOpenVINO Deep LearningMetaverseWorkshopsIFA2022Book Summary commonplace bookknowledgemanagementPKMTopics and ProjectsAIHubChatGPTHow to startYouTubeYouTube IISoftwareRoadmap for Image Processing Source CodeOpenCVCPPpythonMacOSOpenCVRustcompileIoTShareVideo TrackingCameraCalibrationDRLHardwareQuantum ComputingAltCoinResumeCVApplestartupLinksamazonAboutFQApromptComputer Vision, Deep Learning, Artificial superintelligence (ASI)Software List of application for workOneNoteOneDriveNotionGoogle (Gmail, Calendar, Meet, Doc, Sheet, …)Collaborate for free with online versions of Microsoft Word, PowerPoint, Excel, Outlook,Teams and OneNote Code Quality and Code Security Rocket.Chat: Communications Platform You Can Fully TrustJitsi Meet Components OktaGitHubAmazon DriveDiscordList of application for work in phoneOkta VerifyAuthyAuthenticatorWebex MeetZoomHabitShareMeetupDrafts Peeking Into Peoples Second Brains: 6 Videos to Inspire Your Second Brain SetupGoogle SitesReport abusePage detailsPage updated Google SitesReport abuseThis site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies.Learn moreGot it

Computer Vision, Deep Learning, Artificial superintelligence (ASI) - startupSearch this siteSkip to main contentSkip to navigationComputer Vision, Deep Learning, Artificial superintelligence (ASI)HomeProductCoursesMachine Learning SpecializationMachine Learning Foundations: A Case Study ApproachFSDLFull Stack Deep LearningMLOpsROSParallel Programming Modern CPPCloud-NativeIoT Scholarship FoundationTensorFlow: Data and Deployment Specialization Workshops and EventsRISC-VEdge-AI-summitEmbedded IoTTeslaAI-HardwareOpenVINO Deep LearningMetaverseWorkshopsIFA2022Book Summary commonplace bookknowledgemanagementPKMTopics and ProjectsAIHubChatGPTHow to startYouTubeYouTube IISoftwareRoadmap for Image Processing Source CodeOpenCVCPPpythonMacOSOpenCVRustcompileIoTShareVideo TrackingCameraCalibrationDRLHardwareQuantum ComputingAltCoinResumeCVApplestartupLinksamazonAboutFQApromptComputer Vision, Deep Learning, Artificial superintelligence (ASI)startupAccelerators and Incubators - These are programs that provide funding, mentorship, and resources to help startups grow and scale.Startup LabsLaunchpadsInnovation CentersVenture StudiosSeed FundsInnovation HubsEntrepreneurship CentersCo-creation SpacesInnovation WorkshopsStartup CommunitiesGermany:TopOther Rocket Internet Wayra - A startup accelerator backed by Telefnica that provides funding, mentorship, and access to a global network of investors.Axel Springer Plug and Play - A startup accelerator focused on media, advertising, and digital content.High-Tech Grnderfonds - A seed fund that invests in technology startups in various sectors, including software, hardware, and engineering.Berlin Startup Academy - A startup accelerator that offers a 3-month program of mentorship, workshops, and networking opportunities.Startupbootcamp Berlin - A startup accelerator that offers a 3-month program focused on fintech, e-commerce, and smart transportation.Factory Berlin - A co-working and innovation hub that provides resources and support for startups in various industries.Founders Factory - A startup accelerator and incubator that offers mentorship, funding, and access to a global network of investors.Next Big Thing AG - A startup incubator that focuses on the Internet of Things (IoT) and connected devices.German Tech Entrepreneurship Center (GTEC) - A startup incubator and accelerator that offers programs and resources for entrepreneurs in various sectors, including fintech, healthtech, and mobility.Factory Berlin: Factory Berlin is a co-working space that provides startups and entrepreneurs with access to a supportive community, events, and resources.Berlin Startup Incubator: Berlin Startup Incubator offers startups in the technology sector access to mentorship, funding, and office space.Betahaus: Betahaus is a co-working space that offers startups and entrepreneurs access to a community of like-minded individuals, events, and resources.The Family: The Family is a startup accelerator that offers entrepreneurs mentorship, funding, and resources to help them build successful businesses.Venture Capital Firms - These firms provide funding and support to startups in exchange for equity in the company.Co-Working Spaces - These spaces provide a physical location for startups to work and collaborate with other entrepreneurs.Startup Consulting Firms - These firms provide guidance and advice to startups on a range of topics, such as business strategy, marketing, and operations.Business Plan Writers - These professionals can help startups create a comprehensive business plan that outlines their goals, strategies, and financial projections.Profit: Accelerators and incubators can earn money in a variety of ways, depending on their business model. Some common revenue streams for accelerators and incubators include:Sponsorship - Many accelerators and incubators are sponsored by corporations, foundations, or government agencies, who provide funding in exchange for branding, marketing, or other benefits.Equity Investment - Some accelerators and incubators do take equity in the startups they support, which allows them to earn a return on their investment if the startup is successful.Program Fees - Some accelerators and incubators charge startups a fee to participate in their programs, which may include access to mentorship, resources, or networking opportunities.Consulting or Advisory Services - Some accelerators and incubators may offer consulting or advisory services to startups for a fee.Event or Conference Revenue - Some accelerators and incubators may host events or conferences, which can generate revenue through ticket sales, sponsorships, or exhibitor fees.grant loanA grant or a loan can be an attractive option for startups that are looking for funding but do not want to give up equity in their company.A grant is a sum of money that is given to a startup with no obligation to pay it back. Grants are often offered by government agencies, non-profit organizations, or foundations that want to support innovation and entrepreneurship in a particular sector or industry. Grants can be highly competitive, and startups typically have to submit a detailed proposal outlining their business plan, goals, and how they plan to use the funds.A loan, on the other hand, is a sum of money that is borrowed from a lender with the obligation to pay it back over a specified period of time, usually with interest. Loans can be offered by banks, government agencies, or private investors. Startups typically have to submit a detailed business plan and financial projections to qualify for a loan. Loans can be a good option for startups that have a solid plan for revenue generation but need some initial capital to get started.Both grants and loans can provide startups with much-needed funding to help them get off the ground. However, it’s important to carefully consider the terms and conditions of any funding agreement before accepting it. Startups should make sure that they understand the repayment terms, interest rates, and any other fees or requirements associated with the grant or loan.sole proprietorship (Einzelunternehmen)Investor Database0. fundraising content 1. alternative datases accelerator incubator business angels competitions conferences investors investor matching public funding startups2. alternative funding options crowd investments europe family offices europepubluic funding germany3. programs accelerator incubator DACH-Region Company builder DACH-Region innovation labs DACH-Region4. business angels business angels germany business angels europe carta5. VCs corporate venture capital europe venture capital europe US venture capital invested in europe6. Networks coaching & mentoring different industries verticals female founder diversity founderinvestor investor reviews7. specials company setup agencies dealflow agencies europe fundraising agencies DACH-Region venture capital law firms germanyAccelerators and Incubators - These are programs that provide funding, mentorship, and resources to help startups grow and scale.Venture Capital Firms - These firms provide funding and support to startups in exchange for equity in the company.Co-Working Spaces - These spaces provide a physical location for startups to work and collaborate with other entrepreneurs.Startup Consulting Firms - These firms provide guidance and advice to startups on a range of topics, such as business strategy, marketing, and operations.Business Plan Writers - These professionals can help startups create a comprehensive business plan that outlines their goals, strategies, and financial projections.Accelerators and Incubators - These are programs that provide funding, mentorship, and resources to help startups grow and scale.Venture Capital Firms - These firms provide funding and support to startups in exchange for equity in the company.Co-Working Spaces - These spaces provide a physical location for startups to work and collaborate with other entrepreneurs.Startup Consulting Firms - These firms provide guidance and advice to startups on a range of topics, such as business strategy, marketing, and operations.Business Plan Writers - These professionals can help startups create a comprehensive business plan that outlines their goals, strategies, and financial projections.Accelerators and Incubators - These are programs that provide funding, mentorship, and resources to help startups grow and scale.Venture Capital Firms - These firms provide funding and support to startups in exchange for equity in the company.Co-Working Spaces - These spaces provide a physical location for startups to work and collaborate with other entrepreneurs.Startup Consulting Firms - These firms provide guidance and advice to startups on a range of topics, such as business strategy, marketing, and operations.Business Plan Writers - These professionals can help startups create a comprehensive business plan that outlines their goals, strategies, and financial projections.Google SitesReport abusePage detailsPage updated Google SitesReport abuseThis site uses cookies from Google to deliver its services and to analyze traffic. 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Computer Vision, Deep Learning, Artificial superintelligence (ASI) - ChatGPTSearch this siteSkip to main contentSkip to navigationComputer Vision, Deep Learning, Artificial superintelligence (ASI)HomeProductCoursesMachine Learning SpecializationMachine Learning Foundations: A Case Study ApproachFSDLFull Stack Deep LearningMLOpsROSParallel Programming Modern CPPCloud-NativeIoT Scholarship FoundationTensorFlow: Data and Deployment Specialization Workshops and EventsRISC-VEdge-AI-summitEmbedded IoTTeslaAI-HardwareOpenVINO Deep LearningMetaverseWorkshopsIFA2022Book Summary commonplace bookknowledgemanagementPKMTopics and ProjectsAIHubChatGPTHow to startYouTubeYouTube IISoftwareRoadmap for Image Processing Source CodeOpenCVCPPpythonMacOSOpenCVRustcompileIoTShareVideo TrackingCameraCalibrationDRLHardwareQuantum ComputingAltCoinResumeCVApplestartupLinksamazonAboutFQApromptComputer Vision, Deep Learning, Artificial superintelligence (ASI)ChatGPT MindMap of ChatGPT Prompt Engineering for Developers by deeplearning.ai Download source : ChatGPT introduction two model based llm predict next word, based on text training data instruction tuned llm tries to follow instructions fine-tune on instructions and good attempts at following those instructions RLHF: reinforcement learning with human feedback principles 1: write clear and specific instructions 2: give the model time to think guidelines two model based llm predict next word, based on text training data instruction tuned llm tries to follow instructions fine-tune on instructions and good attempts at following those instructions RLHF: reinforcement learning with human feedback principles 1: write clear and specific instructions Tactic 1: Use delimiters Triple quotes: Triple backticks: Triple dashes: - - - Angle brackets: XML tags: User:”) tokenizer.eostoken, returntensors’pt’) botinputids torch.cat(chathistoryids, newuserinputids, dim-1) if step 0 else newuserinputids chathistoryids model.generate(botinputids, maxlength1000, padtokenidtokenizer.eostokenid) print(“DialoGPT: “.format(tokenizer.decode(chathistoryids:, botinputids.shape-1:0, skipspecialtokensTrue)))code print(“www.tiziran.com”)from transformers import GPT2LMHeadModel, GPT2Tokenizermodelnameorpath ‘microsoftDialoGPT-medium’tokenizer GPT2Tokenizer.frompretrained(modelnameorpath)model GPT2LMHeadModel.frompretrained(modelnameorpath, timeout3000)inputtext “Hi, how are you?”inputids tokenizer.encode(inputtext, returntensors’pt’)response model.generate(inputids)outputtext tokenizer.decode(response0, skipspecialtokensTrue)print(outputtext)Google SitesReport abusePage detailsPage updated Google SitesReport abuseThis site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies.Learn moreGot it

Computer Vision, Deep Learning, Artificial superintelligence (ASI) - commonplace bookSearch this siteSkip to main contentSkip to navigationComputer Vision, Deep Learning, Artificial superintelligence (ASI)HomeProductCoursesMachine Learning SpecializationMachine Learning Foundations: A Case Study ApproachFSDLFull Stack Deep LearningMLOpsROSParallel Programming Modern CPPCloud-NativeIoT Scholarship FoundationTensorFlow: Data and Deployment Specialization Workshops and EventsRISC-VEdge-AI-summitEmbedded IoTTeslaAI-HardwareOpenVINO Deep LearningMetaverseWorkshopsIFA2022Book Summary commonplace bookknowledgemanagementPKMTopics and ProjectsAIHubChatGPTHow to startYouTubeYouTube IISoftwareRoadmap for Image Processing Source CodeOpenCVCPPpythonMacOSOpenCVRustcompileIoTShareVideo TrackingCameraCalibrationDRLHardwareQuantum ComputingAltCoinResumeCVApplestartupLinksamazonAboutFQApromptComputer Vision, Deep Learning, Artificial superintelligence (ASI)Commonplace BookSecond BrainWhy?ReferenceSecond BrainA commonplace book is a system for writing down and sorting all manner of tidbits: quotes, anecdotes, observations, and information gleaned from books, conversations, movies, song lyrics, social posts, podcasts, life experiences, or anything else that you might want to return to later.Why?A commonplace book is a system for writing down and sorting all manner of tidbits: quotes, anecdotes, observations, and information gleaned from books, conversations, movies, song lyrics, social posts, podcasts, life experiences, or anything else that you might want to return to later.Where Is It Located?What Type of Content Is This?MacControlCommandSpace barwindows win.Symbols Note (1) (orange) ToDo (2) (pink) Important ? (3) (Purple) Question ! (4) (yellow) Attentionremember for later (5) (green) New idea or direction (6) (blue) Need research (7) (brown) Management (1) (orange) ToDo my long term todo (2) (pink) Importantimportant ? (3) (Purple) Question ! (4) (yellow) Attentionremember for later (5) (green) New idea or directioncreate datasets for multi-class multi-object tracking based on self collected videos with different cameras and resolutions (6) (blue) Need research (7) (brown) Management6 minutes diaryDran Bleliben2023 Planer ReferenceMINIMALIST JOURNAL IDEAS ft. 6-Minute Diary (productivity, self love, mindfulness) Google SitesReport abusePage detailsPage updated Google SitesReport abuseThis site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies.Learn moreGot it

Computer Vision, Deep Learning, Artificial superintelligence (ASI) - Embedded IoTSearch this siteSkip to main contentSkip to navigationComputer Vision, Deep Learning, Artificial superintelligence (ASI)HomeProductCoursesMachine Learning SpecializationMachine Learning Foundations: A Case Study ApproachFSDLFull Stack Deep LearningMLOpsROSParallel Programming Modern CPPCloud-NativeIoT Scholarship FoundationTensorFlow: Data and Deployment Specialization Workshops and EventsRISC-VEdge-AI-summitEmbedded IoTTeslaAI-HardwareOpenVINO Deep LearningMetaverseWorkshopsIFA2022Book Summary commonplace bookknowledgemanagementPKMTopics and ProjectsAIHubChatGPTHow to startYouTubeYouTube IISoftwareRoadmap for Image Processing Source CodeOpenCVCPPpythonMacOSOpenCVRustcompileIoTShareVideo TrackingCameraCalibrationDRLHardwareQuantum ComputingAltCoinResumeCVApplestartupLinksamazonAboutFQApromptComputer Vision, Deep Learning, Artificial superintelligence (ASI)Embedded IoTEmbedded IoT World 2021 April 28-29 87.1 B devices; Tensorflow lite without OS in microcontroller, RISC-V, FPGA; TinyML, ; Docker for embedded; CORE-V; eFPGA, 2- Embedded IoT: Devices, Solutions Architect, Security, Safety, Standardization and certification, secured connectivity, end to end security, modern development practices, 3- Safe access of local IoT devices from the internet OAuth 2.0 & OIDC relies on TLS; MQTT, 4- Scale4Edge RISC- V edge computing ecosystem: Virtual prototyping first! Performance, Energy efficiency, low cost, quality, robustness (safety, security, reliability); virtual system prototypes(VSP); E2E AI performance assessment: NN ML-Compiler Virtual Hardware Model KPI based evaluation TC-ResNet; Sparsity extraction, microcoded data path; Machine learning on edge devices requires tailored hardwaresoftware solutionssupport and integration of different target architectures: RISC-V; UltraTrail; SpinnEDGEdeployment software flow based on TVMapplication specific vs domain specific 5 - A structured approach to comprehensive IoT security in the smart home Network Protocols: BLE, Bluetooth mesh, WiFi, Zigbee, Sub-G, GPRS, 4G5G, NB-IoT, GNSS6 - Code size compiler optimizations and techniques for embedded systems Code size of embedded application has been a concern for a very long time. While storage becomes cheaper and smaller, developers find creative ways to increase code size by adding features or unnecessary software engineering. Compilers have come a long way in optimizing applications for code size. While most compiler optimization work were focused on application performance, we have seen increase in the code size optimizations in recent years. This session will cover classical as well as recent compiler optimizations for code size, a few of which Aditya has implemented in the LLVM compiler. Some optimizations (hot cold splitting, function entry instrumentation) require collecting data from the field while the application is running. The presentation will provide an overview of how those compiler techniques help reduce code size. We will also explore some tips and techniques (compiler flags to reduce code size, tuning of compiler options like inline threshold), that help reduce binary size. Having knowledge of the code generated by the compiler and the instruction set architecture can help engineers chose appropriate programming abstractions and idioms. Optimize applications for code size using available compiler techniques and software engineering techniques ; Various code-size and performance trade offs ; Understanding the code size requirements embedded application. code size optimization flags:-Os-Oz (only in llvm)-fno-unroll-loops-fno-exceptions-fno-rtti-fno-jump-tables-fno-function-sections -ffunction-sections-Wl, –strip-all ( or do not pass ‘-g’ flag) 7 - Condition monitoring through machine learning Data acquisition: acquisition sensor setup; retrieve data over wiredwireless connectivity; label data; store data condition monitoring: data cleaningdenoising; data visualization; preprocessing and feature extraction; feature engineering anomaly detection & classification: machine learning of the system behavior; semi-supervised learning at the edge for anomaly detection; supervised learning to classify anomalies predictive maintenance: model deployment; remaining life prediction models; overall efficiency optimization; operational systems integration 8 - Optimizing machine learning models for IoT applications MLAI in embedded applications is tightly constrained and performance intensive; best-in-class optimization makes it possible; structuring your code effectively can help; 9 - Edge AI processing in real time edge sensing with cloud AI: data may be filtered, compressed, or pre-processed at edge edge AI with cloud data upstream: results send cloud; AI inference at the edge for efficiency, latency, cost, or scale edge AI real time interactive system: everything on edge ioFog; 10 - Enabling machine learning on Arm Cortex M0-powered IoT nodes using Qeexo AutoML (qeexo.com)benefits of ML IoT low latency, low bandwidth, low power consumption, high availability, data privacy and securityml pipeline for IoT: data processing, feature extraction, model training, model conversion, training & conversion, ensemble methods: feature-based, compact representation, easy to reduce model size, model size can be reduced post-trainingbagging is a type of ensemble method where multiple models are trained in parallel on subsampled datasets (reduces error due to variance); many models to combine output to make a single classification; every model get pictures on dataset , over fit, but combine get same accuracy, picture the noise is cancelled for each model. boosting is a type of ensemble method where multiple models are trained in sequence to improve upon the errors of the previous model (reduces error due to bias)11 - Managing ROS2 applications at the edgeROS 2: end-to-end application lifecycle12 - Edge computing: Use cases, requirements, architectures and implementationsheterogeneous processors; challenges: latency, network bandwidth, trustworthiness (safety, security, resilience, reliability, privacy), scalability, data models data ownership, ITOT disconnect, justifying the cost, …Company:Zephyr Project, QuickLoginc, Antmicro, Google SitesReport abusePage detailsPage updated Google SitesReport abuseThis site uses cookies from Google to deliver its services and to analyze traffic. 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Computer Vision, Deep Learning, Artificial superintelligence (ASI) - ResumeCVSearch this siteSkip to main contentSkip to navigationComputer Vision, Deep Learning, Artificial superintelligence (ASI)HomeProductCoursesMachine Learning SpecializationMachine Learning Foundations: A Case Study ApproachFSDLFull Stack Deep LearningMLOpsROSParallel Programming Modern CPPCloud-NativeIoT Scholarship FoundationTensorFlow: Data and Deployment Specialization Workshops and EventsRISC-VEdge-AI-summitEmbedded IoTTeslaAI-HardwareOpenVINO Deep LearningMetaverseWorkshopsIFA2022Book Summary commonplace bookknowledgemanagementPKMTopics and ProjectsAIHubChatGPTHow to startYouTubeYouTube IISoftwareRoadmap for Image Processing Source CodeOpenCVCPPpythonMacOSOpenCVRustcompileIoTShareVideo TrackingCameraCalibrationDRLHardwareQuantum ComputingAltCoinResumeCVApplestartupLinksamazonAboutFQApromptComputer Vision, Deep Learning, Artificial superintelligence (ASI)How to make perfect resume (CV) Best templateAll secret, tips and tricks for the interview My experience in Germany good YouTube channel about basic question in interview : Google SitesReport abusePage detailsPage updated Google SitesReport abuseThis site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies.Learn moreGot it

Computer Vision, Deep Learning, Artificial superintelligence (ASI) - RISC-VSearch this siteSkip to main contentSkip to navigationComputer Vision, Deep Learning, Artificial superintelligence (ASI)HomeProductCoursesMachine Learning SpecializationMachine Learning Foundations: A Case Study ApproachFSDLFull Stack Deep LearningMLOpsROSParallel Programming Modern CPPCloud-NativeIoT Scholarship FoundationTensorFlow: Data and Deployment Specialization Workshops and EventsRISC-VEdge-AI-summitEmbedded IoTTeslaAI-HardwareOpenVINO Deep LearningMetaverseWorkshopsIFA2022Book Summary commonplace bookknowledgemanagementPKMTopics and ProjectsAIHubChatGPTHow to startYouTubeYouTube IISoftwareRoadmap for Image Processing Source CodeOpenCVCPPpythonMacOSOpenCVRustcompileIoTShareVideo TrackingCameraCalibrationDRLHardwareQuantum ComputingAltCoinResumeCVApplestartupLinksamazonAboutFQApromptComputer Vision, Deep Learning, Artificial superintelligence (ASI)RISC-V for AIReferenceRISC-V Magazine ( December 2020)RISC-V Summit 2020; The third annual RISC-V Summit will highlight the continued rapid expansion of the RISC-V ecosystem, presenting both commercial offerings and exciting open-source developments.Reference Rev B. RISC V 15 minute sample course SitesReport abusePage detailsPage updated Google SitesReport abuseThis site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies.Learn moreGot it

Computer Vision, Deep Learning, Artificial superintelligence (ASI) - Book Summary Search this siteSkip to main contentSkip to navigationComputer Vision, Deep Learning, Artificial superintelligence (ASI)HomeProductCoursesMachine Learning SpecializationMachine Learning Foundations: A Case Study ApproachFSDLFull Stack Deep LearningMLOpsROSParallel Programming Modern CPPCloud-NativeIoT Scholarship FoundationTensorFlow: Data and Deployment Specialization Workshops and EventsRISC-VEdge-AI-summitEmbedded IoTTeslaAI-HardwareOpenVINO Deep LearningMetaverseWorkshopsIFA2022Book Summary commonplace bookknowledgemanagementPKMTopics and ProjectsAIHubChatGPTHow to startYouTubeYouTube IISoftwareRoadmap for Image Processing Source CodeOpenCVCPPpythonMacOSOpenCVRustcompileIoTShareVideo TrackingCameraCalibrationDRLHardwareQuantum ComputingAltCoinResumeCVApplestartupLinksamazonAboutFQApromptComputer Vision, Deep Learning, Artificial superintelligence (ASI)Book Summary BooksHow to take smart NotesWriting Solid CodeShape Up: Stop Running in Circles and Ship Work that MattersDisciplined entrepreneurship 24 steps to a successful startup by Bill AuletList of books I’ve read:Computer Vision: Algorithms and ApplicationsMultiple View Geometry in Computer Vision Second EditionComplete to download the latest draft of Machine Learning YearningMultiple view geometry in computer visionLearning OpenCV Book by Adrian Kaehler and Gary BradskiDigital Image Processing, Global Edition by Rafael C. GonzalezIntroduction to Algorithms, 3rd Edition (The MIT Press)The Mythical Man-Month: Essays on Software EngineeringOpenCV-4-with-Python-Blueprints-Second-EditionCracking the coding interview 189 programming questions and solutionsCracking the Tech Career: Insider Advice on Landing a Job at Google, Microsoft, Apple, Or Any Top Tech CompanyThe Art of Startup FundraisingMathematics for Machine LearningPrinciples of Economics (6th edition)The E-Myth Revisited Why Most Small Businesses Don’t Work and What to Do About ItClean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin)Reinventing Your Life: The Breakthough Program to End Negative BehaviorWays Of Thinking: The Limits Of Rational Thought And Artificial Intelligence and linksToolsYouTube - List channelsLanguage:Essential Tips And Tricks For commonplace bookknowledge managementPKMBooksHow to take smart Notes Introduction 4, The four underlying principles 4, the six steps to successful writing 6, afterwordintroductionwrite everything collect notes not related to any thing, organize notes, revirew, rewrite, no reorganise no complex system, develop ideas by smart notes1 slip-box; 2 repeatable tasks that can become automatic and fit together seamlessly change routingesoverarching workflow “Getting Things Done” GTDconstantly have to ump back and forth between different tasks1- bibliographical slip-box: reference2- collected and generated ideas slip-box3- index: notes serve as entry point thought topicwriting a paper step by step1 fleeting notes: it will delete within a dayI always have a slip of paper at hand, on which I note down the ideas of certain pages. on the backside I write down the bibliographic details. after finishing the book I go throught my notes and think how ehse notes might be relevant for already written notes in the slip-box. it means that I always read with an eye towards possible connections in the slip-box2 literature notes: contain the necessary information - in reference system or in the slip-boxI make a note with the bibliographic details. on the backside I would write “on page x is this, on page y is that” and then it goes into the bibliographic slib-box where I collect everything I readread, think about its relevance theoretical background, methodological approach or perspective of the text we read.whether something adds to a discussion in the slip-boxwe look at our slip-box for already existing lines of thought and think about the questions and problems already on our minds to which a new note might contribute. - assigning keywords is much more than just a bureaucratic act. it is a crucial part of the thinking process, which often leads to a deeper elaboration of the note itself and the connection to other notes. 3 permanent notes: develop ideasnew - only relevant to one particular project - in project specific folder - can be discarded or archived after project finishedadd to slip-box behind the note you dirctyly refer to or behind the last note in the slip-boxadd links to other notes or links on other notes to your new notemake sure it can be found from the index (MOC) add an entry in the index if necessary or refer to it from a note that is connected to the indexbuild a latticework of mental models 4 behind multiple notes (link backward) - link to related note - link to MOCmake sure it can be found againkeywords can easily be added to a note like tags and will then show up in the indexa dayread and take notesbuild connections within the slip-box - new ideayou write on your paper, notice a hole in the argument and have another look in the file system for the missing link. you follow up on a footnote, go back to research and might add a fitting quote to one of your papers in the making. The four underlying principles writingsimplicitynot from scratchlet the work carry you forwardthe six steps to successful writingseparate and interlocking taskswe use memo techniques is to bundle items together in a meaningful way and remember the bundlesthe brain doesn’t distinguish between an actual finished task and one that is postponed by taking a notethe fist step is to break down the amprphous task of “writing” into smaller pieces of different tasks that can be finished in one go. the second step is to make sure we always write down the outcome of our thinking, including possible connections to further inquiries.ego depletionread for understandingtake smart notesdevelp ideas (MOC)1 overview of a topic - referred to from the index and used as an entry point into a topic has already develped 2 keeping tarck of all topic - index - share your insightmake it a habitafterwordWriting Solid Code Writing Solid Code (Microsoft Programming Series) by Trendelles Store Note from Writing Solid Code Book Chapter 1Enable all optional compiler warnings.Use lint to catch bugs that your compiler may miss.If you have unit tests, use them.Chapter 2 introduction assert page 16; assert.hassert is nothing more than a repackaged form of the code we saw before, but when you use the macro, it takes one line instead of sevenThat’s why assert is a macro and not a function; if it were a function, callingit could cause unexpected memory or code swapping. Use assertions to validate function arguments .Strip undefined behavior from your code, or use assertions to catch illegal uses of undefined behavior .Don’t waste people’s time. Document unclear assertions .Either remove implicit assumptions, or assert that they are valid .Use assertions to detect impossible conditions.Don’t hide bugs when you program defensively.Use a second algorithm to validate your results.Don’t wait for bugs to happen; use startup checks.Chapter 3Maintain shipping and debugging versions of your program. Shrink-wrap the shipping version, but use the debugging version as much as possible to catch bugs quickly.Assertions are a shorthand way to write debugging checks. Use them to catch illegal conditions that should never arise. Don’t confuse such conditions with error conditions, which you must handle in the final product.Use assertions to validate function arguments and to alert pro grammars when they do something undefined. The more rigidly you define your functions, the easier it will be to validate the arguments.Once you’ve written a function, review it and ask yourself, ‘What am I assuming?” If you find an assumption, either assert that your assumption is always valid, or rewrite the code to re move the assumption. Also ask, ‘What is most likely to be wrong in this code, and how can I automatically detect the problem?” Strive to implement tests that catch bugs at the earliest possible moment. Textbooks encourage programmers to program defensively, but remember that this coding style hides bugs. When you write defensive code, use assertions to alert you if the “can’t happen” cases do happen.Eliminate random behavior. Force bugs to be reproducible .Destroy your garbage so that it’s not misused .Not only did reallocate have to move the block of memory as it was expanding it, but the old memory had to be reallocated and filled with new data. In the assembler, both happened rarely.This brings up another guideline for writing bug-free code: You don’t want anything to happen rarely. You need to isolate those behaviors in your subsystems that may happen and make sure that they do happen. And of ten. If you find rare behavior in your subsystems, be sure to do something anything-to stir things up.The assembler bug could have been found within hours, instead of years, if reallocate hadn’t so rarely moved blocks when it expanded them. If something happens rarely, force it to happen often.Keep debug information to allow stronger error checking.There is no question that the debug code will create differences be tween the ship and the debug versions of your code, but as long as you’re careful not to change the underlying behavior of the code, those differences shouldn’t matter. Create thorough subsystem checks, and use them often.Design your tests carefully. Nothing should be arbitrary.Strive to implement transparent integrity checks.Don’t apply ship version constraints to the debug version. Trade size and speed for error detection.Chapter 4Don’t wait until you have a bug to step through your code.Step through every code path.What About Sweeping Changes? In the past, programmers have asked, “What if I add a feature that touches code in many places? Stepping through all that new code is going to be time consuming.” Let me answer that question with another: Can you make such sweeping changes without introducing any bugs? The habit of stepping through your code creates an interesting negative feedback loop. Programmers who step through their code soon learn to write small, easily testable functions because stepping through large functions is so painful. Programmers also spend more time thinking about how they can localize the changes they need to make-again, so that they can more easily test their code. And isn’t this exactly what you want? No project lead likes it when programmers touch a lot of code; it’s too destabilizing. Nor do leads like large, unmanageable functions; they’re often unmaintainable. Try hard to localize your changes. If you find that you must make pervasive changes, think twice before you decide not to step through all the new code.Overflow and underflow bugsData conversion bugsOff-by-one bugsNULL pointer bugsBugs using garbage memory (0xA3 bugs)Assignment bugs in which you’ve used instead of Precedence bugsLogic bugsAs you step through code, focus on data flow. Source level debuggers can hide execution details. Step through critical code at the instruction level .Chapter 5Make it hard to ignore error conditions. Don’t bury error codes in return values.Always look for, and eliminate, flaws in your interfaces.Don’t write multipurpose functions. Write separate functions to allow stronger argument validation.Don’t be wishy-washy. Define explicit function arguments.Write functions that, given valid inputs, cannot fail.Make the code intelligible at the point of call. Avoid Boolean arguments.Write comments that emphasize potential hazards.Chapter 6Use well-defined data types.Always ask ,”Can this variable or expression over- or underflow?”Implement your designs as accurately as possible. Being kind a close is being kind a buggy. these principles already: Don’t accept special purpose arguments such as the NULL pointer, and Implement your design, not something that approximates it. The third principle is new: Strive to make every function perform its task exactly one time. Implement “the task” just once.Get rid of extraneous if statements.Avoid using nested ?: operators .Handle your special cases just once.Avoid risky language idioms.Don’t needlessly mix operator types. If you must mix operators, use parentheses to isolate the operations.Avoid calling functions that return errors.Chapter 7Don’t reference memory that you don’t own.Don’t reference memory that you have freed.Don’t use output memory as workspace buffers.Don’t pass data in static (or global) memory.Don’t write parasitic functions.Don’t abuse your programming language.Tight C does not guarantee efficient machine code.Write code for the “average” programmer.Chapter 8Bugs don’t just “go away.”Don’t fix bugs later; fix them now.Fix the cause, not the symptom.Don’t clean up code unless the clean up is critical to the product’s success.Don’t implement nonstrategic features.There are no free features.Don’t allow unnecessary flexibility.Don’t keep “trying” solutions until you find one that works. Take the time to find the correct solution.Write and test code in small chunks.Always test your code, even if that means your schedule will slip.Don’t rely on the testing group to find your bugs.Don’t blame testers for finding your bugs.Establish your priorities and stick to them.Never allow the same bug to bite you twice.A: CODING CHECKLISTSB: MEMORY LOGGING ROUTINESTo sum up Charles Simonyi in the early 1970s. ch;byte b;flag f; flags that are always TRUE or FALSEsymbol sym; some sort of symbol structurechar byte flag symbolpch: character pointerpb;pf:psym:related link: Shape Up: Stop Running in Circles and Ship Work that Matters Six-week cycles: Our decisions are based on moving the product forward in the next six weeks, not micromanaging time. We dont count hours or question how individual days are spent. We dont have daily meetings. We dont rethink our roadmap every two weeks. Our focus is at a higher level. We say to ourselves: If this project ships after six weeks, well be really happy. Well feel our time was well spent. Then we commit the six weeks and leave the team alone to get it doneShaping the work: key elements of a solution, appetitepart one shaping:two separate tracks: one for shaping, one for building. Steps to shaping1. Set boundaries.2. Rough out the elements: idea that solves the problem within the appetite but without all the fine details worked out 3. Address risks and rabbit holes. 4. Write the pitch.fixed time, variable scope,Where in the current system does the new thing fit? How do you get to it? What are the key components or interactions? Where does it take you?five ingredients that we always want to include in a pitch: 1. Problem The raw idea, a use case, or something weve seen that motivates us to work on this 2. Appetite How much time we want to spend and how that constrains the solution 3. Solution The core elements we came up with, presented in a form thats easy for people to immediately understand 4. Rabbit holes Details about the solution worth calling out to avoid problems 5. No-gos Anything specifically excluded from the concept: functionality or use cases we intentionally arent covering to fit the appetite or make the problem tractablepart two : BettingSome companies use two-week cycles (aka sprints). We learned that two weeks is too short to get anything meaningful done. Worse than that, two-week cycles are extremely costly due to the planning overhead. The amount of work you get out of two weeks isnt worth the collective hours around the table to sprint plan or the opportunity cost of breaking everyones momentum to re-group.after each six-week cycle, we schedule two weeks for cool-down. bugs: Use cool-down. Bring it to the betting table. Schedule a bug smash.Weve noticed three phases of work when we build a new product from scratch. R&D mode (bet the time on spiking some key pieces of the new product idea. CEO and designer, we dont expect to ship anything at the end of an R&D cycle) , Production mode (Shaping is deliberate again. bet multiple teams in parallel , Shipping is the goal) , Cleanup mode (Theres no shaping. There arent clear team boundaries. Work is shipped) Cleanup shouldnt last longer than two cyclesExample: two years of development, R&D mode for the first year, a year of production mode cycles followed, two cycles of cleanup and significantly cut back the feature set, some overlap between R&D and production mode after that first year, Part three: BuildingWe dont start by assigning tasks to anyone. we define and track the scopes as to-do lists. Each scope corresponds to a list name. Then any tasks for that scope go in that list.image 1, 2, 3, Mark nice-to-haves with First theres the uphill phase of figuring out what our approach is and what were going to do. Then, once we can see all the work involved, theres the downhill phase of executionimage 4, 5, Disciplined entrepreneurship 24 steps to a successful startup by Bill Aulet getting start: idea, technology, passion; What can I do well that I would love to do for an extended period of time? P: 19, 211 market segmentation: The single necessary and sufficient condition for a business is a paying customer. ; technological enthusiastsearly adoptersearly majority ; P: 31, 2 select a beachhead market: P: 44, 3 build an end user profile: P: 52, 4 Calculate the Total Addressable Market (TAM) Size for the Beachhead Market; 200 secure, less impact on network Which of these are reasons for development of the Edge?Proliferation of devices, need for low-latency compute, need for disconnected devices. In this course, well largely focus on AI at the Edge using the Intel Distribution of OpenVINO Toolkit.First, well start off with pre-trained models available in the OpenVINO Open Model Zoo. Even without needing huge amounts of your own data and costly training, you can deploy powerful models already created for many applications.Next, youll learn about the Model Optimizer, which can take a model you trained in frameworks such as TensorFlow, PyTorch, Caffe and more, and create an Intermediate Representation (IR) optimized for inference with OpenVINO and Intel hardware.Third, youll learn about the Inference Engine, where the actual inference is performed on the IR model.Lastly, we’ll hit some more topics on deploying at the edge, including things like handling input streams, processing model outputs, and the lightweight MQTT architecture used to publish data from your edge models to the web. Very important listen again first two video : ClassificationYesnoClasses (1000 competition)20 000 classes ImageNet DetectionFind objects and locationBounding boxes where object isCombined with some form of classification SegmentationClassification segment of images (classify each and every pixel)Semantic segmentationAll objects of the same class are oneInstance segmentationEach object of a class is separate (two cats will different color) Pose estimationText recognitionGANs sudo .downloader –name vehicle-attributes-recognition-barrier-0039 –precisions INT8 -o homeworkspace Pre-processing:Varies by modelColor channel order matters (RGB vs. BGR)Image resizingNormalization def preprocessing(inputimage, height, width): image cv2.resize(inputimage, (width, height)) image image.transpose((2,0,1)) image image.reshape(1, 3, height, width)return image python app.py -i “imagesblue-car.jpg” -t “CARMETA” -m “homeworkspacemodelsvehicle-attributes-recognition-barrier-0039.xml” -c “optintelopenvinodeploymenttoolsinferenceenginelibintel64libcpuextensionsse4.so” python app.py -i “imagessitting-on-car.jpg” -t “POSE” -m “homeworkspacemodelshuman-pose-estimation-0001.xml” -c “optintelopenvinodeploymenttoolsinferenceenginelibintel64libcpuextensionsse4.so” python app.py -i “imagessign.jpg” -t “TEXT” -m “homeworkspacemodelstext-detection-0004.xml” -c “optintelopenvinodeploymenttoolsinferenceenginelibintel64libcpuextensionsse4.so” source optintelopenvinobinsetupvars.sh -pyver 3.5 python optintelopenvinodeploymenttoolsmodeloptimizermo.py –inputmodel frozeninferencegraph.pb –tensorflowobjectdetectionapipipelineconfig pipeline.config –reverseinputchannels –tensorflowusecustomoperationsconfig optintelopenvinodeploymenttoolsmodeloptimizerextensionsfronttfssdv2support.json export MODOPToptintelopenvinodeploymenttoolsmodeloptimizer python optintelopenvinodeploymenttoolsmodeloptimizermo.py –inputmodel squeezenetv1.1.caffemodel –inputproto deploy.prototxt python optintelopenvinodeploymenttoolsmodeloptimizermo.py –inputmodel model.onnx Theres two main command line arguments to use for cutting a model with the Model Optimizer, named intuitively as –input and –output, where they are used to feed in the layer names that should be either the new entry or exit points of the model. -l CLWSclcoshuserieextensionscpubuildlibcoshcpuextension.soinferenceenginesamplesbuildintel64Releaseclassificationsampleasync -i CLTpicsdog.bmp -m CLWSclextcoshmodel.ckpt.xml -d CPU -l CLWSclcoshuserieextensionscpubuildlibcoshcpuextension.so import argparseimport cv2from helpers import loadtoIE, preprocessing CPUEXTENSION “optintelopenvinodeploymenttoolsinferenceenginelibintel64libcpuextensionsse4.so” def getargs(): ‘’’ Gets the arguments from the command line. ‘’’ parser argparse.ArgumentParser(“Load an IR into the Inference Engine”) – Create the descriptions for the commands mdesc “The location of the model XML file” idesc “The location of the image input” rdesc “The type of inference request: Async (‘A’) or Sync (‘S’)” – Create the arguments parser.addargument(“-m”, helpmdesc) parser.addargument(“-i”, helpidesc) parser.addargument(“-r”, helpidesc) args parser.parseargs() return args def asyncinference(execnet, inputblob, image): TODO: Add code to perform asynchronous inference Note: Return the execnet execnet.startasync(requestid0, inputsinputblob: image) while True: status execnet.requests0.wait(-1) if status 0: break else: time.sleep(1) return execnet def syncinference(execnet, inputblob, image): TODO: Add code to perform synchronous inference Note: Return the result of inference result execnet.infer(inputblob: image) return result def performinference(execnet, requesttype, inputimage, inputshape): ‘’’ Performs inference on an input image, given an ExecutableNetwork ‘’’ Get input image image cv2.imread(inputimage) Extract the input shape n, c, h, w inputshape Preprocess it (applies for the IRs from the Pre-Trained Models lesson) preprocessedimage preprocessing(image, h, w) Get the input blob for the inference request inputblob next(iter(execnet.inputs)) Perform either synchronous or asynchronous inference requesttype requesttype.lower() if requesttype ‘a’: output asyncinference(execnet, inputblob, preprocessedimage) elif requesttype ‘s’: output syncinference(execnet, inputblob, preprocessedimage) else: print(“Unknown inference request type, should be ‘A’ or ‘S’.”) exit(1) Return the execnet for testing purposes return output def main(): args getargs() execnet, inputshape loadtoIE(args.m, CPUEXTENSION) performinference(execnet, args.r, args.i, inputshape) if name “main”: main() Note: There is one small change from the code on-screen for running on Linux machines versus Mac. On Mac, cv2.VideoWriter uses cv2.VideoWriterfourcc(‘M’,’J’,’P’,’G’) to write an .mp4 file, while Linux uses 0x00000021. import argparseimport cv2import numpy as np def getargs(): ‘’’ Gets the arguments from the command line. ‘’’ parser argparse.ArgumentParser(“Handle an input stream”) – Create the descriptions for the commands idesc “The location of the input file” – Create the arguments parser.addargument(“-i”, helpidesc) args parser.parseargs() return args def capturestream(args): TODO: Handle image, video or webcam imageflagFalse if args.i’CAM’: args.i0 elif args.i.endswith(‘.jpg’) or args.i.endswith(‘.bmp’): imageflagTrue capture cv2.VideoCapture(args.i) capture.open(args.i) if not imageflag: outcv2.VideoWriter(‘out.mp4’,cv2.VideoWriterfourcc(‘M’,’J’,’P’,’G’),30,(100,100)) else: outNone while capture.isOpened(): flag, frame capture.read() if not flag: break TODO: Get and open video capture keypressedcv2.waitKey(60) if keypressed 27: break TODO: Re-size the frame to 100x100 image cv2.resize(frame, (100, 100)) edges cv2.Canny(image,100,200) if imageflag: cv2.imwrite(“out.jpg”,edges) else: out.write(edges) if not imageflag: out.release cv2.imshow(‘display’, edges) edges) TODO: Add Canny Edge Detection to the frame, with min & max values of 100 and 200 Make sure to use np.dstack after to make a 3-channel image TODO: Write out the frame, depending on image or video TODO: Close the stream and any windows at the end of the application def main(): args getargs() capturestream(args) if name “main”: main() import argparseimport cv2from inference import Network INPUTSTREAM “pets.mp4”CPUEXTENSION “optintelopenvinodeploymenttoolsinferenceenginelibintel64libcpuextensionsse4.so” def getargs(): ‘’’ Gets the arguments from the command line. ‘’’ parser argparse.ArgumentParser(“Run inference on an input video”) – Create the descriptions for the commands mdesc “The location of the model XML file” idesc “The location of the input file” ddesc “The device name, if not ‘CPU’” – Add required and optional groups parser.actiongroups.pop() required parser.addargumentgroup(‘required arguments’) optional parser.addargumentgroup(‘optional arguments’) – Create the arguments required.addargument(“-m”, helpmdesc, requiredTrue) optional.addargument(“-i”, helpidesc, defaultINPUTSTREAM) optional.addargument(“-d”, helpddesc, default’CPU’) args parser.parseargs() return args def assessscene(result, counter, incidentflag): ‘’’ Based on the determined situation, potentially send a message to the pets to break it up. ‘’’ if result01 1 and not incidentflag: timestamp counter 30 print(“Log: Incident at :.3f seconds.”.format(timestamp)) print(“Break it up!”) incidentflag True elif result01 ! 1: incidentflag False return incidentflag def inferonvideo(args): Initialize the Inference Engine plugin Network() Load the network model into the IE plugin.loadmodel(args.m, args.d, CPUEXTENSION) netinputshape plugin.getinputshape() Get and open video capture cap cv2.VideoCapture(args.i) cap.open(args.i) incidentflagFalse counter0 Process frames until the video ends, or process is exited while cap.isOpened(): Read the next frame counter1 flag, frame cap.read() if not flag: break keypressed cv2.waitKey(60) Pre-process the frame pframe cv2.resize(frame, (netinputshape3, netinputshape2)) pframe pframe.transpose((2,0,1)) pframe pframe.reshape(1, pframe.shape) Perform inference on the frame plugin.asyncinference(pframe) Get the output of inference if plugin.wait() 0: result plugin.extractoutput() TODO: Process the output incidentflagassessscene(result,counter,incidentflag) Break if escape key pressed if keypressed 27: break Release the capture and destroy any OpenCV windows cap.release() cv2.destroyAllWindows() def main(): args getargs() inferonvideo(args) if name “main”: main() import argparseimport cv2import numpy as npimport socketimport jsonfrom random import randintfrom inference import Network TODO: Import any libraries for MQTT and FFmpegimport paho.mqtt.client as mqtt INPUTSTREAM “testvideo.mp4”CPUEXTENSION “optintelopenvinodeploymenttoolsinferenceenginelibintel64libcpuextensionsse4.so”ADASMODEL “homeworkspacemodelssemantic-segmentation-adas-0001.xml” CLASSES ‘road’, ‘sidewalk’, ‘building’, ‘wall’, ‘fence’, ‘pole’,’trafficlight’, ‘trafficsign’, ‘vegetation’, ‘terrain’, ‘sky’, ‘person’,’rider’, ‘car’, ‘truck’, ‘bus’, ‘train’, ‘motorcycle’, ‘bicycle’, ‘ego-vehicle’ MQTT server environment variablesHOSTNAME socket.gethostname()IPADDRESS socket.gethostbyname(HOSTNAME)MQTTHOST IPADDRESSMQTTPORT 3004 TODO: Set the Port for MQTTMQTTKEEPALIVEINTERVAL 60 def getargs(): ‘’’ Gets the arguments from the command line. ‘’’ parser argparse.ArgumentParser(“Run inference on an input video”) – Create the descriptions for the commands idesc “The location of the input file” ddesc “The device name, if not ‘CPU’” – Create the arguments parser.addargument(“-i”, helpidesc, defaultINPUTSTREAM) parser.addargument(“-d”, helpddesc, default’CPU’) args parser.parseargs() return args def drawmasks(result, width, height): ‘’’ Draw semantic mask classes onto the frame. ‘’’ Create a mask with color by class classes cv2.resize(result0.transpose((1,2,0)), (width,height), interpolationcv2.INTERNEAREST) uniqueclasses np.unique(classes) outmask classes (25520) Stack the mask so FFmpeg understands it outmask np.dstack((outmask, outmask, outmask)) outmask np.uint8(outmask) return outmask, uniqueclasses def getclassnames(classnums): classnames for i in classnums: classnames.append(CLASSESint(i)) return classnames def inferonvideo(args, model): TODO: Connect to the MQTT server client mqtt.Client() client.connect(MQTTHOST, MQTTPORT, MQTTKEEPALIVEINTERVAL) Initialize the Inference Engine plugin Network() Load the network model into the IE plugin.loadmodel(model, args.d, CPUEXTENSION) netinputshape plugin.getinputshape() Get and open video capture cap cv2.VideoCapture(args.i) cap.open(args.i) Grab the shape of the input width int(cap.get(3)) height int(cap.get(4)) Process frames until the video ends, or process is exited while cap.isOpened(): Read the next frame flag, frame cap.read() if not flag: break keypressed cv2.waitKey(60) Pre-process the frame pframe cv2.resize(frame, (netinputshape3, netinputshape2)) pframe pframe.transpose((2,0,1)) pframe pframe.reshape(1, pframe.shape) Perform inference on the frame plugin.asyncinference(pframe) Get the output of inference if plugin.wait() 0: result plugin.extractoutput() Draw the output mask onto the input outframe, classes drawmasks(result, width, height) classnames getclassnames(classes) speed randint(50,70) TODO: Send the class names and speed to the MQTT server client.publish(“class”, json.dumps(“classnames”: classnames)) client.publish(“speedometer”, json.dumps(“speed”: speed)) Hint: The UI web server will check for a “class” and “speedometer” topic. Additionally, it expects “classnames” and “speed” as the json keys of the data, respectively. TODO: Send frame to the ffmpeg server sys.stdout.buffer.write(frame) sys.stdout.flush() Break if escape key pressed if keypressed 27: break Release the capture and destroy any OpenCV windows cap.release() cv2.destroyAllWindows() TODO: Disconnect from MQTT client.disconnect() def main(): args getargs() model ADASMODEL inferonvideo(args, model) if name “main”: main() -xvf ssdmobilenetv2coco20180329.tar.gz python optintelopenvinodeploymenttoolsmodeloptimizermo.py –inputmodel homeworkspacessdmobilenetv2coco20180329frozeninferencegraph.pb –tensorflowobjectdetectionapipipelineconfig homeworkspacessdmobilenetv2coco20180329pipeline.config –reverseinputchannels –tensorflowusecustomoperationsconfig optintelopenvinodeploymenttoolsmodeloptimizerextensionsfronttfssdv2support.json 4 app.py -m frozeninferencegraph.xml -ct 0.6 -c BLUE python app.py ffmpeg -v warning -f rawvideo -pixelformat bgr24 -videosize 1280x720 -framerate 24 -i - E4:import argparseimport cv2import numpy as np def getargs(): ‘’’ Gets the arguments from the command line. ‘’’ parser argparse.ArgumentParser(“Handle an input stream”) – Create the descriptions for the commands idesc “The location of the input file” – Create the arguments parser.addargument(“-i”, helpidesc) args parser.parseargs() return args def capturestream(args): TODO: Handle image, video or webcam imageflagFalse if args.i’CAM’: args.i0 elif args.i.endswith(‘.jpg’) or args.i.endswith(‘.bmp’): imageflagTrue capture cv2.VideoCapture(args.i) capture.open(args.i) if not imageflag: if Linux outcv2.VideoWriter(‘out.mp4’,0x00000021,30,(100,100)) else: outNone while capture.isOpened(): flag, frame capture.read() if not flag: break TODO: Get and open video capture keypressedcv2.waitKey(60) if keypressed 27: break TODO: Re-size the frame to 100x100 image cv2.resize(frame, (100, 100)) TODO: Add Canny Edge Detection to the frame, with min & max values of 100 and 200 edges cv2.Canny(image,100,200) Make sure to use np.dstack after to make a 3-channel image edges np.dstack((edges, edges, edges)) TODO: Write out the frame, depending on image or video if imageflag: cv2.imwrite(“out.jpg”,edges) else: out.write(edges) if not imageflag: out.release (display:144): Gtk-WARNING : cannot open display: :1 edges) TODO: Close the stream and any windows at the end of the application capture.release() cv2.destroyAllWindows() def main(): args getargs() capturestream(args) if name “main”: main() Let’s say you have a cat and two dogs at your house. import argparseimport cv2from inference import Network INPUTSTREAM “pets.mp4”CPUEXTENSION “optintelopenvinodeploymenttoolsinferenceenginelibintel64libcpuextensionsse4.so” def getargs(): ‘’’ Gets the arguments from the command line. ‘’’ parser argparse.ArgumentParser(“Run inference on an input video”) – Create the descriptions for the commands mdesc “The location of the model XML file” idesc “The location of the input file” ddesc “The device name, if not ‘CPU’” – Add required and optional groups parser.actiongroups.pop() required parser.addargumentgroup(‘required arguments’) optional parser.addargumentgroup(‘optional arguments’) – Create the arguments required.addargument(“-m”, helpmdesc, requiredTrue) optional.addargument(“-i”, helpidesc, defaultINPUTSTREAM) optional.addargument(“-d”, helpddesc, default’CPU’) args parser.parseargs() return args def assessscene(result, counter, incidentflag): ‘’’ Based on the determined situation, potentially send a message to the pets to break it up. ‘’’ if result01 1 and not incidentflag: timestamp counter 30 print(“Log: Incident at :.3f seconds.”.format(timestamp)) print(“Break it up!”) incidentflag True elif result01 ! 1: incidentflag False return incidentflag def inferonvideo(args): Initialize the Inference Engine plugin Network() Load the network model into the IE plugin.loadmodel(args.m, args.d, CPUEXTENSION) netinputshape plugin.getinputshape() Get and open video capture cap cv2.VideoCapture(args.i) cap.open(args.i) incidentflagFalse counter0 Process frames until the video ends, or process is exited while cap.isOpened(): Read the next frame counter1 flag, frame cap.read() if not flag: break keypressed cv2.waitKey(60) Pre-process the frame pframe cv2.resize(frame, (netinputshape3, netinputshape2)) pframe pframe.transpose((2,0,1)) pframe pframe.reshape(1, pframe.shape) Perform inference on the frame plugin.asyncinference(pframe) Get the output of inference if plugin.wait() 0: result plugin.extractoutput() TODO: Process the output incidentflagassessscene(result,counter,incidentflag) Break if escape key pressed if keypressed 27: break Release the capture and destroy any OpenCV windows cap.release() cv2.destroyAllWindows() def main(): args getargs() inferonvideo(args) if name “main”: main() Integrate the Inference Engine - SolutionLet’s step through the tasks one by one, with a potential approach for each. Convert a bounding box model to an IR with the Model Optimizer.I used the SSD Mobilenet V2 architecture from TensorFlow from the earlier lesson here. Notethat the original was downloaded in a separate workspace, so I needed to download it againand then convert it.python optintelopenvinodeploymenttoolsmodeloptimizermo.py –inputmodel frozeninferencegraph.pb –tensorflowobjectdetectionapipipelineconfig pipeline.config –reverseinputchannels –tensorflowusecustomoperationsconfig optintelopenvinodeploymenttoolsmodeloptimizerextensionsfronttfssdv2support.json Extract the results from the inference requestself.execnetwork.requests0.outputsself.outputblob Add code to make the requests and feed back the results within the applicationself.execnetwork.startasync(requestid0, inputsself.inputblob: image)…status self.execnetwork.requests0.wait(-1) Add a command line argument to allow for different confidence thresholds for the modelI chose to use -ct as the argument name here, and added it to the existing arguments.optional.addargument(“-ct”, help”The confidence threshold to use with the bounding boxes”, default0.5)I set a default of 0.5, so it does not need to be input by the user every time. Add a command line argument to allow for different bounding box colors for the outputSimilarly, I added the -c argument for inputting a bounding box color.Note that in my approach, I chose to only allow “RED”, “GREEN” and “BLUE”, which alsoimpacts what I’ll do in the next step; there are many possible approaches here.optional.addargument(“-c”, help”The color of the bounding boxes to draw; RED, GREEN or BLUE”, default’BLUE’) Correctly utilize the command line arguments in and within the applicationBoth of these will come into play within the drawboxes function. For the first, a new lineshould be added before extracting the bounding box points that check whether box2(e.g. the probability of a given box) is above args.ct - assuming you have added args.ct as an argument passed to the drawboxes function. If not, the boxshould not be drawn. Without this, any random box will be drawn, which could be a ton ofvery unlikely bounding box detections. app.pyimport argparseimport cv2from inference import NetworkINPUTSTREAM “testvideo.mp4”CPUEXTENSION “optintelopenvinodeploymenttoolsinferenceenginelibintel64libcpuextensionsse4.so”def getargs(): ‘’’ Gets the arguments from the command line. ‘’’ parser argparse.ArgumentParser(“Run inference on an input video”) – Create the descriptions for the commands mdesc “The location of the model XML file” idesc “The location of the input file” ddesc “The device name, if not ‘CPU’” TODO: Add additional arguments and descriptions for: 1) Different confidence thresholds used to draw bounding boxes 2) The user choosing the color of the bounding boxes – Add required and optional groups parser.actiongroups.pop() required parser.addargumentgroup(‘required arguments’) optional parser.addargumentgroup(‘optional arguments’) – Create the arguments required.addargument(“-m”, helpmdesc, requiredTrue) optional.addargument(“-i”, helpidesc, defaultINPUTSTREAM) optional.addargument(“-d”, helpddesc, default’CPU’) args parser.parseargs() return args def drawboxes(frame, result, args, width, height): ‘’’ Draw bounding boxes onto the frame. ‘’’ for box in result00: Output shape is 1x1x100x7 conf box2 if conf 0.5: xmin int(box3 width) ymin int(box4 height) xmax int(box5 width) ymax int(box6 height) cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), (0, 0, 255), 1) return frame def inferonvideo(args): TODO: Initialize the Inference Engine plugin Network() TODO: Load the network model into the IE plugin.loadmodel(args.m, args.d, CPUEXTENSION) netinputshape plugin.getinputshape() Get and open video capture cap cv2.VideoCapture(args.i) cap.open(args.i) Grab the shape of the input width int(cap.get(3)) height int(cap.get(4)) Create a video writer for the output video The second argument should be cv2.VideoWriterfourcc(‘M’,’J’,’P’,’G’) on Mac, and 0x00000021 on Linux out cv2.VideoWriter(‘out.mp4’, 0x00000021, 30, (width,height)) Process frames until the video ends, or process is exited while cap.isOpened(): Read the next frame flag, frame cap.read() if not flag: break keypressed cv2.waitKey(60) TODO: Pre-process the frame pframe cv2.resize(frame, (netinputshape3, netinputshape2)) pframe pframe.transpose((2,0,1)) pframe pframe.reshape(1, pframe.shape) TODO: Perform inference on the frame plugin.asyncinference(pframe) TODO: Get the output of inference if plugin.wait() 0: result plugin.extractoutput() TODO: Update the frame to include detected bounding boxes frame drawboxes(frame, result, args, width, height) Write out the frame out.write(frame) Break if escape key pressed if keypressed 27: break Release the out writer, capture, and destroy any OpenCV windows out.release() cap.release() cv2.destroyAllWindows() def main(): args getargs() inferonvideo(args) if name “main”: main() app-sustom.pyimport argparseimport cv2from inference import NetworkINPUTSTREAM “testvideo.mp4”CPUEXTENSION “optintelopenvinodeploymenttoolsinferenceenginelibintel64libcpuextensionsse4.so”def getargs(): ‘’’ Gets the arguments from the command line. ‘’’ parser argparse.ArgumentParser(“Run inference on an input video”) – Create the descriptions for the commands mdesc “The location of the model XML file” idesc “The location of the input file” ddesc “The device name, if not ‘CPU’” TODO: Add additional arguments and descriptions for: 1) Different confidence thresholds used to draw bounding boxes 2) The user choosing the color of the bounding boxes cdesc “The color of the bounding boxes to draw; RED, GREEN or BLUE” ctdesc “The confidence threshold to use with the bounding boxes” – Add required and optional groups parser.actiongroups.pop() required parser.addargumentgroup(‘required arguments’) optional parser.addargumentgroup(‘optional arguments’) – Create the arguments required.addargument(“-m”, helpmdesc, requiredTrue) optional.addargument(“-i”, helpidesc, defaultINPUTSTREAM) optional.addargument(“-d”, helpddesc, default’CPU’) optional.addargument(“-c”, helpcdesc, default’BLUE’) optional.addargument(“-ct”, helpctdesc, default0.5) args parser.parseargs() return args def convertcolor(colorstring): ‘’’ Get the BGR value of the desired bounding box color. Defaults to Blue if an invalid color is given. ‘’’ colors “BLUE”: (255,0,0), “GREEN”: (0,255,0), “RED”: (0,0,255) outcolor colors.get(colorstring) if outcolor: return outcolor else: return colors’BLUE’ def drawboxes(frame, result, args, width, height): ‘’’ Draw bounding boxes onto the frame. ‘’’ for box in result00: Output shape is 1x1x100x7 conf box2 if conf args.ct: xmin int(box3 width) ymin int(box4 height) xmax int(box5 width) ymax int(box6 height) cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), args.c, 1) return frame def inferonvideo(args): Convert the args for color and confidence args.c convertcolor(args.c) args.ct float(args.ct) TODO: Initialize the Inference Engine plugin Network() TODO: Load the network model into the IE plugin.loadmodel(args.m, args.d, CPUEXTENSION) netinputshape plugin.getinputshape() Get and open video capture cap cv2.VideoCapture(args.i) cap.open(args.i) Grab the shape of the input width int(cap.get(3)) height int(cap.get(4)) Create a video writer for the output video The second argument should be cv2.VideoWriterfourcc(‘M’,’J’,’P’,’G’) on Mac, and 0x00000021 on Linux out cv2.VideoWriter(‘out.mp4’, 0x00000021, 30, (width,height)) Process frames until the video ends, or process is exited while cap.isOpened(): Read the next frame flag, frame cap.read() if not flag: break keypressed cv2.waitKey(60) TODO: Pre-process the frame pframe cv2.resize(frame, (netinputshape3, netinputshape2)) pframe pframe.transpose((2,0,1)) pframe pframe.reshape(1, pframe.shape) TODO: Perform inference on the frame plugin.asyncinference(pframe) TODO: Get the output of inference if plugin.wait() 0: result plugin.extractoutput() TODO: Update the frame to include detected bounding boxes frame drawboxes(frame, result, args, width, height) Write out the frame out.write(frame) Break if escape key pressed if keypressed 27: break Release the out writer, capture, and destroy any OpenCV windows out.release() cap.release() cv2.destroyAllWindows() def main(): args getargs() inferonvideo(args) if name “main”: main() interface.py’'’Contains code for working with the Inference Engine.You’ll learn how to implement this code and more inthe related lesson on the topic.’'’import osimport sysimport logging as logfrom openvino.inferenceengine import IENetwork, IECoreclass Network: ‘’’ Load and store information for working with the Inference Engine, and any loaded models. ‘’’ def init(self): self.plugin None self.network None self.inputblob None self.outputblob None self.execnetwork None self.inferrequest None def loadmodel(self, model, device”CPU”, cpuextensionNone): ‘’’ Load the model given IR files. Defaults to CPU as device for use in the workspace. Synchronous requests made within. ‘’’ modelxml model modelbin os.path.splitext(modelxml)0 “.bin” Initialize the plugin self.plugin IECore() Add a CPU extension, if applicable if cpuextension and “CPU” in device: self.plugin.addextension(cpuextension, device) Read the IR as a IENetwork self.network IENetwork(modelmodelxml, weightsmodelbin) Load the IENetwork into the plugin self.execnetwork self.plugin.loadnetwork(self.network, device) Get the input layer self.inputblob next(iter(self.network.inputs)) self.outputblob next(iter(self.network.outputs)) return def getinputshape(self): ‘’’ Gets the input shape of the network ‘’’ return self.network.inputsself.inputblob.shape def asyncinference(self, image): ‘’’ Makes an asynchronous inference request, given an input image. ‘’’ self.execnetwork.startasync(requestid0, inputsself.inputblob: image) return def wait(self): ‘’’ Checks the status of the inference request. ‘’’ status self.execnetwork.requests0.wait(-1) return status def extractoutput(self): ‘’’ Returns a list of the results for the output layer of the network. ‘’’ return self.execnetwork.requests0.outputsself.outputblob The second is just a small adjustment to the cv2.rectangle function that draws the bounding boxes we found to be above args.ct. I actually added a function to matchthe different potential colors up to their RGB values first, due to how I took them in from thecommand line:def convertcolor(colorstring): ‘’’ Get the BGR value of the desired bounding box color. Defaults to Blue if an invalid color is given. ‘’’ colors “BLUE”: (255,0,0), “GREEN”: (0,255,0), “RED”: (0,0,255) outcolor colors.get(colorstring) if outcolor: return outcolor else: return colors’BLUE’I can also add the tuple returned from this function as an additional color argument to feed todrawboxes.Then, the line where the bounding boxes are drawn becomes:cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), color, 1)I was able to run my app, if I was using the converted TF model from earlier (and placed in the current directory), using the below:bashpython app.py -m frozeninferencegraph.xmlOr, if I added additional customization with a confidence threshold of 0.6 and blue boxes:bashpython app.py -m frozeninferencegraph.xml -ct 0.6 -c BLUENote that I placed my customized app actually in app-custom.pyGoogle SitesReport abusePage detailsPage updated Google SitesReport abuseThis site uses cookies from Google to deliver its services and to analyze traffic. 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Computer Vision, Deep Learning, Artificial superintelligence (ASI) - Cloud-NativeSearch this siteSkip to main contentSkip to navigationComputer Vision, Deep Learning, Artificial superintelligence (ASI)HomeProductCoursesMachine Learning SpecializationMachine Learning Foundations: A Case Study ApproachFSDLFull Stack Deep LearningMLOpsROSParallel Programming Modern CPPCloud-NativeIoT Scholarship FoundationTensorFlow: Data and Deployment Specialization Workshops and EventsRISC-VEdge-AI-summitEmbedded IoTTeslaAI-HardwareOpenVINO Deep LearningMetaverseWorkshopsIFA2022Book Summary commonplace bookknowledgemanagementPKMTopics and ProjectsAIHubChatGPTHow to startYouTubeYouTube IISoftwareRoadmap for Image Processing Source CodeOpenCVCPPpythonMacOSOpenCVRustcompileIoTShareVideo TrackingCameraCalibrationDRLHardwareQuantum ComputingAltCoinResumeCVApplestartupLinksamazonAboutFQApromptComputer Vision, Deep Learning, Artificial superintelligence (ASI)Cloud-NativeMaster Cloud-Native infrastructure with KubernetesI) Learning Docker (best )I) Learning Docker-ti terminal interactivedocker run -ti ubuntu:latest bash cat etclsb-release which distribution docker ps -format FORMAT which docker … runningdocker ps -l the last docker exiteddocker commit ID new image id docker tag imageID my-imagedocker run –rm -ti ubuntu sleep 5 –rm to remove after usingdocker run -ti ubuntu bash -c “sleep 3; echo all done”” -c : some commandsdocker run -d -ti ubuntu bash -d is detached run in background docker attach nameDocker ctrlp qdocker logs continaersname-p publish nc -lp docker imagesGoogle SitesReport abusePage detailsPage updated Google SitesReport abuseThis site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies.Learn moreGot it

Computer Vision, Deep Learning, Artificial superintelligence (ASI) - Video TrackingSearch this siteSkip to main contentSkip to navigationComputer Vision, Deep Learning, Artificial superintelligence (ASI)HomeProductCoursesMachine Learning SpecializationMachine Learning Foundations: A Case Study ApproachFSDLFull Stack Deep LearningMLOpsROSParallel Programming Modern CPPCloud-NativeIoT Scholarship FoundationTensorFlow: Data and Deployment Specialization Workshops and EventsRISC-VEdge-AI-summitEmbedded IoTTeslaAI-HardwareOpenVINO Deep LearningMetaverseWorkshopsIFA2022Book Summary commonplace bookknowledgemanagementPKMTopics and ProjectsAIHubChatGPTHow to startYouTubeYouTube IISoftwareRoadmap for Image Processing Source CodeOpenCVCPPpythonMacOSOpenCVRustcompileIoTShareVideo TrackingCameraCalibrationDRLHardwareQuantum ComputingAltCoinResumeCVApplestartupLinksamazonAboutFQApromptComputer Vision, Deep Learning, Artificial superintelligence (ASI)Video TrackingTrackingList of DatasetsSource codeSelf collected datasetsVideo labelingReferenceThe online course about multiple object tracking in Edx: Resilient object detection and tracking on Edge and cloud (AWS): The best methods of object tracking run on GPU. The versioning of different deep learning frameworks are crucial. For example the latest version of OS for Jetson Nano Jetpack which use latest CUDA but the Pytorch only support up to 10.1 now. So we need to install lower Jetpack version on Jetson Nano or compile the Pytorch. I compile Pytorch and it takes few hours with a lot of issue to solve. For the Ubuntu 20 there is not support for CUDA 10 you need install CUDA 11 and compile the Pytorch with a lot of library. install CUDA 10.x on Ubuntu 20 is possible but takes time to solve conflicts also supporting eGPU is another issue for lower ubuntu. On MacOS installing everything is easy because of not supporting GPU but many library and frameworks of the source codes of tracking require GPU version. Even install CPU version of all library does not grantee to run tracking methods. Another aspect is speed. Running tracking even on GPU is very slow based on my experience using Yolo version 3 which is one of the fastest object detection on GTX 2070 can process up to 15 FPS with Full HD videos. Methods on tracking is very different. First generation, it is completely based on computer vision. The second generation combining Kalman filter and advanced computer vision (SIFT), the third generation using deep learning and some of the methods of previous generation like Kalman filter. The fourth generation using combination of two deep learning methods. And the latest generation using complete end to end models like RNN. Object tracking works with all combination of environments such as, moving objects, moving objects and camera in dynamic environments. As long as object appear in the frame until disappeared it the tracking can track and identification as one objects. No mater how many FPS.TrackingClassic object tracking classic feature detection (SIFT and SURF), combined with a machine learning algorithm like KNN or SVM for classification, or with a description matcher like FLANN for object detection.Kalman filtering, sparse and dense optical flow, Example: Simple Online and Realtime Tracking (SORT), which uses a combination of the Hungarian algorithm and Kalman filter SOT is a hot topic in the last decade. Early visual tracking methods rely on extracting hand-crafted features of candidate target regions, and use matching algorithms or hand-crafted discriminative classifiers to generate tracking results.The MOT track aims to recover the trajectories of objects in video sequences, which is an important problem in computer vision with many applications, such as surveillance, activity analysis, and sport video analysis.Video object detection datasets. The video object detection task aims to detect objects of different categories in video sequences. Multi-object tracking datasetslarge-scale benchmark Multi-Class Multi-object tracking datasetsVisDrone datasets is captured in various unconstrained scenes, focusing on four core problems in computer vision fields, i.e., image object detection, video object detection, single object tracking, and multi- object tracking.the accuracy of detection methods suffers from degenerated object appearances in videos such as motion blur, pose variations, and video de-focus. Exploiting temporal coherence and aggregating features in consecutive frames might to be two effective ways to handle such issue.Temporal coherence. A feasible way to exploit temporal coherence is using object trackersFeature aggregation. Aggregating features in consecutive frames is also a useful way to improve the performance.List of DatasetsMOT20KITTI Tracking MOTChallenge 2015 UA-DETRAC Tracking DukeMTMC Campus MOT17 UAVDT-MOT VisDroneSource codeROLOTensorFlow: linkSiamMaskPyTorch 0.4.1: linkDeep SORTPyTorch 0.4.0: linkTensorFlow 1.0: linkTrackR-CNNTensorFlow 1.13.1: linkTracktorPyTorch 1.3.1: linkJDEPyTorch 1.2.0: linkMCMOT: One-shot multi-class multi-object trackingSelf collected datasets Video labelingVATICUltimateLabeling ReferenceVision Meets Drones: Past, Present and Future Deep Learning in Video Multi-Object Tracking: A Survey HOTA: A Higher Order Metric for Evaluating Multi-object Tracking some examples Endeavor to summarize MOT:The best methods running on GPU. The versioning of different deep learning frameworks are crucial. For example the latest version of OS for Jetson Nano “Jetpack” use CUDA 11 but the Pytorch only support up to 10.1 now. So we need to install lower Jetpack version on Jetson Nano or compile the Pytorch. I compile Pytorch and it takes few hours with a lot of issue to solve. For the Ubuntu 20 there is not support for CUDA 10 you need install CUDA 11 and compile the Pytorch with a lot of library. install CUDA 10.x on Ubuntu 20 is possible but takes time to solve conflicts also supporting eGPU is another issue for lower ubuntu. On MacOS installing everything is easy because of not supporting GPU but many library and frameworks of the source codes of tracking require GPU version. Even install CPU version of all library does not grantee to run tracking methods. Another aspect is speed. Running tracking even on GPU is very slow based on my experience using Yolo version 3 which is one of the fastest object detection on GTX 2070 may run in real time.Methods on tracking is very different. First generation, it is completely based on computer vision. The second generation combining machine learning, Kalman filter and advanced computer vision (SIFT), the third generation using deep learning and some of the methods of previous generation like Kalman filter. The fourth generation using combination of two deep learning methods. And the latest generation using complete end to end models with RNN.Object tracking works with all combination of environments such as, moving objects, moving objects and camera in dynamic environments. As long as object appear in the frame until disappeared it the tracking can track and identification as one objects. No mater how many FPS.In around 130 videos of the course of Multiple Object Tracking on EDEX means this topic is huge and require more attention for the more research and development.Running MOT on Jetson nano is tricky and hacky in many way. First, the cup is arm based and not many package are build for it. Datasets for Tracking:MOTChallengeMOT15MOT1617MOT19KITTIUA-DETRAC tracking benchmarkmetricsMostly Tracked (MT) trajectories: number of ground-truth trajectories that are correctly tracked in at least 80 of the frames.Fragments: trajectory hypotheses which cover at most 80 of a ground truth trajectory. Observe that a true trajectory can be covered by more than one fragment.Mostly Lost (ML) trajectories: number of ground-truth trajectories that are correctly tracked in less than 20 of the frames. False trajectories: predicted trajectories which do not correspond to a real object (i.e. to a ground truth trajectory).ID switches: number of times when the object is correctly tracked, but the associated ID for the object is mistakenly changed. Test: Ubuntu, Not mac, can based on GPU, webcam not working GPUYouTube:OpenCVTracking Objects OpenCV Python Tutorials for Beginners 2020Multiple Object TrackingPython: Real-time Multiple Object Tracking (MOT) with Yolov3, Tensorflow and Deep SORT FULL COURSEThere are at least 7 types of tracker algorithms that can be used in OpenCV: not DLMILBOOSTINGMEDIANFLOWTLDKCFGOTURNMOSSE Kalman filtering, sparse and dense optical flow are Simple Online and Realtime Tracking (SORT), which uses a combination of the Hungarian algorithm and Kalman filter to achieve decent object tracking.R-CNNaround 2000 region proposals selective searchshare colors and textures, lightning conditionsslow to train and testFast R-CNNcomputes a convolutional feature map for the entire input image in a single forward pass of the networkarchitecture is trained end-to-end with a multi-task loss Online and Realtime Tracking with a Deep Association Metric. 2017 online course about multiple object tracking in Edx:Course Section 0: Welcome and Introduction ‘Part 1: Introduction to Multiple Object Tracking (MOT): good ; many definition and definitions: 15 videosIntroductory examplesIs about the accurate perception of the driving environmentAvoid collisions at the airportCrowd surveillanceCrowd behaviorPlanning of emergency proceduresPedestrian tracking using LIDARTracking based on detectionsGroup behaviorPart 2: Single Object Tracking in clutter (SOT): Many math; basic methods, 23 videosIntroduction to SOT in Clutter Pruning and merging Pruning : remove hypotheses with small weights (and renormalize) Merging: approximate a mixture of densities by a single density (often Gaussian) Gating: technique to disregard unreasonable detections pruningSOTGaussian densitiesNearest neighbour (NN) filter pruningProbabilistic data association (PDA) filter mergingGaussian mixture densitesGaussian sum filter (GSF) pruningmergingPart 3: Tracking a known number of objects in clutter 303.3.6 Predicting the n object density3.4.1 Introduction to data associationPart 4: Random Finite Sets 24Part 5: Multiple Object Tracking using conjugate priors 25 only in YouTube Part 6: Outlook - what is next? 18 only in YouTubeGoogle SitesReport abusePage detailsPage updated Google SitesReport abuseThis site uses cookies from Google to deliver its services and to analyze traffic. 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Computer Vision, Deep Learning, Artificial superintelligence (ASI) - CPPSearch this siteSkip to main contentSkip to navigationComputer Vision, Deep Learning, Artificial superintelligence (ASI)HomeProductCoursesMachine Learning SpecializationMachine Learning Foundations: A Case Study ApproachFSDLFull Stack Deep LearningMLOpsROSParallel Programming Modern CPPCloud-NativeIoT Scholarship FoundationTensorFlow: Data and Deployment Specialization Workshops and EventsRISC-VEdge-AI-summitEmbedded IoTTeslaAI-HardwareOpenVINO Deep LearningMetaverseWorkshopsIFA2022Book Summary commonplace bookknowledgemanagementPKMTopics and ProjectsAIHubChatGPTHow to startYouTubeYouTube IISoftwareRoadmap for Image Processing Source CodeOpenCVCPPpythonMacOSOpenCVRustcompileIoTShareVideo TrackingCameraCalibrationDRLHardwareQuantum ComputingAltCoinResumeCVApplestartupLinksamazonAboutFQApromptComputer Vision, Deep Learning, Artificial superintelligence (ASI)CClean Code for Computer Vision using OpenCV and CWhen writing clean code using the OpenCV library in C, here are some additional principles to follow:Avoid using magic numbers: Instead of using hardcoded values, use named constants or named variables for numbers that have a specific meaning.Keep functions focused on OpenCV operations: Limit the number of lines of code in functions and make sure each function focuses on performing OpenCV operations.Use clear and descriptive names for OpenCV functions: When calling OpenCV functions, use names that are clear and descriptive of what the function does.Use OpenCV data structures appropriately: Familiarize yourself with the different OpenCV data structures, like cv::Mat, cv::Point, etc., and use the appropriate one for each task.Error handling: Make sure to check the return value of OpenCV functions and handle errors appropriately.Make use of OpenCV’s high-level functions: Whenever possible, use OpenCV’s high-level functions instead of lower-level functions to simplify code and reduce the amount of boilerplate code.Keep track of the image size: Make sure to keep track of the size of images, especially when performing operations like resizing, as this can affect the results.These examples demonstrate how following good coding practices and paying attention to the specific features of the OpenCV library can help you write clean, efficient, and effective code.By following these principles, you can write clean and maintainable code that makes effective use of the OpenCV library. Here are several examples of clean code in OpenCV C:Meaningful variable names:cv::Mat originalimage cv::imread(“image.jpg”);cv::Mat resizedimage;cv::resize(originalimage, resizedimage, cv::Size(), 0.5, 0.5, cv::INTERAREA);Use of high-level functions:cv::Mat src cv::imread(“image.jpg”);cv::Mat dst;cv::GaussianBlur(src, dst, cv::Size(3,3), 0);Error handling:cv::Mat src cv::imread(“image.jpg”);if(src.empty()) std::cout corners;cv::goodFeaturesToTrack(src, corners, 100, 0.01, 10);Reusable functions:cv::Mat src cv::imread(“image.jpg”);cv::Mat gray;cv::cvtColor(src, gray, cv::COLORBGR2GRAY);cv::Mat sharpenimage(const cv::Mat& image) cv::Mat result; cv::GaussianBlur(image, result, cv::Size(0,0), 3); cv::addWeighted(image, 1.5, result, -0.5, 0, result); return result;cv::Mat sharpened sharpenimage(gray);Clear and concise comments: Load the source imagecv::Mat src cv::imread(“image.jpg”); Convert the image to grayscalecv::Mat gray;cv::cvtColor(src, gray, cv::COLORBGR2GRAY); Threshold the image to create a binary imagecv::Mat thresholded;cv::threshold(gray, thresholded, 128, 255, cv::THRESHBINARY);docthis code shows information about imagecode docthis code shows information about imagecode Google SitesReport abusePage detailsPage updated Google SitesReport abuseThis site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies.Learn moreGot it

Computer Vision, Deep Learning, Artificial superintelligence (ASI) - MacOSOpenCVSearch this siteSkip to main contentSkip to navigationComputer Vision, Deep Learning, Artificial superintelligence (ASI)HomeProductCoursesMachine Learning SpecializationMachine Learning Foundations: A Case Study ApproachFSDLFull Stack Deep LearningMLOpsROSParallel Programming Modern CPPCloud-NativeIoT Scholarship FoundationTensorFlow: Data and Deployment Specialization Workshops and EventsRISC-VEdge-AI-summitEmbedded IoTTeslaAI-HardwareOpenVINO Deep LearningMetaverseWorkshopsIFA2022Book Summary commonplace bookknowledgemanagementPKMTopics and ProjectsAIHubChatGPTHow to startYouTubeYouTube IISoftwareRoadmap for Image Processing Source CodeOpenCVCPPpythonMacOSOpenCVRustcompileIoTShareVideo TrackingCameraCalibrationDRLHardwareQuantum ComputingAltCoinResumeCVApplestartupLinksamazonAboutFQApromptComputer Vision, Deep Learning, Artificial superintelligence (ASI)MacOSOpenCV YouTube Channel: OpenCV on MacOS - 2023How to compile OpenCV on MacHow to use OpenCV with Xcode (C)To view these steps, you may watch a video on YouTube. binbash -c “(curl -fsSL brew install jpeg libpng libtiff openexrbrew install opencv find path of OpenCV. in order to see hidden folders and file in Mac you can use “CommandShiftDot” export PKGCONFIGPATH”usrlocalCellaropencv4.7.01libpkgconfig:PKGCONFIGPATH”pkg-config –cflags opencv4sMakefileTARGET .mainSRCS : (wildcard .src.cpp ..cpp)OBJS : (patsubst cpp,o,(SRCS))CFLG -g -Wall -IusrlocalCellaropencv4.7.01includeopencv4 -Iinc -I. -stdc17LDFG -Wl, (shell pkg-config opencv –cflags –libs)CXX g(TARGET) : (OBJS) (CXX) -o (TARGET) (OBJS) (LDFG).o:.cpp (CXX) (CFLG) -c “opencv2opencv.hpp” using namespace cv;using namespace std;int main(int argc, const char argv) insert code here… cout git clone clone branch -agit switch 5.xmkdir buildopencvcd buildopencvcmake guiopencv flolderopencv build folder Unix makefile compiler ( do not select Xcode)OPENCVEXTRAMODULESPATH to modulesOPENCVENABLENONFREEONconfigure againgenerateremove below itemszlibJava 2ximgcodeipp 2xxfeatures2dfacewechat qrcodeimgprocademake -j8sh setupvars.sh sudo make installCompile 3:sudo xcodebuild -licensesudo xcode-select –installusrbinruby -e “(curl -fsSL install cmake pkg-configbrew install jpeg libpng libtiff openexrbrew install wgetbrew install –cask yunasudo .cmake-gui brew install cmake brew install –cask cmakecd git clone clone branch -agit switch 5.xmkdir buildopencvcd buildopencvcmake opencv flolderopencv build folder Unix makefile compiler ( do not select Xcode)OPENCVEXTRAMODULESPATH to modulesOPENCVENABLENONFREEONconfigure againgeneratecmake -D CMAKEBUILDTYPERELEASE -D CMAKEINSTALLPREFIXusrlocal -D OPENCVEXTRAMODULESPATHUsersfarshidcodeopencvcontribmodules -D PYTHON3LIBRARYpython -c ‘import subprocess ; import sys ; s subprocess.checkoutput(“python-config –configdir”, shellTrue).decode(“utf-8”).strip() ; (M, m) sys.versioninfo:2 ; print(“libpython..dylib”.format(s, M, m))’ -D PYTHON3INCLUDEDIRpython -c ‘import distutils.sysconfig as s; print(s.getpythoninc())’ -D PYTHON3EXECUTABLEVIRTUALENVbinpython -D BUILDopencvpython2OFF -D BUILDopencvpython3ON -D INSTALLPYTHONEXAMPLESON -D INSTALLCEXAMPLESOFF -D OPENCVENABLENONFREEON -D BUILDEXAMPLESON ..opencvrm CMakeCache.txtmake -j8sh setupvars.sh sudo make installCompile:brew install cmake brew install –cask cmakecd git clone clone branch -agit switch 5.xmkdir buildopencvcd buildopencvcmake guiopencv flolderopencv build folder Unix makefile compiler ( do not select Xcode)OPENCVEXTRAMODULESPATH to modulesconfigure againgenerateremove below itemszlibJava 2ximgcodeipp 2xxfeatures2dfacewechat qrcodeimgprocademake -j8sh setupvars.sh sudo make installbrew install pkg-configusrlocalincludeopencv5 Google SitesReport abusePage detailsPage updated Google SitesReport abuseThis site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies.Learn moreGot it

Computer Vision, Deep Learning, Artificial superintelligence (ASI) - IFA2022Search this siteSkip to main contentSkip to navigationComputer Vision, Deep Learning, Artificial superintelligence (ASI)HomeProductCoursesMachine Learning SpecializationMachine Learning Foundations: A Case Study ApproachFSDLFull Stack Deep LearningMLOpsROSParallel Programming Modern CPPCloud-NativeIoT Scholarship FoundationTensorFlow: Data and Deployment Specialization Workshops and EventsRISC-VEdge-AI-summitEmbedded IoTTeslaAI-HardwareOpenVINO Deep LearningMetaverseWorkshopsIFA2022Book Summary commonplace bookknowledgemanagementPKMTopics and ProjectsAIHubChatGPTHow to startYouTubeYouTube IISoftwareRoadmap for Image Processing Source CodeOpenCVCPPpythonMacOSOpenCVRustcompileIoTShareVideo TrackingCameraCalibrationDRLHardwareQuantum ComputingAltCoinResumeCVApplestartupLinksamazonAboutFQApromptComputer Vision, Deep Learning, Artificial superintelligence (ASI)IFA2022IFA 2022 Keynotes 2-6 Sep. 2022 in Berlin more: IFA 2022 Keynotes 2-6 Sep. 2022 in Berlin more: IFA 2022 Keynotes 2-6 Sep. 2022 in Berlin: Qualcomm more: Google SitesReport abusePage detailsPage updated Google SitesReport abuseThis site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies.Learn moreGot it

Computer Vision, Deep Learning, Artificial superintelligence (ASI) - TeslaSearch this siteSkip to main contentSkip to navigationComputer Vision, Deep Learning, Artificial superintelligence (ASI)HomeProductCoursesMachine Learning SpecializationMachine Learning Foundations: A Case Study ApproachFSDLFull Stack Deep LearningMLOpsROSParallel Programming Modern CPPCloud-NativeIoT Scholarship FoundationTensorFlow: Data and Deployment Specialization Workshops and EventsRISC-VEdge-AI-summitEmbedded IoTTeslaAI-HardwareOpenVINO Deep LearningMetaverseWorkshopsIFA2022Book Summary commonplace bookknowledgemanagementPKMTopics and ProjectsAIHubChatGPTHow to startYouTubeYouTube IISoftwareRoadmap for Image Processing Source CodeOpenCVCPPpythonMacOSOpenCVRustcompileIoTShareVideo TrackingCameraCalibrationDRLHardwareQuantum ComputingAltCoinResumeCVApplestartupLinksamazonAboutFQApromptComputer Vision, Deep Learning, Artificial superintelligence (ASI)TeslaTesla AI day (2021)Video modules: Video neural net architecture3D Convolution Transformer RNNSpatial RNN video moduleTesla AI day (2021)vision system SitesReport abusePage detailsPage updated Google SitesReport abuseThis site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies.Learn moreGot it

Computer Vision, Deep Learning, Artificial superintelligence (ASI) - Topics and ProjectsSearch this siteSkip to main contentSkip to navigationComputer Vision, Deep Learning, Artificial superintelligence (ASI)HomeProductCoursesMachine Learning SpecializationMachine Learning Foundations: A Case Study ApproachFSDLFull Stack Deep LearningMLOpsROSParallel Programming Modern CPPCloud-NativeIoT Scholarship FoundationTensorFlow: Data and Deployment Specialization Workshops and EventsRISC-VEdge-AI-summitEmbedded IoTTeslaAI-HardwareOpenVINO Deep LearningMetaverseWorkshopsIFA2022Book Summary commonplace bookknowledgemanagementPKMTopics and ProjectsAIHubChatGPTHow to startYouTubeYouTube IISoftwareRoadmap for Image Processing Source CodeOpenCVCPPpythonMacOSOpenCVRustcompileIoTShareVideo TrackingCameraCalibrationDRLHardwareQuantum ComputingAltCoinResumeCVApplestartupLinksamazonAboutFQApromptComputer Vision, Deep Learning, Artificial superintelligence (ASI)Topics Google SitesReport abusePage detailsPage updated Google SitesReport abuseThis site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies.Learn moreGot it

Computer Vision, Deep Learning, Artificial superintelligence (ASI) - Edge-AI-summitSearch this siteSkip to main contentSkip to navigationComputer Vision, Deep Learning, Artificial superintelligence (ASI)HomeProductCoursesMachine Learning SpecializationMachine Learning Foundations: A Case Study ApproachFSDLFull Stack Deep LearningMLOpsROSParallel Programming Modern CPPCloud-NativeIoT Scholarship FoundationTensorFlow: Data and Deployment Specialization Workshops and EventsRISC-VEdge-AI-summitEmbedded IoTTeslaAI-HardwareOpenVINO Deep LearningMetaverseWorkshopsIFA2022Book Summary commonplace bookknowledgemanagementPKMTopics and ProjectsAIHubChatGPTHow to startYouTubeYouTube IISoftwareRoadmap for Image Processing Source CodeOpenCVCPPpythonMacOSOpenCVRustcompileIoTShareVideo TrackingCameraCalibrationDRLHardwareQuantum ComputingAltCoinResumeCVApplestartupLinksamazonAboutFQApromptComputer Vision, Deep Learning, Artificial superintelligence (ASI)Edge AI summitShort summary of the edge AI summit 18-20 November 2020Wednesday, November 18, 2020A Software Solution Enabling Predictive Maintenance at the Sensor LevelHelping Fish Farmers Feed The World With Deep LearningtinyMLPerf: Benchmarking Ultra-low Power Machine Learning SystemsUltra-low power neuromorphic intelligence for the sensor edge How is AI affecting hearables and sensors?Breaking the Barriers to Deploy DNNs on Low-Power HardwareOptimizing ML Models At The Edge Made SimpleThursday, November 19, 2020Developing Edge AI Solutions For A Post-Pandemic SocietyThe Evolving Landscape of Edge AIInferX X1, The Fastest and Most Efficient Edge Inference AcceleratorImplementing Edge Technologies in Retail: Walmart Case StudyThe Era of Analog AI Compute Is HereUsing Edge AI To Detect Repetitive MotFriday, November 20, 2020Spatial Computing: A Collision of Edge and Cloud-Based ComputingBuilding An Autonomous Network For IoT and Edge ApplicationsPractical Edge Inferencing: Enabling fastest AI inferencing per Watt leveraging sparsityLarge Scale Deep Learning and AI models on the EdgeThe Edge: The Hottest Market for AI Accelerator Chips - Introducing the Kisaco Leadership Chart on AI Hardware Accelerators 2020-21: Edge and AutomotiveShort summary of the edge AI summit 18-20 November 2020 Best of Wednesday, November 18, 2020; tinyMLPerf, Breaking the Barriers to Deploy DNNs on Low-Power Hardware, Optimizing ML Models At The Edge Made SimpleThursday, November 19, 20208:00 AM - 8:30 AM (PST) KEYNOTE PRESENTATION: Developing Edge AI Solutions For A Post-Pandemic Society Sastry Malladi - FogHorn Systems8:35 AM - 9:05 AM (PST) PRESENTATION: The Evolving Landscape of Edge AI Ajay Nair - Google9:05 AM - 9:20 AM (PST) Comfort Break 9:20 AM - 9:45 AM (PST) PRESENTATION: InferX X1, The Fastest and Most Efficient Edge Inference Accelerator Cheng Wang - Flex Logix Technologies Inc.9:50 AM - 10:20 AM (PST) PRESENTATION: Implementing Edge Technologies in Retail: Walmart Case Study Alex Sabatier - Nvidia10:20 AM - 10:35 AM (PST) Comfort Break 10:35 AM - 11:20 AM (PST) Meet speaker! 11:20 AM - 11:50 AM (PST) PRESENTATION: The Era of Analog AI Compute Is Here Mike Henry - Mythic11:55 AM - 12:25 PM (PST) PRESENTATION: Using Edge AI To Detect Repetitive Mot Marcellino Gemelli - Bosch Sensortec12:30 PM - 2:30 PM (PST) NETWORKING - Dedicated Networking 2 hours for 1-2-1 Video MeetingsFriday, November 20, 20208:00 AM - 8:30 AM (PST) PRESENTATION: Spatial Computing: A Collision of Edge and Cloud-Based Computing Ashwin Swaminathan - Magic Leap8:35 AM - 9:05 AM (PST) PRESENTATION: Building An Autonomous Network For IoT and Edge Applications Anshul Bhatt - Rakuten Mobile9:05 AM - 9:20 AM (PST) Comfort Break 9:20 AM - 9:45 AM (PST) PRESENTATION: Practical Edge Inferencing: Enabling fastest AI inferencing per Watt leveraging sparsity Mahesh Makhijani - GrAI Matter Labs9:50 AM - 10:20 AM (PST) PRESENTATION: Large Scale Deep Learning and AI models on the Edge Chandra Khatri - Got It AI10:20 AM - 10:35 AM (PST) Comfort Break 10:35 AM - 11:20 AM (PST) NETWORKING: Interest Groups (18 people per room, topic-specific discussions) 11:20 AM - 11:50 AM (PST) PANEL DISCUSSION: The Symbiotic Relationship between 5G and Edge AI Sami Badri - Credit Suisse, Christos Kolias - Orange, Rima Raouda - Independent11:55 AM - 12:25 PM (PST) PANEL DISCUSSION: Investment Trends & Dynamics Panel Rashmi Gopinath - B Capital Group, Yvonne Lutsch - Bosch Venture Capital, Eileen Tanghal - In-Q-Tel, Albert Wang - Qualcomm Ventures12:30 PM - 12:50 PM (PST) PRESENTATION: The Edge: The Hottest Market for AI Accelerator Chips - Introducing the Kisaco Leadership Chart on AI Hardware Accelerators 2020-21: Edge and Automotive Michael Azoff - Kisaco ResearchWednesday, November 18, 2020A Software Solution Enabling Predictive Maintenance at the Sensor LevelSensiML Toolkit enables AI for a broad array of resource constrained time-series sensor endpoint applications. These include a wide range of consumer and industrial sensing applications.The problem is machine learning engineer do not have experience with embedded system and moving model to embedded system takes long time. AutoML for Embedded system usage. it is on the cloud. using the compiler for that device for this toolscost edge and cloud. easy to work on cloud. streaming data to cloud is difficult. faster if working on edge.TinyML addresses problems, battery powered, limited internet connectivity, securityprivacy, latency, economic Helping Fish Farmers Feed The World With Deep Learning Count sea lice and accurately measure biomass in real-time while reducing cage furniture. Our expertsintheloop ensure that every single prediction is correct. Aquabyte is seeking a Machine Learning Platform Engineer to drive the development, testing, and delivery of machine learning models that enable cutting-edge analytics and automation of fish farms around the world.Aquabyte is on a mission to revolutionize the sustainability and efficiency of aquaculture. It is an audacious, and incredibly rewarding mission. By making fish farming cheaper and more viable than livestock production, we aim to mitigate one of the biggest causes of climate change and help prepare our planet for impending population growth. Aquaculture is the single fastest growing food-production sector in the world, and now is the time to define how technology is used to harvest the sea for generations to come.We are currently focused on helping Norwegian salmon farmers better understand their fish populations and make environmentally-sound decisions. Through custom underwater cameras, computer vision, and machine learning we are able to quantify fish weights, detect sea lice infestations, and generate optimal feeding plans in real time. Our product operates at three levels: on-site hardware for image capture, cloud pipelines for data processing, and a user-facing web application. As a result, there are hundreds of moving pieces and no shortage of fascinating challenges across all levels of the stack.tinyMLPerf: Benchmarking Ultra-low Power Machine Learning Systems Deep Learning Benchmarks for Embedded DevicesThe goal of TinyMLPerf is to provide a representative set of deep neural nets and benchmarking code to compare performance between embedded devices. Embedded devices include microcontrollers, DSPs, and tiny NN accelerators. These devics typically run at between 10MHz and 250MHz, and can perform inference using less then 50mW of power. TinyMLPerf submissions will allow device makers and researchers to choose the best hardware for their use case, and allows hardware vendors to showcase their offerings. TinyMLPerf is primarily intended to benchmark hardware rather than new network archietctures, or embedded neural net runtimes. The reference benchmarks are provided using TensorFlow Lite for Microcontrollers (TFLM). Submitters can directly use the TFLM, although submitters are encouraged to use the software stack that works best on thier hardware.anomaly detection benchmark, visual wake words benchmark, Ultra-low power neuromorphic intelligence for the sensor edge Innatera Nanosystems BV (Innatera, (Innatera, innatera.com) is a rapidly-growing Dutch semiconductor company that develops ultra-efficient neuromorphic processors for AI at the edge. These microprocessors mimic the brains mechanisms for processing fast data streams from sensors, enabling complex turn-key sensor analytics functionalities, with 10,000x higher performance per watt than competing solutions. Innatera’s technology serves as a critical enabler for next-generation use-cases in the IoT, wearable, embedded, and automotive domains. How is AI affecting hearables and sensors? The Neural Network Menu is a collection of software that implements Neural Networks on Greenwaves Application Processors (GAP). This repository contains common mobile and edge NN archtecture examples, NN sample applications and full flagged reference designs. Our tools maps a TFLITE model (quantized or unquantized) onto gap. There is also a flow in the ingredients directory showing how to hand map from a Pytorch Model onto GAP. is a Proof of Concept Board that can be used for demonstration of battery-operated, edge computer vision applications based on GAP8.It incorporates GAPmod, a surface-mount module that implements all the layout sensitive portion of a GAP8 design, along with a VGA image sensor and a Bluetooth Low Energy radio.The GAPPoc-A board enables battery-operated applications developed around algorithms such as people counting, face-identification and many others to be quickly assembled and evaluated in the field. Breaking the Barriers to Deploy DNNs on Low-Power Hardware Deeplite, named to the 2020 CB Insights AI100 List of Most Innovative Artificial Intelligence Startups, is devoted to making fundamental advancements in accessible and efficient deep learning. Our solution helps deep learning engineers and experts automatically create faster, smaller and more energy-efficient deep neural networks. Industry leaders in computer vision, augmented reality and autonomous driving use our technology to unlock new possibilities for deep learning in the real world. At Deeplite, our vision is to create a lightweight intelligence thats accessible for daily life. Deeplite, we are tackling inference optimization of deep neural networks, making them faster and energy-efficient from cloud to edge computing. Our solution leverages state-of-the-art technology from elite universities to make deep neural networks applicable for any device, and our team works hard on the iterative evolution of the science behind deep neural networks to directly improve daily life.reduce the size of model 40xOptimizing ML Models At The Edge Made Simple is an energetic new company changing how developers optimize and deploy machine learning models for their AI needs. Were a team of machine learning systems leaders focused on making ML more efficient and easier to deploy by applying machine learning to it!OctoML is leveraging the power and traction of Apache TVM, an open source project originated by our founding team, to enable companies of every size to harness the power of deep learning without the expensive heavy lifting of tuning and securing models to each hardware configuration that a customer might need. Apache TVM and Deep Learning Compilation Conference, Wed-Fri, December 2nd-4th 2020, Free Virtual Event. Thursday, November 19, 2020Developing Edge AI Solutions For A Post-Pandemic Society Lightning Edge AI platform brings a groundbreaking dimension to IIoT and edge computing by embedding AI as close to the source of streaming sensor data as possible. The Edge AI software platform is a highly compact, advanced and feature-rich edge solution that delivers unprecedented low latency for onsite data processing, real-time analytics, ML and AI capabilities. It delivers the industrys lowest total cost for computing requirements, communications services, and cloud processing and storage.temperature detection, social distancing, cough detection, PPEMask detectionFlexible, customizable, integrated, actionableThe Evolving Landscape of Edge AI Corals local AI technology enables new possibilities across almost any kind of industryThe Coral Dev Board is a single-board computer that contains an Edge TPU coprocessor. It’s ideal for prototyping new projects that demand fast on-device inferencing for machine learning models. This page is your guide to get started. The setup requires flashing Mendel Linux to the board, and then accessing the board’s shell terminal. Once you have terminal access and update some of the software, we’ll show you how to run an image classification model on the board. If you want to learn more about the hardware, see the Dev Board datasheet.TPU v3, 32 to 512 TOPS, Q2 2021InferX X1, The Fastest and Most Efficient Edge Inference AcceleratorInferX X1: World’s fastest and most efficient Edge Inference Accelerator. We have just launched our first inference chip and it is the best in the world for edge inference. We are bringing up neural network models now and moving forward on the steps required for Q22021 chip and board production and Inference Compiler availability. mbedded FPGA, or eFPGA, enables your SoC to have flexibility in critical areas where algorithm, protocol or market needs are changing. FPGA can also accelerate many workloads faster than processors: Microsoft Azure uses one FPGA accelerator for every 2 Xeons.Flex Logix provides eFPGA cores which have density and performance similar to leading FPGAs in the same process node. Our EFLX eFPGA is silicon proven in 40nm, 2822nm, 16nm and 12nm. 67nm EFLX eFPGA is planned. Our eFPGA is based on a tile called EFLX 4K, which comes in two versions: all logic or mostly logic with some MACs (multiply-accumulators). The programmable logic is called LUTs (look up tables) that can implement any Boolean function. EFLX 4K Logix has 4000 LUT4 equivalents, EFLX 4K DSP has 3000 LUT4s and 40 Multiplier-Accumulators (MACs): the MAC has a 22-bit pre-adder, a 2222 multiple and a 48-bit post adderaccumulator. MACs can be combined or cascaded to form fast DSP functions. (For 40nm-180nm we offer an EFLX 1K tile).depth-wise conv2dImplementing Edge Technologies in Retail: Walmart Case StudyNVidiaThe Era of Analog AI Compute Is HereMythic products are based on a unique tile-based AI compute architecture that features three fundamental hardware technologies Compute-in-Memory, Dataflow Architecture, and Analog Computing. For AI developers, the Mythic SDK streamlines the preparation of trained neural networks for edge and low-latency datacenter deployments, and also performs automatic optimization and compilation of dataflow graphs for our unique architecture.low power consumption, ultra-low latency, high ai performance, large weight capacity, small form factor, cost effective solution Using Edge AI To Detect Repetitive MotBosch Sensortec develops and markets a wide portfolio of MEMS sensors and solutions for applications in smartphones, tablets, wearables, ARVR devices, drones, robots, smart home and the Internet of Things. Striving to meet the demanding requirements of the consumer electronics market, we provide best-in-class sensing solutions in terms of customer focus, quality and reliability, performance, sustainability and competitiveness. Friday, November 20, 2020Spatial Computing: A Collision of Edge and Cloud-Based Computing instance semantic segmentation contextual computing spatial computingSLAM: trackinglocalization, mapping: latency is critical for see through displaysweight is critical cannot compensate for lack of compute with more sensorsthermal is critical more sensors and more compute lead to heatrigidity leads to weight our device should be lightvery stringent requirements for MRwhy build a map: drift correction, robustness (pose recovery), persistencefeature descriptorsmatching across large baselines and illumination changes is challengingmost of the SOTA methods based on deep learning and not feasible withing compute budgetour deep descriptor is optimized for SLAM and provides the best trade off in terms of performance and computesemantic segmentation 3d point cloud Building An Autonomous Network For IoT and Edge Applications5G AI Practical Edge Inferencing: Enabling fastest AI inferencing per Watt leveraging sparsity worlds first sparsity-enabled AI processor optimized for ultra-low latency and low power processing at the edge.GrAI One drastically reduces application latency, for instance, it reduces the end-to-end latencies for deep learning networks such as PilotNet to the order of milliseconds. The GrAI One chip is based on GMLs innovative NeuronFlow technology that combines the dynamic Dataflow paradigm with sparse computing to produce massively parallel in-network processing.GrAI Matter Labs (www.graimatterlabs.ai), a fabless semiconductor company specialized in brain-inspired technology, designs and develops fully programmable ultra-low power neuromorphic HW for sensor analytics and machine learning. The company has offices in Eindhoven (NL), Paris (FR) and San Jose (USA) and has strong relations with top-ranking research groups on neuroscience, human vision and natural computationLarge Scale Deep Learning and AI models on the Edgedeployment pipelinesthere are several steps involved in the AIML life-cycleseveral tools to help simplify the whole processtensorflow extended (TFX): an end to end platform for deploying production ML pipelinesMLflow (other options michelangelo): an open source platform for the end to end machine learning life cycleapache airflow (other options kubeflow): an open source workflow management platformdataiku data science studio (DSS): collaborative data science software platform for teams of data scientist , data analysts, and engineers to explore prototype build and deliverThe Edge: The Hottest Market for AI Accelerator Chips - Introducing the Kisaco Leadership Chart on AI Hardware Accelerators 2020-21: Edge and Automotive OpenHTF is a Python library that provides a set of convenient abstractions designed to remove as much boilerplate as possible from hardware test setup and execution, so test engineers can focus primarily on test logic.Google SitesReport abusePage detailsPage updated Google SitesReport abuseThis site uses cookies from Google to deliver its services and to analyze traffic. 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Computer Vision, Deep Learning, Artificial superintelligence (ASI) - Search this siteSkip to main contentSkip to navigationComputer Vision, Deep Learning, Artificial superintelligence (ASI)HomeProductCoursesMachine Learning SpecializationMachine Learning Foundations: A Case Study ApproachFSDLFull Stack Deep LearningMLOpsROSParallel Programming Modern CPPCloud-NativeIoT Scholarship FoundationTensorFlow: Data and Deployment Specialization Workshops and EventsRISC-VEdge-AI-summitEmbedded IoTTeslaAI-HardwareOpenVINO Deep LearningMetaverseWorkshopsIFA2022Book Summary commonplace bookknowledgemanagementPKMTopics and ProjectsAIHubChatGPTHow to startYouTubeYouTube IISoftwareRoadmap for Image Processing Source CodeOpenCVCPPpythonMacOSOpenCVRustcompileIoTShareVideo TrackingCameraCalibrationDRLHardwareQuantum ComputingAltCoinResumeCVApplestartupLinksamazonAboutFQApromptComputer Vision, Deep Learning, Artificial superintelligence (ASI) - . . - . . 2 . . ( )- . . ( ). . - : . . - . - . ( ) - . .- . . . 1234 1235.- . 1234 1235 1234a1- ( ) - - - . - - - - , , , , , , - - - - Outline Note-Taking Method - Cornell Note-Taking Method - Boxing Note-Taking Method - Charting Note-Taking Method - Mapping Note-Taking Method - Sentence Note-Taking Method- - CODE - Capture: Saving valuable information from the internet and the world around you - Organize: Breaking that information into small chunks and preparing them for later use - Distill: Extracting the pieces of knowledge most relevant to your current goals - Express: Turning your knowledge into creative output that has an impact on others - PARA - Projects: series of tasks linked to a goal, with a deadline. - Areas: spheres of activity with a standard to be maintained over time. - Resources: topics or themes of ongoing interest. - Archives: inactive items from the other three categories. - productive - Home - Goals - Writings - Productivity - Creativity - Ideas - Zettelkasten - Fleeting Notes - Literature Notes - Permanent Notes Evergreen - (MOC (map of content - Slip-box title: How to take smart notes: One simple technique to boost writing, learning and thinkingauthors: Snke Ahrensyear: 2022ahrens2022take OBSIDIAN . JabRef . I use citation plugin 1. add path to the JabRef database “reading notesdh.bib” 2. create folder “Reading notes” 3. use CtrlShiftO to select reference 4. automatically create file based on that reference 5. CtrlShiftE to insert link to citation page1. www.tiziran.com2. www.pirahansiah.com 3. 6. 8. Zettlr is creating a Markdown editor for the 21st century Patreon( Choosing between Zettlr and Obsidian Aquiles Carattino( Zettlr vs Obsidian md detailed comparison as of 2022 - Slant( SitesReport abusePage detailsPage updated Google SitesReport abuseThis site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies.Learn moreGot it

Computer Vision, Deep Learning, Artificial superintelligence (ASI) - OpenCVSearch this siteSkip to main contentSkip to navigationComputer Vision, Deep Learning, Artificial superintelligence (ASI)HomeProductCoursesMachine Learning SpecializationMachine Learning Foundations: A Case Study ApproachFSDLFull Stack Deep LearningMLOpsROSParallel Programming Modern CPPCloud-NativeIoT Scholarship FoundationTensorFlow: Data and Deployment Specialization Workshops and EventsRISC-VEdge-AI-summitEmbedded IoTTeslaAI-HardwareOpenVINO Deep LearningMetaverseWorkshopsIFA2022Book Summary commonplace bookknowledgemanagementPKMTopics and ProjectsAIHubChatGPTHow to startYouTubeYouTube IISoftwareRoadmap for Image Processing Source CodeOpenCVCPPpythonMacOSOpenCVRustcompileIoTShareVideo TrackingCameraCalibrationDRLHardwareQuantum ComputingAltCoinResumeCVApplestartupLinksamazonAboutFQApromptComputer Vision, Deep Learning, Artificial superintelligence (ASI)OpenCVDownload source code (GitHub): CPythonNuGet - OpenCV 5 betacvtest: Computer Vision TestUnit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep LearningStandard test for computer vision applicationAdvanced OpenCV techniques:Advanced OpenCV techniques:Advanced OpenCV techniques: balance whiteAdvanced OpenCV techniques: contrast and brightnessAdvanced subpixel techniques: Shift image contentMesh grid floatmesh grid intmain:Tips and Tricks of OpenCV that Nobody Told YouTricksTipsSave resultsErrorTesting for OpenCV ProjectsTricksTipsExampleYouTubeNuGet - OpenCV 5 betaNuGet packages for OpenCV 5 - Static Library for Visual Studio 2019 and 2022 Install and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static opencv library for visual studio 2022 by using NuGet package manager just in few minutes C, Computer Vision, Image Processing, download source code (GitHub): NuGet packages comprised of two versions for different VS version. Visual Studio 2019 Install-Package OpenCV5StaticLibVS2019NuGet -Version 2022.7.7Visual Studio 2022 Install-Package OpenCV5StaticLibVS22NuGet -Version 2022.7.7 more: cvtest: Computer Vision TestUnit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep LearningDo you want to test your output of computer vision application which is video or images?Standard test for computer vision applicationThere isn’t any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware.Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? source code (GitHub): code in C: cvtestAdvanced OpenCV techniques:sub-pixel, floating points, more precise, real-valued coordinates, Changing the contrast and brightness of an image in CV32FC3 with OpenCV, Tips and Tricks of OpenCV that Nobody Told Youdownload source code (GitHub): code in C: Advanced OpenCV techniques:Cross correlation (CC): TMCCORRMean shifted cross correlation (Pearson correlation coefficient): TMCCOEFFNormalization: TMSQDIFFNORMED, TMCCORRNORMED, TMCCOEFFNORMEDmaximum absolute difference metric (MaxAD), which is also known as the uniform distance metric computeECC() and findTransformECC().Sum of absolute differences (SAD)Cross correlation (CC)find identical regions of an image that match a template, select by giving a threshold 2D convolutionIt simply slides the template image over the input image (as in 2D convolution) and compares the template and patch of input image under the template image. Template matching cv2.TMCCOEFF cv2.TMCCOEFFNORMED cv2.TMCCORR cv2.TMCCORRNORMED(y); for (int x 0; x (1 - discardratio) total) vmaxi - 1; if (vmaxi (y); for (int x 0; x vmaxj) val vmaxj; ptrx 3 j staticcast((val - vminj) 255.0 (vmaxj - vminj)); reference Advanced OpenCV techniques: contrast and brightnesssub-pixel, floating points, more precise, real-valued coordinates, Changing the contrast and brightness of an image in CV32FC3 with OpenCV, Tips and Tricks of OpenCV that Nobody Told Youmore: code in C: showImage.convertTo(showImage, CV32FC3); double alpha 2.0; (y, x)c cv::saturatecast(alpha showImage.at(y, x)c beta); showImage.convertTo(showImage, CV8UC3); cv::imshow(“Changing the contrast and brightness of an image! “, showImage); cv::waitKey(0);based on Advanced subpixel techniques: Shift image contentsub-pixel, floating points, mesh grid, remap, more precise, real-valued coordinates, moving image pixel, Shift image content with OpenCV, Tips and Tricks of OpenCV that Nobody Told Youmore: code in C:Mesh grid floatstatic void meshgridfloat(const cv::Mat& xgv, const cv::Mat& ygv, cv::Mat1f& X, cv::Mat1f& Y) cv::repeat(xgv.reshape(1, 1), ygv.total(), 1, X); cv::repeat(ygv.reshape(1, 1).t(), 1, xgv.total(), Y);static void meshgridmapfloat(const cv::Range& xgv, const cv::Range& ygv, cv::Mat1f& X, cv::Mat1f& Y) std::vector tx, ty; for (int i xgv.start; i tx, ty; for (int i xgv.start; i (rowsImage, colsImage) offset1; YF.at(rowsImage, colsImage) offset2; cv::remap(convertimgi, dst, XF, YF, cv::INTERLINEAR); if (show) cv::Mat resizedImage dst.clone(); dst.convertTo(resizedImage, CV8UC3); cv::resize(resizedImage, resizedImage, cv::Size(), 0.5, 0.5); std::string nameWindow “ meshgrid and remap in float “; cv::imshow(nameWindow, resizedImage); cv::waitKey(0); Tips and Tricks of OpenCV that Nobody Told YouTips and Tricks of OpenCV that Nobody Told Youmore: code in C:Trickscv::multiply(outMat, cv::Scalar(gain, gain, gain), outMat); colorcv::multiply(outMat, cv::Scalar(gain), outMat); grayscalecopy small Mat to bigger Matcv::Rect roi( cv::Point( originX, originY ), smallImage.size() );smallImage.copyTo( bigImage( roi ) ); Tipscopy mat to vector need clone()Save resultssave image in float cv::imwrite(“image.exr”, MatImage); save image in uncompressed format :cv::imwrite(“fa.tiff”, resizedImage, cv::IMWRITETIFFCOMPRESSION, 1,cv::IMWRITETIFFXDPI, 300,cv::IMWRITETIFFYDPI,300 );cv2.imwrite(filename, array, params(cv2.IMWRITETIFFCOMPRESSION, 1)) Nonecv2.imwrite(filename, array, params(cv2.IMWRITETIFFCOMPRESSION, 5)) LZWcv2.imwrite(filename, array, params(cv2.IMWRITETIFFCOMPRESSION, 8)) Adobe Deflatecv2.imwrite(filename, array, params(cv2.IMWRITETIFFCOMPRESSION, 32946)) Deflatecv::imwrite(“far.png”, resizedImage, cv::IMWRITEPNGCOMPRESSION, 0 );Save images in streaming int64 t0 cv::getTickCount(); std::string fileName “fashid”std::tostring(t0) “.png”; cv::imwrite(fileName, cimMat, cv::IMWRITEPNGCOMPRESSION, 0 );Save file name:std::filesystem::path p std::filesystem::path(filesi).filename();std::string imgFile savePath “” p.string() “.tiff”;cv2.imwrite(filename, array, params(cv2.IMWRITETIFFCOMPRESSION, 1)) ;ErrorException thrown at 0x00007FFB5CB21636 (vcruntime140d.dll) in farshid.exe: 0xC0000005: Access violation writing location 0x000002B09BAC4000.check the size of Mat cv::Mat meanImages cv::Mat::zeros(rowsmain, colsmain, CV32F);cv::Mat meanImages cv::Mat::zeros(farshidMat.size(), CV32F);Linker Tools Error LNK2038 & Linker Tools Error LNK2001; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for ‘ITERATORDEBUGLEVEL’: value ‘2’ doesn’t match value ‘0’ in Source.obj ; Severity Code Description Project File Line Suppression State Error LNK2038 mismatch detected for ‘RuntimeLibrary’: value ‘MTdStaticDebug’ doesn’t match value ‘MTStaticRelease’ the link files are not match based on release or debug mode. Testing for OpenCV ProjectsTest, C, Computer Vision, Image Processing, more: code in C:Arrange, Act and Assert (AAA) PatternGoogle C Test FrameworkAssertions Types and Test Fixtures ASSERTFALSE(frame.empty()); ASSERTNOTHROW(cap img); ASSERTFALSE(img.empty()) assert(!im.empty());assert(x.size()y.size());assert(x.size()2); DEBUG true YouTubeInstall and setup your OpenCV project in just 5 minutes Config your visual studio project for computer vision application static opencv library for visual studio 2022 by using NuGet package manager just in few minutes C, Computer Vision, Image Processing, more: A set of C APIs are provided to mimic the same behaviors as the MATLAB function “linspace” and “meshgrid”.Must-Read Books of All Time in Computer Vision and Machine Learning Google SitesReport abusePage detailsPage updated Google SitesReport abuseThis site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. 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Computer Vision, Deep Learning, Artificial superintelligence (ASI) - How to startSearch this siteSkip to main contentSkip to navigationComputer Vision, Deep Learning, Artificial superintelligence (ASI)HomeProductCoursesMachine Learning SpecializationMachine Learning Foundations: A Case Study ApproachFSDLFull Stack Deep LearningMLOpsROSParallel Programming Modern CPPCloud-NativeIoT Scholarship FoundationTensorFlow: Data and Deployment Specialization Workshops and EventsRISC-VEdge-AI-summitEmbedded IoTTeslaAI-HardwareOpenVINO Deep LearningMetaverseWorkshopsIFA2022Book Summary commonplace bookknowledgemanagementPKMTopics and ProjectsAIHubChatGPTHow to startYouTubeYouTube IISoftwareRoadmap for Image Processing Source CodeOpenCVCPPpythonMacOSOpenCVRustcompileIoTShareVideo TrackingCameraCalibrationDRLHardwareQuantum ComputingAltCoinResumeCVApplestartupLinksamazonAboutFQApromptComputer Vision, Deep Learning, Artificial superintelligence (ASI)How to startUpdate July 2022 Visual codeBasic of Programming and toolsBasic of machine learningDeep Learning SpecializationFull Stack Deep Learning - Spring 2021Machine Learning Engineering for Production (MLOps) SpecializationLearn more aboutBest practiceYouTubeBest Books IDE: Visual code Basic of Programming and toolsLecture: Modern C (Summer 2018, Uni Bonn); Cyrill StachnissPython Crash CourseHands-On GPU-Accelerated Computer Vision with OpenCV and CUDA: Effective tech… Git from Basics to Advanced: Practical Guide for DevelopersBasic of machine learningMachine Learning (Stanford University)Neural Networks for Machine Learning University of Toronto - Geoffrey E. HintonDeep Learning SpecializationFull Stack Deep Learning - Spring 2021Machine Learning Engineering for Production (MLOps) SpecializationLearn more about End to End solution for machine vision, post product specialist, performance engineer, teamsite lead researcher, platform, design deep learning architecture, support specialist, senior technical assistant, … www.tiziran.com Best practice The Complete Self-Driving Car Course - Applied Deep LearningPyTorch for Deep Learning and Computer VisionYouTubeFree online videos ( Image Processing)Free online videos (OpenCV)Best BooksMachine Learning Engineering by Andriy Burkov Google SitesReport abusePage detailsPage updated Google SitesReport abuseThis site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies.Learn moreGot it

Computer Vision, Deep Learning, Artificial superintelligence (ASI) - PKMSearch this siteSkip to main contentSkip to navigationComputer Vision, Deep Learning, Artificial superintelligence (ASI)HomeProductCoursesMachine Learning SpecializationMachine Learning Foundations: A Case Study ApproachFSDLFull Stack Deep LearningMLOpsROSParallel Programming Modern CPPCloud-NativeIoT Scholarship FoundationTensorFlow: Data and Deployment Specialization Workshops and EventsRISC-VEdge-AI-summitEmbedded IoTTeslaAI-HardwareOpenVINO Deep LearningMetaverseWorkshopsIFA2022Book Summary commonplace bookknowledgemanagementPKMTopics and ProjectsAIHubChatGPTHow to startYouTubeYouTube IISoftwareRoadmap for Image Processing Source CodeOpenCVCPPpythonMacOSOpenCVRustcompileIoTShareVideo TrackingCameraCalibrationDRLHardwareQuantum ComputingAltCoinResumeCVApplestartupLinksamazonAboutFQApromptComputer Vision, Deep Learning, Artificial superintelligence (ASI)Personal knowledge management (PKM)Google SitesReport abusePage detailsPage updated Google SitesReport abuseThis site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By clicking “accept”, you agree to its use of cookies. Cookie PolicyRejectAccept

Computer Vision, Deep Learning, Artificial superintelligence (ASI) - DRLSearch this siteSkip to main contentSkip to navigationComputer Vision, Deep Learning, Artificial superintelligence (ASI)HomeProductCoursesMachine Learning SpecializationMachine Learning Foundations: A Case Study ApproachFSDLFull Stack Deep LearningMLOpsROSParallel Programming Modern CPPCloud-NativeIoT Scholarship FoundationTensorFlow: Data and Deployment Specialization Workshops and EventsRISC-VEdge-AI-summitEmbedded IoTTeslaAI-HardwareOpenVINO Deep LearningMetaverseWorkshopsIFA2022Book Summary commonplace bookknowledgemanagementPKMTopics and ProjectsAIHubChatGPTHow to startYouTubeYouTube IISoftwareRoadmap for Image Processing Source CodeOpenCVCPPpythonMacOSOpenCVRustcompileIoTShareVideo TrackingCameraCalibrationDRLHardwareQuantum ComputingAltCoinResumeCVApplestartupLinksamazonAboutFQApromptComputer Vision, Deep Learning, Artificial superintelligence (ASI)My paper: A Comprehensive Review on Deep Reinforcement LearningThe updates2021YouTubeNotes and infoLinks:Reading List (Video, Conference, Workshop, Paper)The updatesDear friends, I recently wrote a survey paper on “A Comprehensive Review on Deep Reinforcement Learning: A Survey”, with some of the leading AI and DRL researchers (including): In this work, we covered top recent DRL works, grouped into several categories. We were lucky to have you, as the external reviewers of this work. I hope this is useful for the research community. Any feedback will be highly welcomed. You can find its summary here too. Imitation learning, expert (teacher), hierarchical, hybrid imitation, high performance parallelism, 2021NeurIPS 2020: Key Research Papers in Reinforcement Learning and MoreKey Papers in Deep RLYouTubeSimple Deep Q Network wPytorch: Learning Crash Course: Gradients wTensorflow: Q Learning wTensorflow Your Own RL Environments to Spec a Deep Learning PC: Q Learning w Pytorch: Learning Freelancing from video: and infotraining on unlabeled data, lifelong learning, and especially letting models explore a simulated environment before transferring what they learn to the real worldLately, simulation has helped achieve impressive results in reinforcement learning, which is extremely data-intensive.using reinforcement learning to train robots that reason about how their actions will affect their environment.How is it that many people learn to drive a car fairly safely in 20 hours of practice, while current imitation learning algorithms take hundreds of thousands of hours, and reinforcement learning algorithms take millions of hours? Clearly were missing something big.In 2021, I expect self-supervised methods to learn features of video and images. Could there be a similar revolution in high-dimensional continuous data like video?One critical challenge is dealing with uncertainty. Models like BERT cant tell if a missing word in a sentence is cat or dog, but they can produce a probability distribution vector. We dont have a good model of probability distributions for images or video frames. But recent research is coming so close that were likely to find it soon.Suddenly well get really good performance predicting actions in videos with very few training samples, where it wasnt possible before. That would make the coming year a very exciting time in AI.DeepMind released the code, model, & dataset behind their groundbreaking “AlphaFold” system. It’s predicts protein shapes from genomic data with apps in health, sustainability, & materials designLinks: List (Video, Conference, Workshop, Paper) DeepMind Open-Sources Lab2D, A System For The Creation Of 2D Environments For Machine LearningGithub: Google to release DeepMind’s StreetLearn for teaching machine-learning agents to navigate cities agent alignment via reward modeling DeepMind Safety Research Medium DeepMind Can Support, Defeat Humans in Quake III Arena - ExtremeTech You searched for deep mind - ExtremeTech DeepMind AI Moves on from Board Games to StarCraft II - ExtremeTech DeepMind AI Challenges Pro StarCraft II Players, Wins Almost Every Match - ExtremeTech Google’s DeepMind AI gets a few new tricks to learn faster Robot armThere are 4 Courses in this SpecializationCourse1Fundamentals of Reinforcement Learning4.8stars801 ratings205 reviewsReinforcement Learning is a subfield of Machine Learning, but is also a general purpose formalism for automated decision-making and AI. This course introduces you to statistical learning techniques where an agent explicitly takes actions and interacts with the world. Understanding the importance and challenges of learning agents that make decisions is of vital importance today, with more and more companies interested in interactive agents and intelligent decision-making.This course introduces you to the fundamentals of Reinforcement Learning. When you finish this course, you will: - Formalize problems as Markov Decision Processes - Understand basic exploration methods and the explorationexploitation tradeoff - Understand value functions, as a general-purpose tool for optimal decision-making - Know how to implement dynamic programming as an efficient solution approach to an industrial control problem This course teaches you the key concepts of Reinforcement Learning, underlying classic and modern algorithms in RL. After completing this course, you will be able to start using RL for real problems, where you have or can specify the MDP. This is the first course of the Reinforcement Learning Specialization.SHOW ALL ABOUT A COURSE IN THIS SPECIALIZATIONSHOW ALLCourse2Sample-based Learning Methods4.8stars397 ratings75 reviewsIn this course, you will learn about several algorithms that can learn near optimal policies based on trial and error interaction with the environment—learning from the agents own experience. Learning from actual experience is striking because it requires no prior knowledge of the environments dynamics, yet can still attain optimal behavior. We will cover intuitively simple but powerful Monte Carlo methods, and temporal difference learning methods including Q-learning. We will wrap up this course investigating how we can get the best of both worlds: algorithms that can combine model-based planning (similar to dynamic programming) and temporal difference updates to radically accelerate learning.By the end of this course you will be able to: - Understand Temporal-Difference learning and Monte Carlo as two strategies for estimating value functions from sampled experience - Understand the importance of exploration, when using sampled experience rather than dynamic programming sweeps within a model - Understand the connections between Monte Carlo and Dynamic Programming and TD. - Implement and apply the TD algorithm, for estimating value functions - Implement and apply Expected Sarsa and Q-learning (two TD methods for control) - Understand the difference between on-policy and off-policy control - Understand planning with simulated experience (as opposed to classic planning strategies) - Implement a model-based approach to RL, called Dyna, which uses simulated experience - Conduct an empirical study to see the improvements in sample efficiency when using DynaSHOW ALL ABOUT A COURSE IN THIS SPECIALIZATIONSHOW ALLCourse3Prediction and Control with Function Approximation4.8stars252 ratings40 reviewsIn this course, you will learn how to solve problems with large, high-dimensional, and potentially infinite state spaces. You will see that estimating value functions can be cast as a supervised learning problem—function approximation—allowing you to build agents that carefully balance generalization and discrimination in order to maximize reward. We will begin this journey by investigating how our policy evaluation or prediction methods like Monte Carlo and TD can be extended to the function approximation setting. You will learn about feature construction techniques for RL, and representation learning via neural networks and backprop. We conclude this course with a deep-dive into policy gradient methods; a way to learn policies directly without learning a value function. In this course you will solve two continuous-state control tasks and investigate the benefits of policy gradient methods in a continuous-action environment.Prerequisites: This course strongly builds on the fundamentals of Courses 1 and 2, and learners should have completed these before starting this course. Learners should also be comfortable with probabilities & expectations, basic linear algebra, basic calculus, Python 3.0 (at least 1 year), and implementing algorithms from pseudocode. By the end of this course, you will be able to: -Understand how to use supervised learning approaches to approximate value functions -Understand objectives for prediction (value estimation) under function approximation -Implement TD with function approximation (state aggregation), on an environment with an infinite state space (continuous state space) -Understand fixed basis and neural network approaches to feature construction -Implement TD with neural network function approximation in a continuous state environment -Understand new difficulties in exploration when moving to function approximation -Contrast discounted problem formulations for control versus an average reward problem formulation -Implement expected Sarsa and Q-learning with function approximation on a continuous state control task -Understand objectives for directly estimating policies (policy gradient objectives) -Implement a policy gradient method (called Actor-Critic) on a discrete state environmentSHOW ALL ABOUT A COURSE IN THIS SPECIALIZATIONSHOW ALLCourse4A Complete Reinforcement Learning System (Capstone)4.6stars177 ratings33 reviewsIn this final course, you will put together your knowledge from Courses 1, 2 and 3 to implement a complete RL solution to a problem. This capstone will let you see how each component—problem formulation, algorithm selection, parameter selection and representation design—fits together into a complete solution, and how to make appropriate choices when deploying RL in the real world. This project will require you to implement both the environment to stimulate your problem, and a control agent with Neural Network function approximation. In addition, you will conduct a scientific study of your learning system to develop your ability to assess the robustness of RL agents. To use RL in the real world, it is critical to (a) appropriately formalize the problem as an MDP, (b) select appropriate algorithms, (c ) identify what choices in your implementation will have large impacts on performance and (d) validate the expected behaviour of your algorithms. This capstone is valuable for anyone who is planning on using RL to solve real problems.To be successful in this course, you will need to have completed Courses 1, 2, and 3 of this Specialization or the equivalent. By the end of this course, you will be able to: Complete an RL solution to a problem, starting from problem formulation, appropriate algorithm selection and implementation and empirical study into the effectiveness of the solution.SHOW ALL ABOUT A COURSE IN THIS SPECIALIZATIONSHOW ALL Using pre trained model to train deeper and lager modelImitation Learning Safety Gym, a suite of environments and tools for measuring progress towards reinforcement learning agents that respect safety constraints while training. It also provides a standardized method of comparing algorithms and how well they avoid costly mistakes while learning. If deep reinforcement learning is applied to the real world, whether in robotics or internet-based tasks, it will be important to have algorithms that are safe even while learninglike a self-driving car that can learn to avoid accidents without actually having to experience them. Credit: Two Minute Papers, OpenAI Follow me for more AI Datascience posts: Safety Gym: A Safe Place For AIs To Learn DeepMind proposes novel way to train safe reinforcement learning AI The Batch Issue 35Different Skills From Different DemosReinforcement learning trains models by trial and error. In batch reinforcement learning (BRL), models learn by observing many demonstrations by a variety of actors. For instance, a robot might learn how to fix ingrown toenails by watching hundreds of surgeons perform the procedure. But what if one doctor is handier with a scalpel while another excels at suturing? A new method lets models absorb the best skills from each.Whats new: Ajay Mandlekar and collaborators at Nvidia, Stanford, and the University of Toronto devised a BRL technique that enables models to learn different portions of a task from different examples. This way, the model can gain useful information from inconsistent examples. Implicit Reinforcement without Interaction at Scale (IRIS) achieved state-of-the-art BRL performance in three tasks performed in a virtual environment.Key insight: Learning from demonstrations is a double-edged sword. An agent gets to see how to complete a task, but the scope of its action is limited to the most complete demonstration of a given task. IRIS breaks down tasks into sequences of intermediate subgoals. Then it performs the actions required to accomplish each subgoal. In this way, the agent learns from the best parts of each demonstration and combines them to accomplish the task.How it works: IRIS includes a subgoal selection model that predicts intermediate points on the way to accomplishing an assigned task. These subgoals are defined automatically by the algorithm, and may not correspond to parts of a task as humans would describe them. A controller network tries to replicate the optimal sequence of actions leading to a given subgoal.The subgoal selection model is made up of a conditional variational autoencoder that produces a set of possible subgoals and a value function (trained via a BRL version of Q-learning) that predicts which next subgoal will lead to the highest reward.The controller is a recurrent neural network that decides on the actions required to accomplish the current subgoal. It learns to predict how demonstrations tend to unfold, and to imitate short sequences of actions from specific demonstrations.Once its trained, the subgoal selection model determines the next subgoal. The controller takes the requisite actions. Then the subgoal selection model evaluates the current state and computes a new subgoal, and so on.Results: In the Robosuite’s lifting and pick-and-place tasks, previous state-of-the-art BRL approaches couldn’t pick up objects reliably, nor place them elsewhere at all. IRIS learned to pick up objects with over 80 percent success and placed them with 30 percent success.Why it matters: Automatically identifying subgoals has been a holy grail in reinforcement learning, with active research in hierarchical RL and other areas. The method used in this paper applies to relatively simple tasks where things happen in a predictable sequence (such as picking and then placing), but might be a small step in an important direction.Were thinking: Batch reinforcement learning is useful when a model must be interpretable or safe after all, a robotic surgeon shouldnt experiment on living patients but it hasnt been terribly effective. IRIS could make it a viable option. Dec 11, 2019Issue 34 Seeing the World BlindfoldedIn reinforcement learning, if researchers want an agent to have an internal representation of its environment, theyll build and train a world model that it can refer to. New research shows that world models can emerge from standard training, rather than needing to be built separately.Whats new: Google Brain researchers C. Daniel Freeman, Luke Metz, and David Ha enabled an agent to build a world model by blindfolding it as it learned to accomplish tasks. They call their approach observational dropout.Key insight: Blocking an agent’s observations of the world at random moments forces it to generate its own internal representation to fill in the gaps. The agent learns this representation without being instructed to predict how the environment will change in response to its actions.How it works: At every timestep, the agent acts on either its observation (framed in red in the video above) or its prediction of what it wasnt able to observe (imagery not framed in red). The agent contains a controller that decides on the most rewarding action. To compute the potential reward of a given action, the agent includes an additional deep net trained using the RL algorithm REINFORCE.Observational dropout blocks the agent from observing the environment according to a user-defined probability. When this happens, the agent predicts an observation.If random blindfolding blocks several observations in a row, the agent uses its most recent prediction to generate the next one.This procedure over many iterations produces a sequence of observations and predictions. The agent learns from this sequence, and its ability to predict blocked observations is tantamount to a world model.Results: Observational dropout solved the task known as Cartpole, in which the model must balance a pole upright on a rolling cart, even when its view of the world was blocked 90 percent of the time. In a more complex Car Racing task, in which a model must navigate a car around a track as fast as possible, the model performed almost equally well whether it was allowed to see its surroundings or blindfolded up to 60 percent of the time.Why it matters: Modeling reality is often part art and part science. World models generated by observational dropout arent perfect representations, but theyre sufficient for some tasks. This work could lead to simple-but-effective world models of complex environments that are impractical to model completely.Were thinking: Technology being imperfect, observational dropout is a fact of life, not just a research technique. A self-driving car or auto-piloted airplane reliant on sensors that drop data points could create a catastrophe. This technique could make high-stakes RL models more robust. Dec 4, 2019Issue 33 Is AI Making Mastery Obsolete?Is there any reason to continue playing games that AI has mastered? Ask the former champions who have been toppled by machines.What happened: In 2016, International Go master Lee Sedol famously lost three out of four matches to DeepMinds AlphaGo model. The 36-year-old announced his retirement from competition on November 27. Even if I become the number one, there is an entity that cannot be defeated, he told South Korean’s Yonhap News Agency,Stages of grief: Prior to the tournament, Lee predicted that he would defeat AlphaGo easily. But the models inexplicable and indefatigable playing style pushed him into fits of shock and disbelief. Afterward, he apologized for his failure to the South Korean public.Reaching acceptance: Garry Kasparov, the former world-champion chess player, went through his own cycle of grief after being defeated by IBMs DeepBlue in 1997. Although he didnt retire, Kasparov did accuse IBMs engineers of cheating. He later retracted the charge, and in 2017 wrote a book arguing that, if humans can overcome their feelings of being threatened by AI, they can learn from it. The book advocates an augmented intelligence in which humans and machines work together to solve problems.The human element: Although AlphaGo won in the 2016 duel, its human opponent still managed to shine. During the fourth match, Sedol made a move so unconventional it defied AlphaGos expectation and led to his sole victory.Were thinking: Lee wasn’t defeated by a machine alone. He was beaten by a machine built by humans under the direction of AlphaGo research lead David Silver. Human mastery is obsolete only if you ignore people like Silver and his team. Google SitesReport abusePage detailsPage updated Google SitesReport abuseThis site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies.Learn moreGot it

Computer Vision, Deep Learning, Artificial superintelligence (ASI) - Machine Learning SpecializationSearch this siteSkip to main contentSkip to navigationComputer Vision, Deep Learning, Artificial superintelligence (ASI)HomeProductCoursesMachine Learning SpecializationMachine Learning Foundations: A Case Study ApproachFSDLFull Stack Deep LearningMLOpsROSParallel Programming Modern CPPCloud-NativeIoT Scholarship FoundationTensorFlow: Data and Deployment Specialization Workshops and EventsRISC-VEdge-AI-summitEmbedded IoTTeslaAI-HardwareOpenVINO Deep LearningMetaverseWorkshopsIFA2022Book Summary commonplace bookknowledgemanagementPKMTopics and ProjectsAIHubChatGPTHow to startYouTubeYouTube IISoftwareRoadmap for Image Processing Source CodeOpenCVCPPpythonMacOSOpenCVRustcompileIoTShareVideo TrackingCameraCalibrationDRLHardwareQuantum ComputingAltCoinResumeCVApplestartupLinksamazonAboutFQApromptComputer Vision, Deep Learning, Artificial superintelligence (ASI)Machine Learning Specialization 2022Coursera: Machine Learning Specialization (2022)Course 2: Advanced Learning AlgorithmsWeek 2: Neural network trainingSee more & download other notes: Machine Learning Specialization (2022)Course 2: Advanced Learning AlgorithmsWeek 1: NNSee more: Machine Learning Specialization (2022)Course 1: Supervised Machine Learning: Regression and ClassificationWeek 3: ClassificationSee more: Machine Learning SpecializationCourse 1: Supervised Machine Learning: Regression and ClassificationWeek 2: Regression with multiple input variablesSee more: Machine Learning SpecializationCourse 1: Supervised Machine Learning: Regression and ClassificationWeek 1: Introduction to Machine Learning See more: Learning SpecializationCourse 1 Week 2See more: you found the content informative, you may Follow me by for more!  COURSE1Machine Learning Foundations: A Case Study Approach4.6stars13,044 ratings3,104 reviewsDo you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems?In this course, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of the first course you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images. Through hands-on practice with these use cases, you will be able to apply machine learning methods in a wide range of domains.This first course treats the machine learning method as a black box. Using this abstraction, you will focus on understanding tasks of interest, matching these tasks to machine learning tools, and assessing the quality of the output. In subsequent courses, you will delve into the components of this black box by examining models and algorithms. Together, these pieces form the machine learning pipeline, which you will use in developing intelligent applications.Learning Outcomes: By the end of this course, you will be able to: -Identify potential applications of machine learning in practice. -Describe the core differences in analyses enabled by regression, classification, and clustering. -Select the appropriate machine learning task for a potential application. -Apply regression, classification, clustering, retrieval, recommender systems, and deep learning. -Represent your data as features to serve as input to machine learning models. -Assess the model quality in terms of relevant error metrics for each task. -Utilize a dataset to fit a model to analyze new data. -Build an end-to-end application that uses machine learning at its core. -Implement these techniques in Python.SHOW ALL ABOUT MACHINE LEARNING FOUNDATIONS: A CASE STUDY APPROACHSHOW ALLCOURSE2Machine Learning: Regression4.8stars5,470 ratings1,016 reviewsCase Study - Predicting Housing PricesIn our first case study, predicting house prices, you will create models that predict a continuous value (price) from input features (square footage, number of bedrooms and bathrooms,…). This is just one of the many places where regression can be applied. Other applications range from predicting health outcomes in medicine, stock prices in finance, and power usage in high-performance computing, to analyzing which regulators are important for gene expression.In this course, you will explore regularized linear regression models for the task of prediction and feature selection. You will be able to handle very large sets of features and select between models of various complexity. You will also analyze the impact of aspects of your data – such as outliers – on your selected models and predictions. To fit these models, you will implement optimization algorithms that scale to large datasets.Learning Outcomes: By the end of this course, you will be able to: -Describe the input and output of a regression model. -Compare and contrast bias and variance when modeling data. -Estimate model parameters using optimization algorithms. -Tune parameters with cross validation. -Analyze the performance of the model. -Describe the notion of sparsity and how LASSO leads to sparse solutions. -Deploy methods to select between models. -Exploit the model to form predictions. -Build a regression model to predict prices using a housing dataset. -Implement these techniques in Python.SHOW ALL ABOUT MACHINE LEARNING: REGRESSIONSHOW ALLCOURSE3Machine Learning: Classification4.7stars3,662 ratings603 reviewsCase Studies: Analyzing Sentiment & Loan Default PredictionIn our case study on analyzing sentiment, you will create models that predict a class (positivenegative sentiment) from input features (text of the reviews, user profile information,…). In our second case study for this course, loan default prediction, you will tackle financial data, and predict when a loan is likely to be risky or safe for the bank. These tasks are an examples of classification, one of the most widely used areas of machine learning, with a broad array of applications, including ad targeting, spam detection, medical diagnosis and image classification. In this course, you will create classifiers that provide state-of-the-art performance on a variety of tasks. You will become familiar with the most successful techniques, which are most widely used in practice, including logistic regression, decision trees and boosting. In addition, you will be able to design and implement the underlying algorithms that can learn these models at scale, using stochastic gradient ascent. You will implement these technique on real-world, large-scale machine learning tasks. You will also address significant tasks you will face in real-world applications of ML, including handling missing data and measuring precision and recall to evaluate a classifier. This course is hands-on, action-packed, and full of visualizations and illustrations of how these techniques will behave on real data. We’ve also included optional content in every module, covering advanced topics for those who want to go even deeper! Learning Objectives: By the end of this course, you will be able to: -Describe the input and output of a classification model. -Tackle both binary and multiclass classification problems. -Implement a logistic regression model for large-scale classification. -Create a non-linear model using decision trees. -Improve the performance of any model using boosting. -Scale your methods with stochastic gradient ascent. -Describe the underlying decision boundaries. -Build a classification model to predict sentiment in a product review dataset. -Analyze financial data to predict loan defaults. -Use techniques for handling missing data. -Evaluate your models using precision-recall metrics. -Implement these techniques in Python (or in the language of your choice, though Python is highly recommended).SHOW ALL ABOUT MACHINE LEARNING: CLASSIFICATIONSHOW ALLCOURSE4Machine Learning: Clustering & Retrieval4.7stars2,294 ratings392 reviewsCase Studies: Finding Similar DocumentsA reader is interested in a specific news article and you want to find similar articles to recommend. What is the right notion of similarity? Moreover, what if there are millions of other documents? Each time you want to a retrieve a new document, do you need to search through all other documents? How do you group similar documents together? How do you discover new, emerging topics that the documents cover? In this third case study, finding similar documents, you will examine similarity-based algorithms for retrieval. In this course, you will also examine structured representations for describing the documents in the corpus, including clustering and mixed membership models, such as latent Dirichlet allocation (LDA). You will implement expectation maximization (EM) to learn the document clusterings, and see how to scale the methods using MapReduce.Learning Outcomes: By the end of this course, you will be able to: -Create a document retrieval system using k-nearest neighbors. -Identify various similarity metrics for text data. -Reduce computations in k-nearest neighbor search by using KD-trees. -Produce approximate nearest neighbors using locality sensitive hashing. -Compare and contrast supervised and unsupervised learning tasks. -Cluster documents by topic using k-means. -Describe how to parallelize k-means using MapReduce. -Examine probabilistic clustering approaches using mixtures models. -Fit a mixture of Gaussian model using expectation maximization (EM). -Perform mixed membership modeling using latent Dirichlet allocation (LDA). -Describe the steps of a Gibbs sampler and how to use its output to draw inferences. -Compare and contrast initialization techniques for non-convex optimization objectives. -Implement these techniques in Python.SHOW ALL ABOUT MACHINE LEARNING: CLUSTERING & RETRIEVALSHOW ALL Download source code and full text of Mind map from Google SitesReport abusePage detailsPage updated Google SitesReport abuseThis site uses cookies from Google to deliver its services and to analyze traffic. 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Computer Vision, Deep Learning, Artificial superintelligence (ASI) - Source CodeSearch this siteSkip to main contentSkip to navigationComputer Vision, Deep Learning, Artificial superintelligence (ASI)HomeProductCoursesMachine Learning SpecializationMachine Learning Foundations: A Case Study ApproachFSDLFull Stack Deep LearningMLOpsROSParallel Programming Modern CPPCloud-NativeIoT Scholarship FoundationTensorFlow: Data and Deployment Specialization Workshops and EventsRISC-VEdge-AI-summitEmbedded IoTTeslaAI-HardwareOpenVINO Deep LearningMetaverseWorkshopsIFA2022Book Summary commonplace bookknowledgemanagementPKMTopics and ProjectsAIHubChatGPTHow to startYouTubeYouTube IISoftwareRoadmap for Image Processing Source CodeOpenCVCPPpythonMacOSOpenCVRustcompileIoTShareVideo TrackingCameraCalibrationDRLHardwareQuantum ComputingAltCoinResumeCVApplestartupLinksamazonAboutFQApromptComputer Vision, Deep Learning, Artificial superintelligence (ASI)Source CodeEssential Python Tips And Tricks For advance computer vision ProgrammersEssential Tips And Tricks For compiling code computer vision projects cvtest: Computer Vision TestUnit Test, Integration Test, System Test, Acceptance Test for Computer Vision and Deep LearningDo you want to test your output of computer vision application which is video or images?Standard test for computer vision applicationThere isn’t any standard test for computer vision program. I wrote many test by myself and I would like to share some of them here. For example, I write a program to test docker and check the processing time, memory usage, CPU usage, etc. In computer vision application sometime you need to check the output which is the image. How do you want to check it. I write some program to check the output which is the image and compare the ground truth. I check some well known methods such as PSNR, SSIM, Image quality, distortion, brightness, sharpness, etc. Furthermore, I check much different hardware and write some test for computer vision application base on different hardware architecture and Evaluation hardware.Do you want to know your program Automatically adjusting brightness of image in the right way?, How do you know using generic sharpening kernel to remove blurriness is working?, How to do check FPS process?, Which OCR system work better for your input image? check S3 bucket in AWS for image and video files and versioning Check Docker load balancer, memory usage, …GPUIn general I would create a wrapperadapter that only exposes the needed functionality of such an external dependency. Apart from being able to easer adapt to changes of the external dependency, we can also mock the adapter in our tests and let it do things we could not do so easily with the dependency itself. For our example, we could it have return a predefined image in our test and it is also easier to test if our code behaves properly in presence of failures (that are usually hard to trigger with the real thing.How to convert programming languages: MatlabPythonCC and modern C 231. trace the images: what happens to each image and memory and copy and transit to that image 1. Using a table and each column transitions for each image 2. always check the call by reference or call by value 3. check the naming similarity 2. compare the output of each step: store data, matrix, array, and vector to the text file and compare compliantly also compare the differences because in image processing some times not exactly output same and different version of OpenCV also has different result 1. the version of the library 2. the function use which library 3. the inputoutput of the function and call by reference or call by value 3. The same function in Matlab, Python, C, and OpenCV is different even change OS also make difference in output 4. test … test … line by lineGoogle SitesReport abusePage detailsPage updated Google SitesReport abuseThis site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By clicking “accept”, you agree to its use of cookies. Cookie PolicyRejectAccept

Computer Vision, Deep Learning, Artificial superintelligence (ASI) - ProductSearch this siteSkip to main contentSkip to navigationComputer Vision, Deep Learning, Artificial superintelligence (ASI)HomeProductCoursesMachine Learning SpecializationMachine Learning Foundations: A Case Study ApproachFSDLFull Stack Deep LearningMLOpsROSParallel Programming Modern CPPCloud-NativeIoT Scholarship FoundationTensorFlow: Data and Deployment Specialization Workshops and EventsRISC-VEdge-AI-summitEmbedded IoTTeslaAI-HardwareOpenVINO Deep LearningMetaverseWorkshopsIFA2022Book Summary commonplace bookknowledgemanagementPKMTopics and ProjectsAIHubChatGPTHow to startYouTubeYouTube IISoftwareRoadmap for Image Processing Source CodeOpenCVCPPpythonMacOSOpenCVRustcompileIoTShareVideo TrackingCameraCalibrationDRLHardwareQuantum ComputingAltCoinResumeCVApplestartupLinksamazonAboutFQApromptComputer Vision, Deep Learning, Artificial superintelligence (ASI)ProductAccording to Gartner: “Democratize Data Science & Machine Learning (DSML) by applying multipersona DSML platforms, bringing its value to an ever-larger audience of less technical experts. In addition, by expanding the use of a platform from a single business department across multiple internal or external business functions, insights and experiences can be shared more effectively, enabling decision optimization.” Have you ever thought about the power of automation? In everyday life, we often no longer notice the automated helpers. What if the benefits also impacted your working life? With our tools & solutions, we can accelerate standard tasks to a very large extent or even completely automate them. Valuable time left for you and other experts to complete further activities. These are the top challenges that were solving for other companies utilizing visual inspection and defect detection: The Accuracy of Traditional Machine Vision (causing false positivesexcess scrap)Align teams on a defect definitions and how to label Monitoring of edge devices, fleet management, change detection, etc.Identify defects on complex parts with rule-based solutions Challenges to classify defect types due to ambiguities with rule-based solutionswe utilizes deep learning to improve accuracy and defect detection across a wide range of applications in industrial automation and manufacturing.We developing a solution that auto-diagnoses issues in Computer Vision models and datasets, enabling ML teams to deliver AI models that perform well in the real world. We currently help ML teams. We are Web & Mobile App Development company specializing in Staff Augmentation (React JSReact Native, Native iOSAndroid, Python, Bootstrap), Digital Marketing and building custom digital solutions. some of the areas we excel in are : AI, Machine Learning, IoT connected devices ARVRXR, Metaverse, Advanced UXUI NFT, Blockchain (Advance payments B2B-B2C) EnterpriseConsumer apps, Fleet tracking, SaasWe have developed an ML Ops platform that helps healthcare companies to push ML innovation faster and more successful. A key aspect is collaboration.The benefits in a nutshell: Leverage your existing ML development capacity by up to 250 via a multipersona development approach. Remove any domain knowledge gap of your data scientists to improve quality and remove up to 75 risk of project failure - btw. nearly 35 of ML PoCs fail because of domain knowledge gaps. Get the best time-to-value with an integrated knowledgebase of hundreds of medical datasets and imaging AIML recipes, ready to use and all open source. Our ML Ops platform is all secure, works on your premise and with high governance capabilities.Google SitesReport abusePage detailsPage updated Google SitesReport abuseThis site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies.Learn moreGot it

Computer Vision, Deep Learning, Artificial superintelligence (ASI) - AltCoinSearch this siteSkip to main contentSkip to navigationComputer Vision, Deep Learning, Artificial superintelligence (ASI)HomeProductCoursesMachine Learning SpecializationMachine Learning Foundations: A Case Study ApproachFSDLFull Stack Deep LearningMLOpsROSParallel Programming Modern CPPCloud-NativeIoT Scholarship FoundationTensorFlow: Data and Deployment Specialization Workshops and EventsRISC-VEdge-AI-summitEmbedded IoTTeslaAI-HardwareOpenVINO Deep LearningMetaverseWorkshopsIFA2022Book Summary commonplace bookknowledgemanagementPKMTopics and ProjectsAIHubChatGPTHow to startYouTubeYouTube IISoftwareRoadmap for Image Processing Source CodeOpenCVCPPpythonMacOSOpenCVRustcompileIoTShareVideo TrackingCameraCalibrationDRLHardwareQuantum ComputingAltCoinResumeCVApplestartupLinksamazonAboutFQApromptComputer Vision, Deep Learning, Artificial superintelligence (ASI)AltCoinDisclaimer Risk warning:I am not a financial advisor and anything you read, see or hear in this site, podcast, video should not by any means be construed as financial advice it is purely intended for your entertainment and demonstration and illustrative purposes only.This is not financial advice and should not be taken as financial advice. the views I have in everyone of my sitepostbloglinkstextdocumentspowerpointvideos are completely speculative opinions and do not guarantee any specific result. The NFT, AltCoin, Metaverse, … is extremely volatile and has high risk. You should never act on anyone’s advice or opinions, without first doing your own research, realising your own risk, and making your own decision. I recommend speaking with a licensed and qualified professional before making any financial decision. BasicLinksHands onsetup:macOS1: install nvm: Node Version Manager2. install hardhat: Ethereum development environment for professionals3.METAVERSEcrypto 18.12.21 - 22.April.2022NFTStock Purchasing managers’ indexes (PMI): A PMI index over 50 represents growth or expansion within the manufacturing sector of the economy compared with the prior month. United States Philadelphia Fed Manufacturing IndexA value greater than 0 reflects growth in the manufacturing sector, whereas a value less than 0 reflects a contraction.My token Tiziran (TIZ) on SolanaToken nameTiziran (TIZ)Token addressFsniTTtb9GeGq1DHkipxera4bsgFkb19maBLKZwMe7inToken Tiziran token Tiziran (TIZ) Token Contract Address0xe30407DB873302D6AEaAB3bA619f44Bc9F924594Token Decimal:18Network:BNB Smart Chain Mainnetonly 100 token available to sell how to modify the code best tools to analysis market PersianInterface: minimum data and functions required to make it a standard ERCEIP smart contractvalue the shows it is a parametersConstant is a variable that can’t be changedMapping() function maps elements from a key to a valueConstructor() function that automatically runs when a new data item is created( initialization code)Emit() function triggers an event (message to be sent out) New project .geth –syncmode “light” MkdirTruffle initTruffle deploy –resetTruffle consoleHelloWorld.deployed().then(function(instance) return instance ); HelloWorld.deployed().then(function(instance) return instance.getHelloMessage() ); npm install trufflehdwallet-provider LinkedInStart 10.April.2022 install truffle -gERC-20 (500K)ERC721: Non-Fungible Tokens (NFT): 70KERC1155: Multi-Token Tokens : 8KdAppSecurity: IIHyperledger.org solidity: docker run ethereumsolc:stable –helpbrew updatebrew upgradebrew tap ethereumethereumbrew install solidity 4. Visual studio code: Name: solidityId: JuanBlanco.solidityDescription: Ethereum Solidity Language for Visual Studio CodeVersion: 0.0.139Publisher: Juan BlancoVS Marketplace Link: Online editor: rulesvalidate transactions properly computer nodes 51 consensus mechanismsadvantage proof of work:anybody can attached machines and gain rewards blockchain trilemma: 1- scalablespeed 2- decentralization securefastest: solana (arweave), AltCoinBasicproof of workstakeLinks (solidity) Hands on setup:macOS1: install nvm: Node Version Managercurl -o- bashnvm install 12nvm use 12nvm alias default 12npm install npm –global Upgrade npm to the latest version2. install hardhat: Ethereum development environment for professionalsnpm install –save-dev hardhat3. METAVERSECoin Bureau: : TOP 5 Virtual Land NFTs!! BEST Metaverse Plays?? sandboxmetaverse group buy decentraland: 2.43 M : cryptopunks, the sandbox, decentraland, cryptovoxels, somnium space, superworld, arcona. OVR Top 5 Landaxie infinitysavannah, forest, arctic, mystic, genesis, lunas landingdecentraland 9K land4500 MANA 3 13500Decentraland Tutorials: my land Tiziran: the sandbox2.5 Eth 3850 9625bitcountrycreate and personalise metaverse aavegotchi is virtual world in ethereum 2011 - 2018 - open metaverse - the sandbox alpha - 29.11 to 20.12.21 require the sandbox (SAND) 5 (17.12.21) 2.73 (22.April.2022) 0.58 (14.11.22)learn: learn: AI meta: Metaverse, Mesh, Open AI and more from Microsoft Ignite Fall 2021: crypto 18.12.21 - 22.April.2022Decentraland (MANA): 3 - 2.03 (22.April.2022)sandbox (SAND): 5 - 2.73 (22.April.2022)Axie Infinity (AXS): 95 - 45.65 (22.April.2022)illuvium (ILV) : 1136 - 514.98 (22.April.2022)star atlas price (ATLAS) base on solana : 0.10 - 0.023 (22.April.2022)wilder world (wild): 3.67 - 1.15 (22.April.2022)bitCoin : revolution to currency, DeFi, ethereumNFTMetaverse NFT Links: Google SitesReport abusePage detailsPage updated Google SitesReport abuseThis site uses cookies from Google to deliver its services and to analyze traffic. 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Computer Vision, Deep Learning, Artificial superintelligence (ASI) - TensorFlow: Data and Deployment Specialization Search this siteSkip to main contentSkip to navigationComputer Vision, Deep Learning, Artificial superintelligence (ASI)HomeProductCoursesMachine Learning SpecializationMachine Learning Foundations: A Case Study ApproachFSDLFull Stack Deep LearningMLOpsROSParallel Programming Modern CPPCloud-NativeIoT Scholarship FoundationTensorFlow: Data and Deployment Specialization Workshops and EventsRISC-VEdge-AI-summitEmbedded IoTTeslaAI-HardwareOpenVINO Deep LearningMetaverseWorkshopsIFA2022Book Summary commonplace bookknowledgemanagementPKMTopics and ProjectsAIHubChatGPTHow to startYouTubeYouTube IISoftwareRoadmap for Image Processing Source CodeOpenCVCPPpythonMacOSOpenCVRustcompileIoTShareVideo TrackingCameraCalibrationDRLHardwareQuantum ComputingAltCoinResumeCVApplestartupLinksamazonAboutFQApromptComputer Vision, Deep Learning, Artificial superintelligence (ASI)TensorFlow: Data and Deployment Specialization 1. Browser-based Models with TensorFlow.js2. Device-based Models with TensorFlow Lite3. Data Pipelines with TensorFlow Data Services 4. Advanced Deployment Scenarios with TensorFlow 6 hours to completeDevice-based models with TensorFlow Lite1 hour to completeRunning a TF model in an Android App2 hours to completeBuilding the TensorFLow model on IOS2 hours to completeTensorFlow Lite on devicesbasic understanding of Kotlin andor Swift, as well as Android Studio andor Xcode, will help you follow along. W1: lightweightlow-latencyprivacyimproved power consumptionefficient model ready to used QuantizationAll available CPU platforms are supportedReducing latency and inference costLow memory footprintAllow execution on hardware restricted-to or optimize for fixed-point operationsOptimized models for special purpose HW accelerator TUPWeight pruningModel topology transformsTensor decompositionDistillation Chrome as our internet browser, Brackets as our HTML editor and the Web Server for Chrome App as our web server. TF.js: training and inference on browserChrome (cat and dog)mobileNet classification androidMobileNet ssd up to 10 objects from 80 classes CNNjavascriptTf-visTf.tidy() - save memoryW3: Converting Models to JavaScript Install Wget on MacLinux1. Install Homebrew by running the following command in your terminal: usrbinruby -e “(curl -fsSL Install wget byrunning the following command in your terminal: brew install wgetInstall Wget on WindowsGo to the wget.exe file from the links provided. You can download the latest version of wget for either 32-bit or 64-bit systems.If prompted, click Run or Save.If you chose Save, double-click the downloaded file to start installing.W4:Transfer learning mobileNetGoogle SitesReport abusePage detailsPage updated Google SitesReport abuseThis site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies.Learn moreGot it

Computer Vision, Deep Learning, Artificial superintelligence (ASI) - HardwareSearch this siteSkip to main contentSkip to navigationComputer Vision, Deep Learning, Artificial superintelligence (ASI)HomeProductCoursesMachine Learning SpecializationMachine Learning Foundations: A Case Study ApproachFSDLFull Stack Deep LearningMLOpsROSParallel Programming Modern CPPCloud-NativeIoT Scholarship FoundationTensorFlow: Data and Deployment Specialization Workshops and EventsRISC-VEdge-AI-summitEmbedded IoTTeslaAI-HardwareOpenVINO Deep LearningMetaverseWorkshopsIFA2022Book Summary commonplace bookknowledgemanagementPKMTopics and ProjectsAIHubChatGPTHow to startYouTubeYouTube IISoftwareRoadmap for Image Processing Source CodeOpenCVCPPpythonMacOSOpenCVRustcompileIoTShareVideo TrackingCameraCalibrationDRLHardwareQuantum ComputingAltCoinResumeCVApplestartupLinksamazonAboutFQApromptComputer Vision, Deep Learning, Artificial superintelligence (ASI)HardwareJetson Nano OpenCV AI KIT OAK-D-LITE depth cameraHardware for Deep Learning (machine learning)My experienceRaspberry Pi 4Smart AI IoT, Robotic, 3D SLAM, AR, VRRISC-VI worked with many different hardware such asCameraWhat is important?Scaled-YOLOv4:scaling model based on hardwareCostHow to use computer vision with deep learning in IoT devices. Inference machine learning on Edge require some extra steps.Use special frameworks or library for edge devices:In some case you need to enhance model for inference. There are many techniques to use such as,HowJetson Nano OpenCV AI KIT OAK-D-LITE depth cameraCameraCamera Specs: Color camera, Stereo pairIMX214 (PY014 AF, PY114 FF), OV7251 (PY013)DFOV HFOV VFOV 81 69 54 , 86 73 58Resolution: 13MP (4208x3120), 480P (640x480)Focus: AF: 8cm - OR FF: 50cm - , Fixed-Focus 6.5cm - Max Framerate: 35 FPS, 120 FPSWidth: 91 mm , Height: 28 mm, Length: 17.5 mm, Baseline: 75 mm, Weight: 61 gchips:Robotics Vision Core 2 (RVC2 in short)Myriad X are integrated into the Robotics Vision Core 2Speed MLModel name, Size, FPS, Latency ms, MobileOne S0 224x224, 165.5, 11.1YoloV8n, 416x416, 31.3, 56.9, YoloV8n, 640x640, 14.3, 123.6YoloV8s, 416x416, 15.2, 111.9YoloV8m, 416x416, 6.0, 273.8Hardware for Deep Learning (machine learning) I experiment with many different hardware to train and run deep learning application. The below list shows my suggestion, comparison, expectation of using different hardware. Embedded AI, implementing distributed data parallel, distributed model parallel solutions. Laptop: NVIDIA Geforce RTX 3080 TiRazer Blade 17 - 17.3 inch gaming laptop (NVIDIA Geforce RTX 3080 Ti, Intel i9-12900H, 4K UHD 144Hz display 32GB DDR5 RAM, 1TB SSD,DesktopeGPURazer RC21-01310100-R351 Core X External Graphics Card Case 300 Euro GPUCooler Master MasterCase EG200 External GPU Enclosure - Thunderbolt 3 Compatible eGPU Enclosure, 1 PWM 92mm Fan, V550 SFX Gold Fully Modular PSU, USB Hub, Vertical Laptop Support - EU Plug 300 Euro GPUGPUGeforce RTX 3090 24G 384Bit Gddr6x Nvidia GeforceMSI GeForce RTX 3090 GAMING TRIO 24G Gaming Graphics Card - NVIDIA RTX 3090, GPU 1740MHz, 24GB GDDR6X memory 2800 Euro IoT: Raspberry pi 3 (you need accelerator )Raspberry pi 4 (you need accelerator )Intel Neural Compute Stick 2Intel Distribution of OpenVINO ToolkitI attached to Raspberry pi 4 by USB 3 and work very well for many deep learning modelsGoogle CoralI attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow modelsWhy TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to usedNVIDIA Jetson Nano ( 2GB and 4GB ram)I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutesNVIDIA JETSON AGX XAVIER NVIDIA AGX Orin 1900 EuroCompare NVIDIA Jetson AGX Orin with AGX Xavier: 8x AI performance, in-advance Ampere GPU, CPU, Memory & StorageOpenCV AI KitOAK 100 EuroOAKD 200 EuroOAKD Wifi 250 EuroOpenCV AI Kit: OAKD-PoE 250 EuroOAKD lite 100 EuroMy experience I tested many different hardware for different computer vision applications in area of IoT and RoboticsAI Edge: How to inference deep learning models on edgeIoT ; Enabling efficient high-performance ; AcceleratorsOptimization on Deep Learning Raspberry Pi 4 How to upgrade Raspberry Pi 4 EEPROM boot recovery; Released 2020-09-14; to install and boot from USB 3 (SSD)update Raspberry Pi 4 EEPROM boot recoveryinstall Ubuntu 20 on SSD change the config.txt and add “programusbbootmode1” at the end of file remove and micro sd card and boot from ssdSmart AI IoT, Robotic, 3D SLAM, AR, VR3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardwareRISC-VI worked with many different hardware such asRaspberry pi 3Raspberry pi 4Intel Neural Compute Stick 2Intel Distribution of OpenVINO ToolkitI attached to Raspberry pi 4 by USB 3 and work very well for many deep learning modelsGoogle CoralI attached to Raspberry pi 4 by USB 3 and work very well for TensorFlow modelsWhy TensorFlow lite on Edge: Lightweight, low-latency, Privacy, improved power consumption, efficient model ready to usedNVIDIA Jetson Nano I test Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) under heavy workloads can perform up to 30 minutesNVIDIA JETSON AGX XAVIERThe best hardware I attended in may conferences and summits in area of Hardware for deep learning such as: AI Hardware Europe Summit (July 2020) The Edge AI & Brain Inspired Computing (November 2020) Apache TVM And Deep Learning Compilation Conference (December 2020) RISC-V Summit (December 2020) OpenCV AI KitCameraI worked with many different cameras such as:Camera Module V1Camera Module V2Camera Module V2.1multispectral cameraUSB webcam IP camera high resolution camera 8Kdepth camera stereo cameraWhat is important?camera calibration is important Quantum efficiency (spectral response)Sensor size inches or mm and pixel size micro meterDynamic Range dBImage noise and signal to noise ratio (SNR), PSNR, SSIM, : greater SNR yields better contrast and clarity, as well as improved low light performanceinter face, cable length in m, bandwidth max in MBs , multi camera, cable costs, real time, plug and playfirewire, 4.5 , 64, , , , gige, 100, 100, , , , usb, 8, 350, , , , link, 10, 850, -, -, , -usb-c, 10, 40 GB,,,,distortions, scaling factors, quality is important, calculate minimum sensor resolution , determine your sensor size, focal length, sensor resolution image resolution 2 ( field of view (FOV) smallest feature )some online tools: baslerweb.com, edmundoptics.com, flir.comto sum upuse USB-C camera. it will help you in the future upgrades in hardware and easy to use with less issues find your best trade-off between WD and FOVsometimes you cannot have everything in life!your lens aperture (f is your friend, use it!a larger DOF requires a larger f performance curves are the ultimate documentation to read when selecting a lensunderstanding them properly requires good knowledge in optics, but it totally worth it.Scaled-YOLOv4:scaling model based on hardware CostHow much does a patent cost? Mobile, Open Hardware, RISC-V System-on-Chip (SoC) Development Kit Hardware NVIDIA Jetson Xavier NX Developer Kit WIFISparkFun GPS-RTK Dead Reckoning pHATMicro Sd cardMophie Powerstation USB C 20000ZED 2 Stereo Camera3D-printed boxAWSAWS S3 AWS xml.p2.xlarge EC2 instances AWS SagemakerHackboard 2 with Ubuntu Linux (99) Intel CPU3D printed humanoid robot: NimbRo-OP2 and NimbRo-OP2X hardwarePost Product to customer by easyshipfulfillmentcrowdChinaDivisionORQA FPVfloshipUpdate 26.April.2021How to use computer vision with deep learning in IoT devices. Inference machine learning on Edge require some extra steps.I tested several hardware such as Raspberry pi 3, Raspberry pi 4, Intel Neural Compute Stick 2, OpenCV AI Kit, Google Coral, NVIDIA Jetson Nano, etc. Different OS: real-time operating system (RTOS), Nasa cFS (core Flight System), Real-Time Executive for Multiprocessor Systems (RTEMS),anomaly detection, object detection, object tracking, …Use special frameworks or library for edge devices:NVIDIA TensorRT TensorFlow Lite: TensorFlow Lite on Microcontroller Gesture Recognition OpenMVTensorflow studio.edgeimpulse.comTensorFlow.js PyTorch LightningPyTorch Mobile Intel Distribution of OpenVINO Toolkit CoreMLML kitFRITZ MediaPipe Apache TVMTinyML: enabling ultra-low power machine learning at the edge tiny machine learning with Arduino Libraries: ffmpeg, GStreamer, celery,GPU library for python: PyCUDA, NumbaPro, PyOpenCL, CuPy Moreover, think about deep learning model for your specific hardware at first stage.In some case you need to enhance model for inference. There are many techniques to use such as,PruningQuantizationDistillation Techniques Binarized Neural Networks (BNNs) Apache TVM (incubating) is a compiler stack for deep learning systems Distributed machine learning and load balancing strategyLow rank matrix factorization (LRMF) Compact convolutional filters (VideoCNN) Knowledge distillation Neural Networks Compression Framework (NNCF) Parallel programmingHowDistributed machine learning and load balancing strategyPruningmodel pruning: reducing redundant parameters which are not sensitive to the performance. aim: remove all connections with absolute weights below a threshold. go for bigger size of network with many layers then pruning much better and faster QuantizationThe best way is using Google library which support most comprehensive methods compresses by reducing the number of bits used to represent the weights quantization effectively constraints the number of different weights we can use inside our kernels per channel quantization for weights, which improves performance by model compression and latency reduction. training a compact neural network with distilled knowledge of a large model distillation (knowledge transfer) from an ensemble of big networks into a much smaller network which learns directly from the cumbersome model’s outputs, that is lighter to deploy Distillation Techniques Distill-Net: Application-Specific Distillation of Deep Convolutional Neural Networks for Resource-Constrained IoT Platforms Binarized Neural Networks (BNNs) It is not support by GPU hardware such as Jetson Nano. mostly based on CPU Apache TVM (incubating) is a compiler stack for deep learning systems challenges with large scale models deep neural networks are: expensive computationally expensive memory intensive hindering their deployment in:devices with low memory resources applications with strict latency requirements other issues:data security: tend to memorize everything including PII bias e.g. profanity: trained on large scale public datas elf discovering: instead of manually configuring conversational flows, automatically discover them from your data self training: let your system train itself with new example s self managing: let your system optimize by itself knowledge distillation Distributed machine learning and load balancing strategyrun models which use all processing power like CPU,GPU,DSP,AI chip together to enhance inference performance. dynamic pruning of kernels which aims to the parsimonious inference by learning to exploit and dynamically remove the redundant capacity of a CNN architecture. partitioning techniques through convolution layer fusion to dynamically select the optimal partition according to the availability of computational resources and network conditions. Low rank matrix factorization (LRMF) there exists latent structures in the data, by uncovering which we can obtain a compressed representation of the dataLRMF factorizes the original matrix into lower rank matrices while preserving latent structures and addressing the issue of sparseness Compact convolutional filters (VideoCNN) designing special structural convolutional filters to save parameters replace over parametric filters with compact filters to achieve overall speedup while maintaining comparable accuracy Knowledge distillation Neural Networks Compression Framework (NNCF) AI Edge: How to inference deep learning models on edgeIoT Enabling efficient high-performance AcceleratorsOptimization on Deep Learning if the object is large and we do not need small anchor in mobileNet we can remove small part of network which related to small objects. in YOLO reduce number of anchor. decrease size of image input but reduce the accuracyParallel programming and clean code, design pattern,Google SitesReport abusePage detailsPage updated Google SitesReport abuseThis site uses cookies from Google to deliver its services and to analyze traffic. 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Computer Vision, Deep Learning, Artificial superintelligence (ASI) - amazonSearch this siteSkip to main contentSkip to navigationComputer Vision, Deep Learning, Artificial superintelligence (ASI)HomeProductCoursesMachine Learning SpecializationMachine Learning Foundations: A Case Study ApproachFSDLFull Stack Deep LearningMLOpsROSParallel Programming Modern CPPCloud-NativeIoT Scholarship FoundationTensorFlow: Data and Deployment Specialization Workshops and EventsRISC-VEdge-AI-summitEmbedded IoTTeslaAI-HardwareOpenVINO Deep LearningMetaverseWorkshopsIFA2022Book Summary commonplace bookknowledgemanagementPKMTopics and ProjectsAIHubChatGPTHow to startYouTubeYouTube IISoftwareRoadmap for Image Processing Source CodeOpenCVCPPpythonMacOSOpenCVRustcompileIoTShareVideo TrackingCameraCalibrationDRLHardwareQuantum ComputingAltCoinResumeCVApplestartupLinksamazonAboutFQApromptComputer Vision, Deep Learning, Artificial superintelligence (ASI)amazonBest Amazon items Google SitesReport abusePage detailsPage updated Google SitesReport abuseThis site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies.Learn moreGot it

Computer Vision, Deep Learning, Artificial superintelligence (ASI) - ShareSearch this siteSkip to main contentSkip to navigationComputer Vision, Deep Learning, Artificial superintelligence (ASI)HomeProductCoursesMachine Learning SpecializationMachine Learning Foundations: A Case Study ApproachFSDLFull Stack Deep LearningMLOpsROSParallel Programming Modern CPPCloud-NativeIoT Scholarship FoundationTensorFlow: Data and Deployment Specialization Workshops and EventsRISC-VEdge-AI-summitEmbedded IoTTeslaAI-HardwareOpenVINO Deep LearningMetaverseWorkshopsIFA2022Book Summary commonplace bookknowledgemanagementPKMTopics and ProjectsAIHubChatGPTHow to startYouTubeYouTube IISoftwareRoadmap for Image Processing Source CodeOpenCVCPPpythonMacOSOpenCVRustcompileIoTShareVideo TrackingCameraCalibrationDRLHardwareQuantum ComputingAltCoinResumeCVApplestartupLinksamazonAboutFQApromptComputer Vision, Deep Learning, Artificial superintelligence (ASI)ShareI would like to give you some of my experience with AI projects. image processing tips:Preparation ML Project WorkflowBefore Training deep learning modelTraining deep learning modelContinuous deliveryAfter Training deep learning modelDeep learning model in productionTechnologyMy Keynote (February 2021)Advanced and practicalFaceI am thrilled to announce the launch of my new service! As a computer vision and machine learning consultant, I provide end-to-end research and development solutions for cutting-edge artificial intelligence projects. My services encompass custom software implementation, MLOps, and project management, ensuring clients receive top-quality results. If you’re looking to enhance your AI capabilities, I’m here to help. Contact me to learn more.Are you looking for expert analysis of your project, eager for professional feedback, or in need of a comprehensive execution plan? Would you like to gain insights from industry leaders? I offer a 15-minute consultation free of charge to help you achieve your goals.improved performance, reduced costs, or increased customer satisfaction. Are you in search of a partner who aligns with your project requirements? As a freelance data analyst with over 10 years of experience in the industry, I understand that finding the right partner can be challenging.To help businesses overcome this hurdle, I offer best-in-class analysis tools such as regression analysis, reliability tools, hypothesis tests, and a graph builder feature for scientific data visualization. Additionally, I use predictive modeling to forecast future market conditions, making it easier for businesses to plan and strategize.I understand that budget limitations can exist, and gaining buy-in for new tools and systems can be burdensome. That’s why my services are designed to be user-friendly, with no coding required and built-in content-sensitive help systems. My tools and systems are also designed for non-statisticians, making it easier for businesses to understand and implement them.With many industry use cases available online, my services are proven to deliver results. Let’s partner up to take your project to the next level!pip install mlc-ai-nightly -f 1:Introduction to Unity: TVMScriptIntroduction to Unity: Relax and PyTorchTVM BYOC in PracticeGet Started with TVM on Adreno GPUIntroduction to Unity: MetascheduleHow to Bring microTVM to a custom IDEDay 2:Community KeynotePyTorch 2.0: the journey to bringing compiler technologies to the core of PyTorchSupport QNN Dialect for TVM with MediaTek Neuron and Devise the Scheduler for AccelerationOn-Device Training Under 256KB MemoryAMD TutorialTVM at TI: Accelerating inference using the C7xMMAAdreno GPU: 4x speed-up and upstreaming to TVM mainlineTransfer-Tuning: Reusing Auto-Schedules for Efficient Tensor Program Code GenerationImprovement in the TVM OpenCL codegen to autogenerate optimal convolution kernels for Adreno GPUsTVM Unity: Pass Infrastructure and BYOCRenesas Hardware accelerators with Apache TVMIntroduction on 4th Gen Intel Xeon processor and BF16 support with TVMHidet: Task Mapping Programming Paradigm for Deep Learning Tensor ProgramsTowards Building a Responsible Data EconomyOptimizing SYCL Device Kernels with AKGAdreno GPU Performance Enhancements using TVMImprovements to CMSIS-NN integration in TVMUMA: Universal Modular Accelerator InterfaceDay 3:TVM Unity for Dynamic ModelsEmpower Tensorflow serving with backend TVMEnabling Conditional Computing on Hexagon targetDecoupled Model Schedule for Large Deep Learning Model TrainingUsing TVM to bring Bayesian neural networks to embedded hardwareEfficient Support of TVM Scan OP on RISC-V Vector ExtensionImprovements to Ethos-U55 support in TVM including CI on Alif Semiconductor boardsCompiling Dynamic ShapesTVM Packaging in 2023: delivering TVM to end usersCross-Platform Training Using Automatic Differentiation on Relax IRAutoTVM: Reducing tuning space by cross axis filteringSparseTIR: Composable Abstractions for Sparse Compilation in Deep LearningAnalytical Tensorization and Fusion for Compute-intensive OperatorsCUTLASS 3.0: Next Generation Composable and Reusable GPU Linear Algebra LibraryEnabling Data Movement and Computation Pipelining in Deep Learning CompilerAutomating DL Compiler Bug Finding with NNSmithTVM at NIOTVM at TencentIntegrating the Andes RISC-V Processors into TVMAlpa: A Compiler for Distributed Deep LearningACRoBat: Compiler and Runtime Techniques for Efficient Auto-Batching of Dynamic Deep Learning ComputationsChannel Folding: a Transform Pass for Optimizing MobilenetsDay 1: Introduction to Unity: TVMScript NN show us some hidden patter in history we can not see before. I always have a slip of paper at hand, on which I note down the ideas of certain pages. On the backside I write down the bibliographic details. After finishing the book I go through my notes and think how these notes might be relevant for already written notes in the slip-box. It means that I always read with an eye towards possible connections in the slip-box. (Luhmann et al., 1987, 150)Deep representation learning Model evaluation.Camera cheaper lidarPoint cloud because of we need 3dCapturing reality1. Standard way: git add . git commit -m “Message”Another way: git commit -a -m “Message”. With aliases, you can write your own Git commands that do anything you want.Eg: git config –global alias.ac ‘!git add -A && git commit -m’ (alias called ac, git add -A && git commit -m will do the full add and commit). The revert command simply allows us to undo any commit on the current branch.Eg: git revert 486bdb2Another way: git revert HEAD (for recent commits). This command lets you easily see the recent commits, pulls, resets, pushes, etc on your local machine.Eg: git reflog. Gives you the ability to print out a pretty log of your commitsbranches.Eg: git log –graph –decorate –oneline. One can also use the log command to search for specific changes in the code.Eg: git log -S “A promise in JavaScript is very similar”. This command will stash (store them locally) all your code changes but does not actually commit them.Eg: git stash. This command will delete all the tracking information for branches that are on your local machine that are not in the remote repository, but it does not delete your local branches.Eg: git remote update –prune. For finding which commits caused certain bugsEg: git bisect start git bisect bad git bisect good 48c86d6. One can wipe out all changes on your local branch to exactly what is in the remote branch.Eg: git reset –hard originmainDont trust your devices IoT. software and hardware are together for better business. Newsletter investing every 3 months1. Prototyping. New bie2. Patent. Website. ( list of investors) 3. Pre seed. First founding 1M VC, inistution, anjel capital. 400 000 preseed. Quveribel. Equtible rund convertible non agreement Template. Convertabel lone 1. Germ standar inistitude 2. 4. Equity. Venture builder. 20 200 0005. 100 000 per year to become unocorn in less than 10 years6. Soniy corn 100k unicorn 1M7. 360 euro per years for database of investor8. Convertable loan: Pay interst rate 5 to 8 18 months later (2M found in 10M) convert on based . 9. Invester Never act as co-founder full time 2010. Project profit, 11. Full time after foun risingMake a plan for your business; take your time to make calculations by creating a target audience. Your target audience determines how you approach your business plan. By studying your target audience, you are making empirical research and collecting information from them Then, secure a good partnership if need be, and get enough capital to start up. What the people need Why people need it When the people need it It’s affordability It’s ease of use It’s maintenance and revenuePair programming The SB7 Framework harnesses the influence of stories. The structure describes the 7 most common story elements: Character Problem Guide Plan Calls to action Failure SuccessDear Hiring Managers Name,I am writing to apply for the position of computer vision for IoT and cloud at Company Name. I am a highly skilled and experienced computer vision engineer with a strong background in IoT and cloud technologies. I believe that my skills and experience make me an ideal candidate for this position and I am excited about the opportunity to contribute to the success of your organization.I have a solid understanding of computer vision algorithms and techniques, as well as experience in developing and implementing computer vision systems. I am proficient in programming languages such as Python, C, and Java, and have experience with popular computer vision libraries such as OpenCV, TensorFlow, and PyTorch.In addition, I have a strong background in IoT and cloud technologies, including experience with IoT platforms such as AWS IoT, Azure IoT, and Google Cloud IoT. I am familiar with cloud computing technologies such as AWS, Azure, and Google Cloud, and have experience with deploying and managing computer vision systems on these platforms.I am also a team player and have excellent communication skills. I am able to work with cross-functional teams and can effectively communicate with both technical and non-technical stakeholders. I am also highly motivated, and I am always looking for ways to improve my skills and stay up-to-date with the latest technologies.I am excited about the opportunity to join Company Name and to contribute to the development of cutting-edge computer vision systems for IoT and cloud. I am confident that my skills and experience make me a strong candidate for this position, and I look forward to discussing how I can contribute to your organization.Thank you for considering my application. I look forward to hearing from you soon.Sincerely,Title: “Unlocking the Power of Computer Vision for IoT and Cloud”Introduction: Hi, and welcome to our video on the topic of computer vision for IoT and cloud. In this video, we’re going to explore how computer vision technology can be used to enhance IoT and cloud-based systems, and how it can be used to unlock new possibilities for businesses and consumers alike.Body: First, let’s talk about what computer vision is and how it works. Essentially, computer vision is the technology that enables computers to understand and interpret visual information from the world around us. This can include things like images, videos, and even 3D models. One of the key ways that computer vision can be used to enhance IoT and cloud-based systems is by enabling devices to better understand and interact with their environment. For example, a computer vision-enabled camera could be used to monitor a manufacturing facility and identify when a machine is in need of maintenance or when an employee is working in an unsafe manner. Another way that computer vision can be used to enhance IoT and cloud-based systems is by enabling devices to better understand and interact with people. For example, a computer vision-enabled security camera could be used to identify individuals and track their movements, or a computer vision-enabled smart home system could be used to detect when someone is in the room and adjust the lighting or temperature accordingly. Additionally, computer vision can also be used to enhance cloud-based systems by providing more accurate data and insights. For example, a computer vision-enabled drone could be used to collect data on crops and provide farmers with more accurate information about the health and growth of their crops.Conclusion: Overall, computer vision technology has the potential to unlock new possibilities for businesses and consumers alike, by enabling IoT and cloud-based systems to better understand and interact with their environment and people. We hope this video has provided you with a better understanding of the potential of computer vision for IoT and cloud, and we look forward to seeing the new possibilities that will be created as this technology continues to evolve.Excited to share my latest project using computer vision and IoT to improve efficiency in manufacturing. I used a combination of machine learning algorithms and cloud computing to analyze data from cameras and sensors in real-time, resulting in a 20 increase in production speed. This was a challenging project but I enjoyed every step of it!I am always looking for new opportunities to apply my skills in computer vision and IoT to help companies improve their operations. Let’s connect if you are working on a similar project or if you are looking for a developer with these skills. this post, you briefly mention your experience and skills in computer vision and IoT, and you provide a specific example of a project you worked on that demonstrates your abilities. You also make it clear that you are open to new opportunities, and you invite others to connect with you. Using relevant hashtags such as can help your post reach a wider audienceExciting news! I just published a paper on a new object detection algorithm that I developed. The algorithm uses a combination of deep learning and computer vision techniques to improve accuracy and speed of object detection in real-world scenarios. This is a big step forward in the field of computer vision and I am proud to have contributed to it.I will be presenting my research at the Computer Vision Conference next month, if you’re attending be sure to stop by and say hi! this post, you briefly explain the main findings and contributions of your research, and you express your excitement and pride in your work. You also mention the upcoming conference where you will be presenting your research, inviting your friends and colleagues to meet you in person. Also using relevant hashtags such as can help reach a wider audience interested in the field.Features stores 1. Car parts detection2. Resize keep aspects ration3. 3.1 Perform damage detection4. 3.2Semantic segregation 5. Transfer to original coordinates 1 class imbalance 2 class definition Maybe Class in between 3 inconstant annotations Color augmentation 1. RGB shift2. Random brithness and contrast3. Sharpen4. Hue saturation valueWhy manually data augmented Becasu control of data. Not too rotate or change something Photogrammetry model Neural radiance fields (NeRF) NeRF in the wild GitHub - google-researchtuningplaybook: A playbook for systematically maximizing the performance of deep learning models.Yocto and Machine Learning OpenCV: Bard Google: Book: Project Management for Non-Project Managers Accelerate deep learning model development with cloud custom environments - AWS Online Tech Talks - YouTube Refactoring ( )Performance Notes Of PyTorch Support for M1 and M2 GPUs - Lightning AIInvestopedia AcademyHandBrake updated with AV1 and VP9 10-bit video encodingHow to Start Your Sole Proprietorship in 6 Simple StepsDuolingo English Test - YouTubePyTorch for Deep Learning & Machine Learning Full Course - YouTubeWhy passive investing makes less sense in the current environment Financial TimesGitHub - google-researchtuningplaybook: A playbook for systematically maximizing the performance of deep learning models.GitHub - mgechevgoogle-interview-preparation-problems: leetcode problems I solved to prepare for my Google interview.Bayesian Neural Networks and Variational DropoutOne machine learning question every day - bnomialGit remote add orgine  Asynchronous  Operation  Anomaly detection Use experience. Personalizes.  Prediction manage society mobility  Personalization  Covenant Platform.   OpenMMLab Wordtune - AI-powered Writing Companiontree -v  -I ‘.png’ -I ‘.jpg’ –charset utf-8 list2.txt 3D object using triangular mesh need vertices point cloud underlying surface of some 3D object, faster Definition of Done User Story complete CodeImplementation complete CodeImplementation Peer Reviews) approved Unit tests complete (if required) Testing Notes complete (if required) User Story Acceptance criteria defined and verified Backend: Python, Redis, Postgres, Celery Frontend: React, Redux, TypeScript DevOps: Terraform, Kubernetes, GitHub, Docker, AWS Data: Python (Data Science), Kafka, Fastapi, MLFlow, AWS SageMaker ML: Selcond core, Kubeflow, Sharpness ,Noise, Dynamic range, Tone reproduction , Contrast, Color, Distortion , DSLR lenses, Vignetting, Exposure, Lateral chromatic aberration (LCA), Lens flare, Color, Artifacts . . . . . . .  . () . ( ) . . namely motion estimation, motion smoothing, and image warping. Motion estimation algorithms often use a similarity transform to handle camera translations, rotations, and zooming. The tricky part is getting these algorithms to lock onto the background motion, 0. video frames captured during fast motion are often blurry. Their appearance can be improved either using deblurring techniques (Section 10.3) or stealing sharper pixels from other frames with less motion or better focus (Matsushita, Ofek, Ge et al. 2006). Exercise 8.3 has you implement and test some of these ideas. 1. Background subtraction 2. Motion estimation 3. Motion smoothing 4. Image warping. image warping can result in missing borders around the image, which must be cropped, filled using information from other frames, or hallucinated using inpainting techniques (Section 10.5.1). Vision stabilization  There is much recent work on  Multi-view 3D reconstruction is a central research topic in computer vision that is driven in many different directions There are many available methods that can handle the noisy image completion problem In the case of surveillance using a fixed camera, there is no desired motion. In the case of most robotic applications, horizontal and vertical motions are desired, but rotation is not. In some cases of ground vehicles where the terrain is known to have many incline changes, or with aerial vehicles undergoing complicated maneuvers where the vehicles body is meant to be in varying orientations, rotation might be desired as the robot is meant to be at an angle at times.  In robotics applications, computational complexity is extremely important due to the need for real-time operation. Also, it is likely that the center of rotation will not lie in the center of the image frame because the camera is rarely mounted at the robots center of mass.  This first assumption is made in many video stabilization algorithms, and is a convenient way to seed the correct features with higher trust values. It is not an unreasonable assumption to make. Depending on the application, there is often a large portion of frames where local motion does not occur. In some situations, such as monitoring of steady traffic, there is no guarantee that local motion will not occur. This situation has not been tested, nor has our algorithm been designed to handle it. The second assumption comes from a combination of common sense, and the experience of many computer vision researchers. It makes sense that an object in the scene which does not move will be recognized more easily and more often. Being recognized consistently and consecutively is considered stable. On the other hand, objects which have local motion are less likely to be recognized as often. They might move through shadows, change orientation, or even move completely out of the scene. These possibilities all lead to a less stable class of features. It is likely that, more often than not, there are more background features than foreground features. Moving objects generally cover a small portion of the screen, which usually yields fewer features. Although uncommon, we did not want to make the assumption that this would occur in every frame. Certain scenes will consist of a large portion of local motion, or an object will move very close to the camera, consuming a much larger portion of the scene than the background. As long as some background features are discovered in each frame, our stabilization algorithm should succeed.  image processing tips:the image size and kernel size need to depended. the best way is to use the one variable to define the size of the image and kernel together.the coordinate of the image start at top left of the imagedisplayin order to change it to the normal coordinate you can usegrid of points; two matrix to X , Y coordinatesubtract half of W, H from X, Y in order to have normal coordinate system for our imagenow we have cartesian coordinate cartesian coordinate to polar coordinate . in MATLAB we can use “:”for example MatrixA(:) which means all entity of the matrix no mater how many dimensions we have but if we want to implemented in Python we can use numpy.flatten(). in the MATLAB the round is different from python. if you want same result you need implement the rand function by yourself.imgemasknp.oneslike(imagesource)255imgemaskimgemask.astype(np.uint8)imgemaskimgemask.flatten() ??? .ravel().asarraynp.logicaland( 1, 2)indexesindex for index in range(len(array1)) if array1index Truecv2.bitwisenot(yyy)”olive” editor remove silence Questions:How to train model to add new classes?How to add a new class to an existing classifier in deep learning?Adding new Class to One Shot Learning trained modelIs it possible to train a neural network as new classes are given?Merging all several models that detection system for all these tasks.Answer 1:There are several ways to add new classes to the trained model, which require just training for the new classes.Incremental training (GitHub)continuously learn a stream of data (GitHub)online machine learning (GitHub)Transfer Learning TwiceContinual learning approaches (Regularization, Expansion, Rehearsal) (GitHub)Answer 2:Online learning is a term used to refer to a model which takes a continual or sequential stream of input data while training, in contrast to offline learning (also called batch learning), where the model is pre-trained on a static predefined dataset.Continual learning (also called incremental, continuous, lifelong learning) refers to a branch of ML working in an online learning context where models are designed to learn new tasks while maintaining performance on historic tasks. It can be applied to multiple problem paradigms (including Class-incremental learning, where each new task presents new class labels for an ever expanding super-classification problem).Do I need to train my whole model again on all four classes or is there any way I can just train my model on new class?Naively re-training the model on the updated dataset is indeed a solution. Continual learning seeks to address contexts where access to historic data (i.e. the original 3 classes) is not possible, or when retraining on an increasingly large dataset is impractical (for efficiency, space, privacy etc concerns). Multiple such models using different underlying architectures have been proposed, but almost all examples exclusively deal with image classification problems.Answer 3:You could use transfer learning (i.e. use a pre-trained model, then change its last layer to accommodate the new classes, and re-train this slightly modified model, maybe with a lower learning rate) to achieve that, but transfer learning does not necessarily attempt to retain any of the previously acquired information (especially if you don’t use very small learning rates, you keep on training and you do not freeze the weights of the convolutional layers), but only to speed up training or when your new dataset is not big enough, by starting from a model that has already learned general features that are supposedly similar to the features needed for your specific task. There is also the related domain adaptation problem.There are more suitable approaches to perform incremental class learning (which is what you are asking for!), which directly address the catastrophic forgetting problem. For instance, you can take a look at this paper Class-incremental Learning via Deep Model Consolidation, which proposes the Deep Model Consolidation (DMC) approach. There are other continualincremental learning approaches, many of them are described here or in more detail here.Answer 4:by using Continual learning approaches to trained without losing the original classes. It has 3 categories:RegularizationExpansionRehearsalAnswer 5:if you access to the dataset then you can download it and add all you new classes when you have “ ‘N’ COCO Classes ‘M’ New classes “ after that you can fine tune model based on new dataset. you do not need all of the dataset just same number of image for all class enough. Before start your machine learning project ask these questions and preparation: What is your inference hardware? specify the use case. specify model interface. how would we monitor performance after deployment? how can we approximate post-deployment monitoring before deployment? build a model and iteratively improve it. How to deploy the model at the end? monitor performance after deployment. what is your metric? How do you split your data (training and validation)? Preparation ML Project WorkflowWhat is your hardware ?specify the use casespecify model interfacehow would we monitor performance after deployment?how can we approximate post-deployment monitoring before deployment?build a model and iteratively improve itdeploy the modelmonitor performancewhat is your are metric?How do you split your data? Before Training deep learning modelusing large model to train because it is faster to train with lower overfit and faster converge due to best training it is easier and higher compress in the final stage model compression and acceleration: reducing parameters without significantly decreasing the model performanceData: How to have good data for training deep learning models; How to Build and Enhance A Good Data Set For Your Deep Learning Project: using same config and data for training and inference, removing redundant (delete data which you don’t need), get more data, Handle missing data, using data augmentation techniques or GAN to generate more data, re-scalebalance data, Transform your data (Change data types), Feature selection based on data-set and use case The data you don’t need: removing redundant samplesget more dataInvent more datadata augmentation Re-scale databalance datasets Transform your dataFeature selection based on dataset and use case ML-Augmented Video Object Tracking: By applying and evaluating multiple algorithmic models, enhanced ability to scale object tracking in high-density video compositions.Training deep learning modelautomated hyper-parameters Using Hyperparameter tuning Hyperparameter optimization toolsAutoML genetic algorithmpopulation based trainingbayesian optimizationYou need to set some parameters and config for training DiagnosticsWeight InitializationLearning rateActivation functionNetwork TopologyBatches and EpochsRegularizationOptimization and LossEarly StoppingContinuous deliveryevolve with latest detection modelsmore data (no labels)semi-supervised learning: big self-supervised models are strong semi-supervised learnersAfter Training deep learning modelParameter pruningmodel pruning: reducing redundant parameters which are not sensitive to the performance.aim: remove all connections with absolute weights below a thresholdQuantizationcompresses by reducing the number of bits used to represent the weightsquantization effectively constraints the number of different weights we can use inside our kernelsper-channel quantization for weights, which improves performance by model compression and latency reduction.Low rank matrix factorization (LRMF)there exists latent structures in the data, by uncovering which we can obtain a compressed representation of the dataLRMF factorizes the original matrix into lower rank matrices while preserving latent structures and addressing the issue of sparsenessCompact convolutional filters (VideoCNN)designing special structural convolutional filters to save parametersreplace over parametric filters with compact filters to achieve overall speedup while maintaining comparable accuracyKnowledge distillationtraining a compact neural network with distilled knowledge of a large modeldistillation (knowledge transfer) from an ensemble of big networks into a much smaller network which learns directly from the cumbersome model’s outputs, that is lighter to deployBinarized Neural Networks (BNNs)Apache TVM (incubating) is a compiler stack for deep learning systemsNeural Networks Compression Framework (NNCF)Deep learning model in productionsecurity: controls access to model(s) through secure packaging and execution Testauto training using parallel processing and library such as GStreamerTechnologyDockerAWSFlaskDjangoMy Keynote (February 2021)introduction Machine Learning Deep LearningMachine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed supervised Machine LearningDeep Convolutional Neural Networks (DCNN) ArchitectureVisualizing and Understanding Convolutional NetworksObject Detection by Deep Learning Video TrackingStyle Transfer semi-supervised Machine Learning Deep Reinforcement learning (DRL)Google Deep Reinforcement learning (DRL)unsupervised Machine LearningAuto Encoder Generative Adversarial Networks (GANs)Tools Pre trained model Effect of Augmented Datasets to Train DCNNsTraining for more classesOptimization Hardware Production setuppost development business , Gartner, Hype Cycle for emerging technologies, 2025Advanced and practical Inside CNNDeep Convolutional Neural Networks ArchitectureConvolutionConvolution LayerConvFC FiltersActivation FunctionsLayer ActivationsPooling LayerDropout ; L2 pooling WhyMax-pooling is usefulHow to see inside each layer and find important features Visualizing and Understanding Convolutional Networks Hands on python for deep learning Fundamental deep learning Installation: TensorFlow, PyTorchUsing PCeGPU for training video trackingSummary of the summitAI Hardware Europe Summit (July 2020) The Edge AI & Brain Inspired Computing (November 2020) Apache TVM And Deep Learning Compilation Conference (December 2020) RISC-V Summit (December 2020) for raspFace Effective and precise face detection based on color and depth data containing or not containing a faceEigenface, Fisherface, waveletface, PCA (Principal Component Analysis), LDA (Linear Dis-criminant Analysis), Haar wavelet transform, and so on. ViolaJones detectorillumination changes and occlusion depthinformation is used to filter the regions of the image where a candidate face regionis found by the ViolaJones (VJ) detector- the first filtering rule is defined on the color of the region; since some false positiveshave colors not compatible with the face (e.g. shadows on jeans) a skin detector isapplied to remove the candidate face regions that do not contain skin pixels;- the second filtering rule is defined on the size of the face: using the depth mapit is quite easy to calculate the size of the candidate face region, which is use-ful to discard smallest and largest faces from the final result set;- the third filtering rule is defined on the depth map to discard flat objects (e.g.candidate faces found in a wall) or uneven objects (e.g. candidate face foundin the leaves of a tree). Combining color and depth data the candidate faceregion can be extracted from the background and measures of depth and reg-ularity are used for filtering out false positives.The size criteria simply remove the candidate faces not included in a fixed rangesize (12.5,30 cm). The size of a candidate face region is extracted from the depthmap according to the following approach.image belowGaussian mixture 3D morphable face model Face Synthesis for Eyeglass-Robust Face Recognition GeneGAN: Learning Object Transfiguration and Attribute Subspace from Unpaired Data Making a Case for Landmark-Free Face Alignment to Regress 3D Face Shape and Expression from an Image without 3D Supervision Eyeglasses Removal in the Wild far are we from solving the 2D & 3D Face Alignment problem? (and a dataset of 230,000 3D facial landmarks) we construct, for the first time, a very strong baseline by combining a state-of-the-art architecture for landmark localization with a state-of-the-art residual block, train it on a very large yet synthetically expanded 2D facial landmark dataset and fi- nally evaluate it on all other 2D facial landmark datasets. (b) We create a guided by 2D landmarks network which con- verts 2D landmark annotations to 3D and unifies all exist- ing datasets, leading to the creation of LS3D-W, the largest and most challenging 3D facial landmark dataset to date (230,000 images). (c) Following that, we train a neural network for 3D face alignment and evaluate it on the newly introduced LS3D-W. (d) We further look into the effect of all traditional factors affecting face alignment performance like large pose, initialization and resolution, and introduce a new one, namely the size of the network. (e) We show that both 2D and 3D face alignment networks achieve per- formance of remarkable accuracy which is probably close to saturating the datasets used. Training and testing code as well as the dataset can be downloaded from https: www.adrianbulat.comface-alignment 19.Sep.2021Medium English (learn write English) storyGreek Mythology Explained: A Deeper Look at Classical Greek Lore and MythPapers:CALTag: High Precision Fiducial Markers for CameraDiatom Autofocusing in Brightfield Microscopy: a Comparative Study :implementation variation of the laplacianAnalysis of focus measure operators in shape-from-focus: why laplacian? Blure detection? Iqaf?Optical flow modeling and computation: A surveyToward general type 2 fuzzy logic systems based on zSlices——————————————————————–Lost in spaceThe OA Film: Serial billonsmonk serial moviesPython asyncHighly decoupled microserviceEdex RIS-V , Self-carRISC-V MagazineRoad mapGame: overunder in IoTThe EU General Data Protection Regulation (GDPR) and Face Images in IoTThe GDPR (General Data Protection Regulation), taking effect in May 2018, introduces strict requirements for personal data protection and the privacy rights of individuals. The EU regulations will set a new global standard for privacy rights and change the way organizations worldwide store and process personal data. The GDPR brings the importance of preserving the privacy of personal information to the forefront, yet the importance of face images within this context is often overlooked. The purpose of this paper is to introduce a solution that helps companies protect face images in IoT devices which record or process image by camera, to strengthen compliance with the GDPR. Our Face is our Identity Our face is the most fundamental and highly visible element of our identity. People recognize us when they see our face or a photo of our face. Recent years have seen exponential increase in the use, storage and dissemination of face images in both private and public sectors - in social networks, corporate databases, IoT, smart-city deployments, digital media, government applications, and nearly every organizations databases. ———————(aws-okta env stage)aws s3 cp s3:datasetarchive.tar.gz Usersa.zipaws s3 ls images tail -n 100aws s3 cp staging-imagestest.jpg Userstest.jpg———————screen -rDk get podsDockerRUN chmod x tmprun.shCan run docker in terminal and run code line by linedocker run -it –rm debian:stable-slim bashapt-get updateapt-get installl -y——————————–brew install awscli aws-okta kubectx kubernetes-cli tfenvtouch .awsconfig——————————————————————–docker image rm TETSTDFSAFDSADFdocker image lsdocker system prunedocker run -p 5000:5000 nameDocker:latestdocker build . -t nameDocker:latestdocker container stop number-docker-namedocker container lsdocker pull quay.iotest:v0.0.1docker run –rm -p 5000:5000 -it quay.iotest:v0.0.1curl –header “Content-Type: applicationjson” –request POST –data ‘“fixed”:7.4, “a”:0, “b”:0.56, “c”:9.4’ run –rm -v home.awscredentials:root.awscredentials -it quay.iotest binsh aws s3 ls –profiletest——————————–Cloud software engineer and consultant focusing on building highly available, scalable and fully automated infrastructure environments on top of Amazon Web Services and Microsoft Azure clouds. My goal is always to make my customers happy in the cloud.—————-Search google for 3d tiger - iPhone show ARVR—————brew install youtube-dl—————————-List: Collection bucket : 1 for week 2 for month 3 for future——————————————————————– Per frame operation Detection Classification Segmentation Feature extraction Recognition Across frames Tracking Counting High level Intention Relations Analyzing Deep compressionPruning deep learningHash table neural networkDl compressionDeep compression Mini PCI-e slot What have I learned so far:Problem-based learningreal life scenariosindex card (answer , idea)Think-Pair-ShareLeverage flip chartsSummarizing——————————————————————– SelfAdvancing Self-Supervised and Semi-Supervised Learning with SimCLR citeChen2020 pretraining on a large unlabeled dataset and then fine-tuning on a smaller labeled datasetpretraining on large unlabeled image datasets, as demonstrated by Exemplar-CNN, Instance Discrimination, CPC, AMDIM, CMC, MoCo and others. A Simple Framework for Contrastive Learning of Visual Representations, 85.8 top-5 accuracy using 1 of labeled images on the ImageNet datasetcontrastive learning algorithmslinear evaluation protocol (Zhang et al., 2016; Oord et al.,2018; Bachman et al., 2019; Kolesnikov et al., 2019)unsupervised learning benefits more from bigger models than its supervised counterpart. —————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————-Some of optimization algorithms Swarm Algorithm1. Ant Colony Optimization (ACO) was inspired by the research on the behavior of ant colonies 2. Firefly Algorithm based on insects called fireflies 3. Marriage in Honey Bees Optimization Algorithm (MBO algorithm) is inspired by the process of reproduction of Honey Bee4. Artificial Bee Colony Algorithm (ABC) is based on the recollection of the Honey Bees5. Wasp Swarm Algorithm was inspired on the Parasitic wasps6. Bee Collecting Pollen Algorithm (BCPA) 7. Termite Algorithm8. Mosquito swarms Algorithm (MSA)9. zooplankton swarms Algorithm (ZSA)10. Bumblebees Swarms Algorithm (BSA)11. Fish Swarm Algorithm (FSA)12. Bacteria Foraging Algorithm (BFA)13. Particle Swarm Optimization (PSO)14. Cuckoo Search15. Bat Algorithm (BA)16. Accelerated PSO17. Bee System18. Beehive Algorithm19. Cat Swarm20. Consultant-guided search21. Eagle Strategy22. Fast Backterial swarming algorithm23. Good lattice swarm optimization24. Glowworm swarm optimization25. Hierarchical swarm model26. Krill Herd27. Monkey Search28. Virtual ant algorithm29. Virtual bees30. Weighted Swarm Algorithm31. Wisdom of Artificial Crowd algorithm 32. Prey-predator algorithm 33. Memetic algorithm 34. Lion Optimization Algorithm 35. Chicken Swarm Optimization 36. Ant Lion Optimizer 37. Compact Particle Swarm Optimization38. Fruit Fly Optimization Algorithm 39. marine propeller optimization algorithm 40. The Whale Optimization Algorithm 41. virus colony search algorithm 42. Slime mould optimization algorithm Ecology Inspired Algorithm1. Biogeography-based Optimization2. Invasive Weed Optimization3. Symbiosis-Inspired Optimization - PS2O4. Atmosphere Clouds Model5. Brain Storm Optimization6. Dolphin echolocation7. Japanese Tree Frog Calling algorithm8. Eco-inspired evolutionary algorithm9. Egyptian Vulture10. Fish School search11. Flower Pollination algorithm12. Gene Expression13. Great Salmon Run14. Group Search Optimizer15. Human Inspired Algorithm16. Roach Infestation algorithm17. Queen-bee algorithm18. Shuffled frog leaping algorithm19. Forest Optimization Algorithm 20. coral reefs optimization algorithm 21. cultural evolution algorithm 22. Grey Wolf Optimizer 23. probabilistic pso 24. omicron aco algorithm 25. shark smell optimization 26. social spider algorithm 27. sosial insects behavior algorithm 28. sperm whale algorithm Evolutionary Optimization1. Genetic Algorithm2. Genetic Programming3. Evolutionary Strategies4. Differential Evolution5. Paddy Field Algorithm6. Queen-bee Evolution7. Quantum Inspired Social Evolution Physic and Chemistry inspired algorithm1. Big bang-Big Crunch2. Block hole algorithm3. Central force optimization4. Charged System search5. Electro-magnetism optimization6. Galaxy based search algorithm7. Gravitational search8. Harmony search algorithm9. Intelligent water drop algorithm10. River formation algorithm11. Self-propelled dynamics12. Simulated Annealing13. Stachastic diffusion search14. Spiral optimization15. Water Cycle algorithm16. Artificial Physics optimization17. Binary Gravitational search algorithm18. Continous quantum ant colony optimization19. Extended artificial physics optimization20. Extended Central force optimization21. Electromagnetism-like heuristic22. Gravitational Interaction optimization23. Hysteristetic Optimization algorithm24. Hybrid quantum-inspired GA25. Immune gravitational inspired algorithm26. Improved quantum evolutinary algorithm27. Linear programming28. Quantum-inspired bacterial swarming29. Quantum-inspired evolutionary algorithm30. Quantum-inspired genetic algorithm31. Quantum-behaved PSO32. Unified big bang-chaotic big crunch33. Vector model of artificial physics34. Versatile quantum-inspired evolutionary algorithm35. Space Gravitational Algorithm 36. Ion Motion Algorithm 37. Light Ray Optimization Algorithm 38. Ray Optimization 39. Photosynthetic Algorithms40. floorplanning algorithm 41. Gases Brownian Motion Optimization 42. gradient-type optimization 43. mean-variance optimization44. Mine blast algorithm 45. moth flame optimization 46. multi battalion search algorithm 47. music inspired optimization48. no free lunch theorems algorithm49. Optics inspired optimization 50. runner-root algorithm 51. sine cosine algorithm52. pitch tracking algorithm 53. Stochastic Fractal Search algorithm 54. stroke volume optimization 55. Stud krill herd algorithm 56. The Great Deluge Algorithm 57. Water Evaporation Optimization 58. water wave optimization algorithm 59. Island model algorithm 60. Steady State modelGoogle SitesReport abusePage detailsPage updated Google SitesReport abuseThis site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies.Learn moreGot it

Computer Vision, Deep Learning, Artificial superintelligence (ASI) - YouTubeSearch this siteSkip to main contentSkip to navigationComputer Vision, Deep Learning, Artificial superintelligence (ASI)HomeProductCoursesMachine Learning SpecializationMachine Learning Foundations: A Case Study ApproachFSDLFull Stack Deep LearningMLOpsROSParallel Programming Modern CPPCloud-NativeIoT Scholarship FoundationTensorFlow: Data and Deployment Specialization Workshops and EventsRISC-VEdge-AI-summitEmbedded IoTTeslaAI-HardwareOpenVINO Deep LearningMetaverseWorkshopsIFA2022Book Summary commonplace bookknowledgemanagementPKMTopics and ProjectsAIHubChatGPTHow to startYouTubeYouTube IISoftwareRoadmap for Image Processing Source CodeOpenCVCPPpythonMacOSOpenCVRustcompileIoTShareVideo TrackingCameraCalibrationDRLHardwareQuantum ComputingAltCoinResumeCVApplestartupLinksamazonAboutFQApromptComputer Vision, Deep Learning, Artificial superintelligence (ASI)YouTube ILearn Image processing A to Z MCMOMCT Computer Vision: OpenCV 4.xDeep Learning: TensorFlow, PyTorch, CaffeOptimization: OpenVINO, OpenCL, CUDALanguage: C, PythonHardware: NVIDIA, Jetson, Google Coral, Intel stick YouTubeVideo Object Tracking, Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT)Multi-Object Tracking (MOT), video tracking, counting objects, deep learning models for SOT Video Object Tracking, Multi-Class Multi-Object Multi-Camera Tracking (MCMOMCT) 2020 - Computer Vision, Thresholding, overview, machine vision, segmentation, jupyter lab, python 2020 , machine vision, deep learning, cnn, drl, Tools, data augmentation, optimization, build dl 2020 - Pattern Recognition, style transfer, Deep Reinforcement Learning, unsupervised learning, GANs 2020 - Basic, Deep Learning, supervised, deep reinforcement learning, unsupervised, GANs, DCNN, 2020 - machine vision, camera calibration, stereo vision, video stabilization, video analytic 2020 - Computer Vision Color Thresholding Transformation Histogram Image Pyramid Motion Estimation 2020 - Deep Learning in Computer Vision overview survey complete guide tutorial categories 2020 - OneNote 2020 Management Plan Organisation Classification Nots and Documents Top Tips Guide 2020 - OneNote as team management and daily planner to optimise your day, tackle goals and roadmap OneNote 2020, Tips Guide Tag Shortcuts How to use Online Free Managing Notes and documents Top Best Thresholding image segmentation opencv python Binarization separate objects from the background Top features, extensions and plugins for visual studio code and terminal in MacOS for developer Computer Vision Using Fast API, Docker, Postman, Docker-compose cv-ml-pipline based on Seldon Core, Docker, Image Processing by python and OpenCV developer setup for MacOS (iTerm, Visual Code, Brew, python) Computer Vision and Deep Learning, AWS Advanced tools for python developer, Computer Vision, Deep Learning, AWS, MacOS, Terminal, Command Advanced Setup for Terminal based for Developer ubuntu optimization packages for deep learning and OpenCV - 1 hardware for Deep Learning Raspberry pi 4, Intel Neural Stick 2, Google Coral, Nvidia Jetson Nano Using ffmpeg library to send and receive videocamera streaming Install and Test GStreamer for Windows, Mac, Linux, Raspberry Pi Compile Optimize OpenCV for best performance in computer vision and deep learning for IoT tutorial how to apply neural style transfer to images using OpenCV 4, C, and deep learning (torch) Deep Learning by OpenCV 4 (2018) - part 1 - compile OpenCV for Deep Learning 0- Using Deep Learning for Computer Vision Applications, TensorFlow, Caffe, OpenCV 4 4-opencv4 Using TensorFlow model in OpenCV 4 3 - OpenCV 4 , Deep Learning for Computer Vision SitesReport abusePage detailsPage updated Google SitesReport abuseThis site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies.Learn moreGot it

Computer Vision, Deep Learning, Artificial superintelligence (ASI) - LinksSearch this siteSkip to main contentSkip to navigationComputer Vision, Deep Learning, Artificial superintelligence (ASI)HomeProductCoursesMachine Learning SpecializationMachine Learning Foundations: A Case Study ApproachFSDLFull Stack Deep LearningMLOpsROSParallel Programming Modern CPPCloud-NativeIoT Scholarship FoundationTensorFlow: Data and Deployment Specialization Workshops and EventsRISC-VEdge-AI-summitEmbedded IoTTeslaAI-HardwareOpenVINO Deep LearningMetaverseWorkshopsIFA2022Book Summary commonplace bookknowledgemanagementPKMTopics and ProjectsAIHubChatGPTHow to startYouTubeYouTube IISoftwareRoadmap for Image Processing Source CodeOpenCVCPPpythonMacOSOpenCVRustcompileIoTShareVideo TrackingCameraCalibrationDRLHardwareQuantum ComputingAltCoinResumeCVApplestartupLinksamazonAboutFQApromptComputer Vision, Deep Learning, Artificial superintelligence (ASI)LinksTiziran, Personal, Roadmap, Books, Online Courses LinksLinks from my research and interestAI in RISC-VMixed realityOld LinksReferenceLink GroupsTop websitePythonComputer VisionDocumentsBusinessToolsPatentsBooksJournal PapersConference PapersArticles on LinkedInSlides slideshareOnline CoursesLearning pathCompletedReferenceLink of best GroupsBooksVideosPapersSiteEfficient Video Dataset Loading and Augmentation in PyTorch for deep learning training loops.GitHub from my research and interest Build Better Generative Adversarial Networks (GANs) - Andrew Ng - 2021 AI in RISC-V RISC-V hardware & software ecosystem highlights in 2020Mixed reality Kimera is a C library for real-time metric-semantic simultaneous localization and mapping, which uses camera images and inertial data to build a semantically annotated 3D mesh of the environment. Kimera is modular, ROS-enabled, and runs on a CPU.Old LinksReference Link GroupsLinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch Source code Facebook : Deep Learning, Computer Vision, Robotics The link to one-note notebook GitHubTop website hbrew install youtube-dl Deep Learning for coders (2020)Deep Learning for Video (Master in Computer Vision Barcelona)Augmented Reality using ArUco Markers in OpenCV (C Python) to Write Beautiful Python Code With PEP 8PEP 622 – Structural Pattern MatchingMonitor Your Dependencies! Stop Being A Blind Data-Scientist.Computer VisionHybrid CVDL pipelines with OpenCV 4.4 G-APIPaddleDetection is an end-to-end object detection development kit based on PaddlePaddle, which aims to help developers in the whole development of training models, optimizing performance and inference speed, and deploying models. Documents is a tool for running local Kubernetes clusters using Docker container “nodes”.FINN is an experimental framework from Xilinx Research Labs to explore deep neural network inference on FPGAs. Gitter is a chat and networking platform that helps to manage, grow and connect communities through messaging, content and discovery.Fair and useful benchmarks for measuring training and inference performance of ML hardware, software, and services. source, highly available Prometheus setup with long term storage capabilities.Deep Learning Design Patterns - Jr Data Scientist - Part 3 - Alternative Connectivity PatternsBusiness5 Ways to Make More Money as a Coach or Consultant convert video for mac opensource Mapping platform built for enterprises MindMapCreate and share beautiful images of your source code.Web IDE Patents System and method for providing advertisement contents based on facial analysis (WO2020141969A2)A method for augmenting a plurality of face images (WO2021060971A1)A method for detecting a moving vehicle (WO2021107761A1)System and method for providing advertisement contents based on facial analysis (WO2020141969A2)The present invention relates to a system (100) for providing advertisement contents based on facial analysis comprising an image acquisition device (10) to acquire an image of a user, a face detection module (20) to detect face of the user in the image, an analysis module (40) to analyse the facial features statistically using classification models retrieved from a classification module (30), a database (60) to store matching rules, weighted advertisements and a plurality of advertisement contents; and a display device (80) to display the advertisement contents. The system (100) further comprises a computation module (50) to compute weighted image of the user and a matching module (70) to match the weighted image of the user with the weighted advertisement to select an advertisement content based on facial analysis of the user. A method of providing the advertisement contents based on facial analysis is also provided thereof. A method for augmenting a plurality of face images (WO2021060971A1)The present invention relates to a method for increasing data for face analysis in video surveillance. The method comprises the steps of acquiring at least one face image from an image acquisition module (102), acquiring a plurality of face images available on the internet using a data input module (104), increasing face images by at least one data augmentation module (106 and 107), generating a plurality of face images based on a trained Generative Adversarial Network, GAN technique by using a GAN module, selecting proper images based on quality of the face images using a fuzzy logic module (111), saving the selected images into a fifth database, and training the deep learning module (113). A method for detecting a moving vehicle (WO2021107761A1)The present invention relates to a method for detecting a moving vehicle. The method comprises the steps of grabbing an initial image from a video stream by a vehicle detection module (1100), wherein the vehicle detection module (1100) is a part of a system (1000) to identify moving vehicle, enhancing the illumination of the initial image by the vehicle detection module (1100), enhancing the edges within the initial image by the vehicle detection module (1100), and finding vehicle based on homogenous properties of the body of the vehicle by the vehicle detection module (1100). The step of finding vehicle based on homogenous property of the body of the vehicle by the vehicle detection module (1100) further comprising the sub-steps of closing open edges, inverting the binary image, segmenting an inverted binary image, filtering the noise based on geometric feature, and filtering the noise based on relation. Booksbook chapter titled Camera Calibration and Video Stabilization Framework for Robot Localization in the Book entitled Control Engineering in Robotics and Industrial Automation” which will be published (24072021) in Springer. book chapter titled Augmented Optical Flow Methods for Video Stabilization”, In Computational Intelligence: from theory to application. (2017) (p18). Journal PapersUsing An Ant Colony Optimization Algorithm For Image Edge Detection As A Threshold Segmentation For OCR System Journal of Theoretical & Applied Information Technology, 95(21) GSFT-PSNR: Global Single Fuzzy Threshold Based on PSNR for OCR Systems, International Journal of Computer Science and Network Solutions 4(6) Adaptive Image Thresholding based On the Peak Signal-To-Noise Ratio, Research Journal of Applied Sciences, Engineering and Technology 8(9). Simultaneous Localization and Mapping Trends and Humanoid Robot Linkages, Asia-Pacific Journal of Information Technology and Multimedia (APJITM) Peak Signal-To-Noise Ratio Based On Threshold Method for Image Segmentation, Journal of Theoretical & Applied Information Technology, 57(2) Character recognition based on global feature extraction; Journal of Theoretical and Applied Information Technology 52 Conference PapersPattern Image Significance for Camera Calibration, IEEE Student Conference on Research and Development (SCOReD 2017) Augmented optical flow methods for video stabilization. 4th Artificial Intelligence Technology Postgraduate Seminar (CAITPS 2015) Auto-Calibration for Multi-Modal Robot Vision based on Image Quality Assessment, The 10th Asian Control Conference (ASCC 2015) 2D versus 3D Map for Environment Movement Objects, Second National Doctoral Seminar in Artificial Intelligence Technology (CAIT 2012) Comparison Single Thresholding Method for Image Segmentation on Handwritten Images, International Conference on Pattern Analysis and Intelligent Robotics License Plate Recognition with Multi-Threshold Based on Entropy, 3rd International Conference on Electrical Engineering and Informatics (ICEEI 2011) Character recognition based on global feature extraction, 3rd International Conference On Electrical Engineering and Informatics (ICEEI 2011) Tafresh Grid: Grid computing in Tafresh university,” 2011 IEEE 3rd International Conference on Communication Software and Networks, Xi’an, 2011, pp. 83-85. image segmentation based on Peak Signal to Noise Ratio for a license plate Recognition system, International Conference on Computer Applications and Industrial Electronics (ICCAIE 2010) Multi-threshold approach for license plate recognition system, International Conference on Signal and Image Processing WASET ICSIP 2010:1046-1050 An evaluation of classification techniques using enhanced Geometrical Topological Feature Analysis, 2nd Malaysian Joint Conference on Artificial Intelligence (MJCAI 2010) Articles on LinkedInADRL: Advanced Deep Reinforcement Learning Patents and papers; Main point summary on recently published research on DRL (draft version Feb 2019) ( most important research papers for deep learning (updated December, 2017)( )List of useful links (Videos, Slides, Articles) for Deep Learning ( driving vehicles (Computer Vision, Deep Learning)( Thresholding( Slides slideshareDeep Learning for Computer Vision in Ubuntu 19; Part 1 installation Link( Deep Learning for Computer Vision Applications Link( ) tensorflow1.3 digits6.0 Caffe Link( ) Install TensorFlow 1.2 on macOS Sierra on MacBook (June 2017) Link( ) Best Deep Learning Post from LinkedIn Group Link( ) Install, Compile, Setup, Setting OpenCV 3.2, Visual C 2015, Win 64bit Link( ) Layers in Deep Learning&Caffe layers (model architecture ) Link( ) How to install Digits 5.1 on Ubuntu 14 Link( ) Deep Learning for Video Analysis part 1 (DeepStream SDK) NVIDIA TensorRT NVIDIA GPU Inference Engine (GIE) Link( ) Computer Vision, Deep Learning, OpenCV Link( )Online Courses Writing with Impact Balancing Work and Life Finance Foundations: Income Taxes Starting a Business with Family and Friends Finance Essentials for Small Business Setting Up Your Small Business as a Legal Entity Creating a Business Plan Understanding Business Entrepreneurship: Finding and Testing Your Business Idea Guy Kawasaki on Entrepreneurship Financial Modeling Foundations Financial Accounting Foundations Strategic Planning Foundations 5 Personal Finance Tips Investment Evaluation Managing Your Personal Investments Financial Wellness: Managing Personal Cash Flow Financial Wellness for Couples and Families Managing Your Personal Finances Finance Foundations Entrepreneurship Foundations Introduction to Deep Learning with OpenCV OpenCV for Python Developers Brad Feld on Validating Your Startup Idea Brad Feld on Raising Capital Take a More Creative Approach to Problem-Solving Professional Networking Building Relationships While Working from Home Entrepreneurship: Raising Startup Capital Business Analysis Foundations: Business Process Modeling Communicating with Confidence Machine Learning for iOS Developers Learning path Stay Ahead in Personal Finance Become a Small Business Owner Completed Reference Link of best Groups LinkedIn: Computer Vision, Deep Learning, Deep Reinforcement Learning, GANs, OpenCV, Caffe, TensorFlow,PyTorch Source code Facebook : Deep Learning, Computer Vision, Robotics The link to one-note notebook GitHubBooksThe Art of Startup FundraisingMathematics for Machine LearningPrinciples of Economics (6th edition)The E-Myth Revisited Why Most Small Businesses Don’t Work and What to Do About ItClean Code: A Handbook of Agile Software Craftsmanship (Robert C. Martin)Reinventing Your Life: The Breakthough Program to End Negative BehaviorWays Of Thinking: The Limits Of Rational Thought And Artificial Intelligence Computer Vision: Algorithms and ApplicationsMultiple View Geometry in Computer Vision Second EditionComplete to download the latest draft of Machine Learning YearningMultiple view geometry in computer vision Learning OpenCV Book by Adrian Kaehler and Gary Bradski Digital Image Processing, Global Edition by Rafael C. GonzalezIntroduction to Algorithms, 3rd Edition (The MIT Press) The Mythical Man-Month: Essays on Software EngineeringOpenCV-4-with-Python-Blueprints-Second-EditionVideos Two Minute PapersConference on Pattern Recognition PapersCALTag: High Precision Fiducial Markers for CameraDiatom Autofocusing in Brightfield Microscopy: a Comparative Study Analysis of focus measure operators in shape-from-focusOptical flow modeling and computation: A surveyToward general type 2 fuzzy logic systems based on zSlicesSiteResearch Tools By: Nader Ale Ebrahim (MindMap) Reference-ComputerVisionDeepLearning.pdf map: octopus.do HOW I GAINED 68,000 SUBSCRIBERS IN A YEAR! My Strategy to Grow on YouTube in 2021! - YouTube - YouTube (UA) - - - - - YouTubeSocial Selling Index Sales Navigator Google SitesReport abusePage detailsPage updated Google SitesReport abuseThis site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. 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Computer Vision, Deep Learning, Artificial superintelligence (ASI) - Full Stack Deep LearningSearch this siteSkip to main contentSkip to navigationComputer Vision, Deep Learning, Artificial superintelligence (ASI)HomeProductCoursesMachine Learning SpecializationMachine Learning Foundations: A Case Study ApproachFSDLFull Stack Deep LearningMLOpsROSParallel Programming Modern CPPCloud-NativeIoT Scholarship FoundationTensorFlow: Data and Deployment Specialization Workshops and EventsRISC-VEdge-AI-summitEmbedded IoTTeslaAI-HardwareOpenVINO Deep LearningMetaverseWorkshopsIFA2022Book Summary commonplace bookknowledgemanagementPKMTopics and ProjectsAIHubChatGPTHow to startYouTubeYouTube IISoftwareRoadmap for Image Processing Source CodeOpenCVCPPpythonMacOSOpenCVRustcompileIoTShareVideo TrackingCameraCalibrationDRLHardwareQuantum ComputingAltCoinResumeCVApplestartupLinksamazonAboutFQApromptComputer Vision, Deep Learning, Artificial superintelligence (ASI)Full Stack Deep LearningFull Stack Deep LearningFull Stack Deep LearningFull Stack Deep Learning (fullstackdeeplearning.com)notes for week 1 to week 12 of Full Stack Deep Learning: (2021)Reference: Learn More: Common solution for under-fitting or over-fitting: check data-set, error analysis, choose a different model architecture, hyper-parameter tuning Under-fitting (reducing bias): bigger model reduce regularization error analysis different model architecture tune hyper-parameters add featuresover-fitting (reducing variance): add more training data add normalization (batch norm, layer norm) add data augmentation increase regularization (dropout, L2, weight decay) error analysis choose a different model architecture tune hyper-parameters early stopping remove features reduce model sizeGoogle SitesReport abusePage detailsPage updated Google SitesReport abuseThis site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies.Learn moreGot it

Computer Vision, Deep Learning, Artificial superintelligence (ASI) - CoursesSearch this siteSkip to main contentSkip to navigationComputer Vision, Deep Learning, Artificial superintelligence (ASI)HomeProductCoursesMachine Learning SpecializationMachine Learning Foundations: A Case Study ApproachFSDLFull Stack Deep LearningMLOpsROSParallel Programming Modern CPPCloud-NativeIoT Scholarship FoundationTensorFlow: Data and Deployment Specialization Workshops and EventsRISC-VEdge-AI-summitEmbedded IoTTeslaAI-HardwareOpenVINO Deep LearningMetaverseWorkshopsIFA2022Book Summary commonplace bookknowledgemanagementPKMTopics and ProjectsAIHubChatGPTHow to startYouTubeYouTube IISoftwareRoadmap for Image Processing Source CodeOpenCVCPPpythonMacOSOpenCVRustcompileIoTShareVideo TrackingCameraCalibrationDRLHardwareQuantum ComputingAltCoinResumeCVApplestartupLinksamazonAboutFQApromptComputer Vision, Deep Learning, Artificial superintelligence (ASI)Courses a partial abstract representation - analyze, communicate, test, document, understanding of the systemcomputational: time-varying behavior of a systemanalytical: math of relationship among variables in a systemnon-analyticaldescriptive: describe components and their relationships in a system.application: UML models, sysML models, BPMN modelsData: relational, network, hierarchical models - OO data modelsUML: 2.5 (2015) - omg.org UML DiagramsStructure: static viewClass diagramclassifiersfeaturesrelationshipsComponent diagramObject diagramComposite structure diagramPackage diagramBehavior: dynamic viewDeployment diagramUse case diagramuse casessystemsactorsassociationsActivity diagramState machine diagramInteractionSequence diagramCommunication diagramTiming diagramInteraction overviewdiagramaggregation and compositionattributes properties characteristics state fields variablesobject instancedefining a class creating objects instantiation superclassparent classbase classsubclasschild classderived classclass components:identitynametype: glass attributespropertiesdata: color, size, fullnessbehaviorsoperations: fill(), empty(), clean() methodabstractionpolymorphisminheritanceencapsulationstartuml farshidleft to right directionpackage “High level definition for Image Processing interface” together interface outputdata data interface inputdata data interface imageprocessingclass inputdata outputdata inputdata “1” – “many” imageprocessingclass : contains outputdata “1” – “many” imageprocessingclass : containsnewpageinterface outputdata data note top of outputdata DataNodeend noteenduml functional requirements: The module must test opencv inputoutput imageallow to check grand truth image and output imagemaintain a library of all comparison SSIM, PSNR, …allow to choose different comparison algorithms: PSNR, SSIM, …non functional requirements: the module should be … (maintainability, reliability, usability, availability)simple library attached to projectfastupdate-able FURPS requirements:functionality: SSIM, PSNR,usability: attached to the project as a class testreliability: assert in C performance: real-time support-ability: simple class with all functions use cases: title: developer test image processing functions by see ground truth image and output image differences primary actor: computer vision developer success scenario: check and see differences between ground truth image and output image by different matrix such as SSIM, PSNR, … user story: as a computer vision developer i want to test my output image so that I can see ground truth image and my output image differences classes - objects relationship between objects conceptual object model CRC card CRH cardClass (name of class) Component : class test histogramresponsibility responsibility : test two image histogram and compare themcollaboration Helper get ground truth image and compare it with output functionclassA myclassnew classA()constructor : we want to set value at the beginning and not problem with null or 0 or … initialization destructor finalizer static variable: shared across all objects in a class shared variable class variable classA.staticVariableSubClass(int foo, int bar): SuperClass(foo)interface: list of methods —-SOLID, DRY (don’t repeat yourself), YAGNI (your ain’t gonna need it), design pattern, Google SitesReport abusePage detailsPage updated Google SitesReport abuseThis site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies.Learn moreGot it

Computer Vision, Deep Learning, Artificial superintelligence (ASI) - FSDLSearch this siteSkip to main contentSkip to navigationComputer Vision, Deep Learning, Artificial superintelligence (ASI)HomeProductCoursesMachine Learning SpecializationMachine Learning Foundations: A Case Study ApproachFSDLFull Stack Deep LearningMLOpsROSParallel Programming Modern CPPCloud-NativeIoT Scholarship FoundationTensorFlow: Data and Deployment Specialization Workshops and EventsRISC-VEdge-AI-summitEmbedded IoTTeslaAI-HardwareOpenVINO Deep LearningMetaverseWorkshopsIFA2022Book Summary commonplace bookknowledgemanagementPKMTopics and ProjectsAIHubChatGPTHow to startYouTubeYouTube IISoftwareRoadmap for Image Processing Source CodeOpenCVCPPpythonMacOSOpenCVRustcompileIoTShareVideo TrackingCameraCalibrationDRLHardwareQuantum ComputingAltCoinResumeCVApplestartupLinksamazonAboutFQApromptComputer Vision, Deep Learning, Artificial superintelligence (ASI)Full Stack Deep Learning FSDL 2022Lecture 07 Note on Full Stack Deep Learning FSDL 2022; more: Lecture 06 Note on Full Stack Deep Learning FSDL 2022; more: Lecture 05 Note on Full Stack Deep Learning FSDL 2022; Lecture 06: Continual Learning (FSDL 2022)- what metrics to monitor - Outcomes and feedback from users - Model performance metrics - Proxy metrics - Data quality testing - accuracy - completeness - consistency - timeliness - validity - integrity - Distribution drift - type - instantaneous drift like - gradual drift - periodic drifts - temporary drift - measure - reference window - metric - 1D : KL , KS - dealing with high-dimensional data - projections - system metrics - how to tell if those metrics are “bad” - KS-Test - good - 1 fixed rule - 2 specified range - 3 predicted range - 4 unsupervised detection - - tools for monitoring - system monitoring tools - datadog - honeycomb.io - NewRelic - amazon cloudwatch - OSS ML monitoring: evidently AI, why logs - 1 logging - profiling - sampling - 2 curation - L1: just sample randomly - L2: stratified sampling - L3: curate “interesting” data - manually - similarity-based curation - projection-based curation - automatically curating data using active learning - scoring function - most uncertain - highest predicted loss - most different from labels - most representative - big impact on training - tools: scale nucleus, data-centric ML tools - 3 retraining triggers - based on performance - online learning - 4 dataset formation - 1: train on all available data - 2: sliding window - 3: online batch selection - 4: continual fine-tuning - 5: offline testing - dynamic - expectation tests - 6: online testing - shadow mode, AB test, roll out gradually, roll back, …- trying it all more: Lecture 04 Note on Full Stack Deep Learning FSDL 2022; Lecture 04: Data Management (FSDL 2022)- fixingaddingaugmenting data: keep it simple- data sources - filesystem: local disk speeds: NVME M.2 SSD, latency: nice visualization - - object storage: usually binary object can versioning, redundancy “S3”, - database: persistent, fast, scalable, in RAM, object-store URLs, - postgres, SQLite - data warehouse: OLAP, OLTP, ETL - data lake: unstructured: ELT, - SQL and DataFrames - SQL: structured - Pandas is DataFrames: DASK parallelize pandas, RAPIDS pandas on GPUs- Airflow: specify the DAG of tasks using python - Prefect - Dagster- feature stores - tecton.ai - FEAST - Featureform- Hu- Activeloop- Labeling - self-supervised learning - image data augmentation - HIVE - scale.ai - labelbox - label studio - diffgram - aquarium and scale nucleus - weak supervision : snorkel.ai - rubrix- data versioning: level 1 - level 3: DVC - privacy more: Lecture 03: testing Note on Full Stack Deep Learning FSDL 2022; more: Lecture 02: Development Infrastructure & Tooling (FSDL 2022) Note on Full Stack Deep Learning FSDL 2022; more: Lecture 01 Lab 1&2&3: Note on Full Stack Deep Learning FSDL 2022; ResnetTransformer, teacherforward, –precision 16, - –limittrainbatches 10 more: Lecture 01: When to Use ML and Course Vision Formulating the problem and estimating project costSourcing, cleaning, processing, labeling, synthesizing, and augmenting dataPicking the right framework and compute infrastructureTroubleshooting training and ensuring reproducibilityDeploying the model at scale Monitoring and continually improving the deployed model How ML teams work and how to manage ML projects Building on Large Language Models and other Foundation Models Google SitesReport abusePage detailsPage updated Google SitesReport abuseThis site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies.Learn moreGot it

Computer Vision, Deep Learning, Artificial superintelligence (ASI) - AppleSearch this siteSkip to main contentSkip to navigationComputer Vision, Deep Learning, Artificial superintelligence (ASI)HomeProductCoursesMachine Learning SpecializationMachine Learning Foundations: A Case Study ApproachFSDLFull Stack Deep LearningMLOpsROSParallel Programming Modern CPPCloud-NativeIoT Scholarship FoundationTensorFlow: Data and Deployment Specialization Workshops and EventsRISC-VEdge-AI-summitEmbedded IoTTeslaAI-HardwareOpenVINO Deep LearningMetaverseWorkshopsIFA2022Book Summary commonplace bookknowledgemanagementPKMTopics and ProjectsAIHubChatGPTHow to startYouTubeYouTube IISoftwareRoadmap for Image Processing Source CodeOpenCVCPPpythonMacOSOpenCVRustcompileIoTShareVideo TrackingCameraCalibrationDRLHardwareQuantum ComputingAltCoinResumeCVApplestartupLinksamazonAboutFQApromptComputer Vision, Deep Learning, Artificial superintelligence (ASI)AppleiPhone 61809 mAh VIDEO STREAMING. WEB BROWSING9:24h - 10:29hiPhone XS Max3174 mAhVIDEO STREAMING. WEB BROWSING11:06h - 13:43hiPhone 14 Pro Max4323 mAhVIDEO STREAMING. WEB BROWSING23:39h - 24:38h Google SitesReport abusePage detailsPage updated Google SitesReport abuseThis site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies.Learn moreGot it

Computer Vision, Deep Learning, Artificial superintelligence (ASI) - Roadmap for Image Processing Search this siteSkip to main contentSkip to navigationComputer Vision, Deep Learning, Artificial superintelligence (ASI)HomeProductCoursesMachine Learning SpecializationMachine Learning Foundations: A Case Study ApproachFSDLFull Stack Deep LearningMLOpsROSParallel Programming Modern CPPCloud-NativeIoT Scholarship FoundationTensorFlow: Data and Deployment Specialization Workshops and EventsRISC-VEdge-AI-summitEmbedded IoTTeslaAI-HardwareOpenVINO Deep LearningMetaverseWorkshopsIFA2022Book Summary commonplace bookknowledgemanagementPKMTopics and ProjectsAIHubChatGPTHow to startYouTubeYouTube IISoftwareRoadmap for Image Processing Source CodeOpenCVCPPpythonMacOSOpenCVRustcompileIoTShareVideo TrackingCameraCalibrationDRLHardwareQuantum ComputingAltCoinResumeCVApplestartupLinksamazonAboutFQApromptComputer Vision, Deep Learning, Artificial superintelligence (ASI)Roadmap for Image Processing Google SitesReport abusePage detailsPage updated Google SitesReport abuseThis site uses cookies from Google to deliver its services and to analyze traffic. Information about your use of this site is shared with Google. By using this site, you agree to its use of cookies.Learn moreGot it