Learn TensorRT, ONNX with Detection,Segmentation 12 PROJECTS_1

Learn TensorRT, ONNX with Detection,Segmentation 12 PROJECTS, available at $84.99, has an average rating of 3.75, with 174 lectures, 7 quizzes, based on 58 reviews, and has 639 subscribers.

You will learn about 1. What is Docker and How to use Docker & their practical usage 2. What is Kubernet and How to use with Docker & their practical usage 3. Nvidia SuperComputer and Cuda Programming Language & their practical usage 4. What are OpenCL and OpenGL and when to use & their practical usage 6.(LAB) Tensorflow/TF2 and Pytorch Installation, Configuration with DOCKER 7. (LAB)DockerFile, Docker Compile and Docker Compose Debug file configuration 8. (LAB)Different YOLO version, comparisons, and when to use which version of YOLO according to your problem 9. (LAB)Jupyter Notebook Editor as well as Visual Studio Coding Skills 10. (LAB) Visual Studio Code Setup and Docker Debugger with VS 11. (LAB) what is ONNX fframework and how to use apply onnx to your custom problems 11. (LAB) What is TensorRT Framework and how to use apply to your custom problems 12. (LAB) Custom Detection, Classification, Segmentation problems and inference on images and videos 13. (LAB) Python3 Object Oriented Programming 14.(LAB)Pycuda Language programming 15. (LAB) Deep Learning Problem Solving Skills on Edge Devices, and Cloud Computings 16. (LAB) How to generate High Performance Inference Models , in order to get high precision, FPS detection as well as less gpu memory consumption 17. (LAB) Visual Studio Code with Docker 18.(LAB Challenge) yolov4 onnx inference with opencv dnn 19.(LAB Challenge) yolov5 onnx inference with opencv dnn 20.(LAB Challenge) yolov5 onnx inference with Opencv DNN 21.(LAB Challenge) yolov5 onnx inference with TensorRT and Pycuda 22.(LAB) ResNet Image Classificiation with TensorRT and Pycuda 23.(LAB) yolov5 onnx inference on Video Frames with TensorRT and Pycuda 24. (LAB) Prepare Yourself for Python Object Oriented Programming Inference! 25. (LAB) Python OOP Inheritance Based on YOLOV7 Object Detection 26. Deep Theoretical Knowledge about Small Target Detection and Image Masking 27. Deep Insight on Yolov5/Yolov6/Yolov7/Yolov8 Architectures and Practical Use Cases 28. Deep Insight on YoloV5 P5 and P6 Models & Their Practical Usage 29. Key Differences:Explicit vs. Implicit Batch Size 30. (Theory) TenSorRT Optimization Profile Tutorial 31. (Theory) Boost TensorRT Knowledge for Beginner Level Quizzies 32. (Theory Challenge) Boost TensorRT Knowledge for? Intermediate Level Quizzies 33. Theory Challenge) Boost TensorRT? Knowledge for Advance Level Quizzies 34.(Theory Challenge) Boost? Cuda Runtime for Beginner/Intermediate/Advance practical & theorytical Quizzies 35.(Theory Challenge) Boost your OpenCV-ONNX Knowledge by doing Mixed? practical & theorytical Quizzies 36.(Deep Theoratical Knowledge) YoloV8 ONNX Model Input and Output Inference 37.(Deep Theoratical Knowledge) YoloV8 Model usage and applied sectors. 38.(Deep Practical Knowledge) YoloV8 ONNX Model for Detection and Segmentation 39. DeepLabV3 with Resnet 101 AND UNet Semantic Segmentation 40.(Bonus Lecture) Mastering Deep Reinforcement Learning with Advance Exercises This course is ideal for individuals who are new graduates or university students or AI experts or Embedded Software Engineer or Robotics Engineer It is particularly useful for new graduates or university students or AI experts or Embedded Software Engineer or Robotics Engineer.

Enroll now: Learn TensorRT, ONNX with Detection,Segmentation 12 PROJECTS

Summary

Title: Learn TensorRT, ONNX with Detection,Segmentation 12 PROJECTS

Price: $84.99

Average Rating: 3.75

Number of Lectures: 174

Number of Quizzes: 7

Number of Published Lectures: 172

Number of Published Quizzes: 7

Number of Curriculum Items: 182

Number of Published Curriculum Objects: 180

Original Price: 54.99

Quality Status: approved

Status: Live

What You Will Learn

1. What is Docker and How to use Docker & their practical usage

2. What is Kubernet and How to use with Docker & their practical usage

3. Nvidia SuperComputer and Cuda Programming Language & their practical usage

4. What are OpenCL and OpenGL and when to use & their practical usage

6.(LAB) Tensorflow/TF2 and Pytorch Installation, Configuration with DOCKER

7. (LAB)DockerFile, Docker Compile and Docker Compose Debug file configuration

8. (LAB)Different YOLO version, comparisons, and when to use which version of YOLO according to your problem

9. (LAB)Jupyter Notebook Editor as well as Visual Studio Coding Skills

10. (LAB) Visual Studio Code Setup and Docker Debugger with VS

11. (LAB) what is ONNX fframework and how to use apply onnx to your custom problems

11. (LAB) What is TensorRT Framework and how to use apply to your custom problems

12. (LAB) Custom Detection, Classification, Segmentation problems and inference on images and videos

13. (LAB) Python3 Object Oriented Programming

14.(LAB)Pycuda Language programming

15. (LAB) Deep Learning Problem Solving Skills on Edge Devices, and Cloud Computings

16. (LAB) How to generate High Performance Inference Models , in order to get high precision, FPS detection as well as less gpu memory consumption

17. (LAB) Visual Studio Code with Docker

18.(LAB Challenge) yolov4 onnx inference with opencv dnn

19.(LAB Challenge) yolov5 onnx inference with opencv dnn

20.(LAB Challenge) yolov5 onnx inference with Opencv DNN

21.(LAB Challenge) yolov5 onnx inference with TensorRT and Pycuda

22.(LAB) ResNet Image Classificiation with TensorRT and Pycuda

23.(LAB) yolov5 onnx inference on Video Frames with TensorRT and Pycuda

24. (LAB) Prepare Yourself for Python Object Oriented Programming Inference!

25. (LAB) Python OOP Inheritance Based on YOLOV7 Object Detection

26. Deep Theoretical Knowledge about Small Target Detection and Image Masking

27. Deep Insight on Yolov5/Yolov6/Yolov7/Yolov8 Architectures and Practical Use Cases

28. Deep Insight on YoloV5 P5 and P6 Models & Their Practical Usage

29. Key Differences:Explicit vs. Implicit Batch Size

30. (Theory) TenSorRT Optimization Profile Tutorial

31. (Theory) Boost TensorRT Knowledge for Beginner Level Quizzies

32. (Theory Challenge) Boost TensorRT Knowledge for? Intermediate Level Quizzies

33. Theory Challenge) Boost TensorRT? Knowledge for Advance Level Quizzies

34.(Theory Challenge) Boost? Cuda Runtime for Beginner/Intermediate/Advance practical & theorytical Quizzies

35.(Theory Challenge) Boost your OpenCV-ONNX Knowledge by doing Mixed? practical & theorytical Quizzies

36.(Deep Theoratical Knowledge) YoloV8 ONNX Model Input and Output Inference

37.(Deep Theoratical Knowledge) YoloV8 Model usage and applied sectors.

38.(Deep Practical Knowledge) YoloV8 ONNX Model for Detection and Segmentation

39. DeepLabV3 with Resnet 101 AND UNet Semantic Segmentation

40.(Bonus Lecture) Mastering Deep Reinforcement Learning with Advance Exercises

Who Should Attend

new graduates

university students

AI experts

Embedded Software Engineer

Robotics Engineer

Target Audiences

new graduates

university students

AI experts

Embedded Software Engineer

Robotics Engineer

For WHOM , THIS COURSE is HIGHLY ADVISABLE:

This course is mainly considered for any candidates(students, engineers,experts)that havegreat motivation to learn deep learning model training and deeployment. Candidates will have deep knowledge of docker, usage of TENSORFLOW ,PYTORCH, KERAS models with DOCKER. In addition,they will be able to OPTIMIZE , QUANTIZE deeplearning models with ONNX and TensorRT frameworks for deployment in variety of sectors such as on edge devices(nvidia jetson nano, tx2, agx, xavier, qualcomm rb5, rasperry pi, particle photon/photon2), AUTOMATIVE, ROBOTICS as well as cloud computing via AWS, AZURE DEVOPS, GOOGLE CLOUD, VALOHAI, SNOWFLAKES. 

Usage of TensorRT and ONNX in Edge Devices:

      Edge Devices are built-in hardware accelerator with nvidia gpu that allows to acccelare real time inference 20x Faster to achieve fast and accurate performance.

1. nvidia jetson nano, tx2, agx, xavier : jetpack 4.5/4.6 cuda accelerative libraries

2. Qualcomm rb5  together with Monoculare and Stereo Vision Camera(CSI/MPI , USB camera )

3. Particle photon/photon2  IoT in order to achieve Web API, through speech recognition systems , for Smart House

4. Robotics: Robot Operations Systems packages  for monocular and Stereo Vision Camera, in order to 3D Tranquilation ,for Human Tracking and Following, Anomaly Target and Noise Detection such as (gun noise, extremely high background  noise)

5. Rasperry Pi 3A/3B/4B gpu OpenGL compiler based

Usage of TensorRT and ONNX in Robotics Devices:

1. Overview of Nvidia Devices and Cuda compiler language

2. Overview Knowledge of OpenCL and OpenGL

3. Learning and Installation of Docker from scratch

4. Preparation of DockerFiles, Docker Compose as well as Docker Compose Debug file

5. Implementing and Python codes via both Jupyter notebook as well as Visual studio code

6. Configuration and Installation of Plugin packages in Visual Studio Code

7. Learning, Installation and Confguration of frameworks such as Tensorflow, Pytorch, Kears with docker images from scratch

8. Preprocessing and Preparation of Deep learning datasets for training and testing

9. OpenCV  DNN 

10. Training, Testing and Validation of Deep Learning frameworks

11. Conversion of prebuilt models to Onnx  and Onnx Inference on images

12. Conversion of onnx model to TensorRT engine

13. TensorRT engine Inference on images and videos

14. Comparison of achieved metrices and result between TensorRT and Onnx Inference

15. Prepare Yourself for Python Object Oriented Programming Inference!

16. Deep Knowledge on Yolov5 P5 and P6 Large Models

17. Deep Knowledge on Yolov5/YoloV6 Architecture and Their Use Cases

18. Deep Theoretical and Practical Coding Skill on Research Paper of Yolov7/Yolov8 Small and Large Models

19. Boost TensorRT Knowledge for Beginner Level Quizzies

20. Boost TensorRT Knowledge for  Intermediate Level Quizzies

21. Boost TensorRT  Knowledge for Advance Level Quizzies

22. Boost Nvidia-Drivers for Beginner/Intermediate/Advance practical & theorytical Quizzies

23. Boost  Cuda Runtime for Beginner/Intermediate/Advance practical & theorytical Quizzies

24. Boost your OpenCV-ONNX Knowledge by doing Mixed  practical & theorytical Quizzies

25. ONNX beginner and Advance Pythons coding Skills for auto-tuning Yolov8 ONNX model hyperparameters and Input (Fast Image or Video Pre-Post processing) for Detection and Semantic Segmentation

26. Deep Reinforcement learning with practical example and deep python programming such as Game of Frozen Lake, Drone of Lunar Lader etc

27. Beginner, Intermediate Vs Advance Transfer Learning Custom Models

28. Beginner, Intermediate Vs Advance Object Classification

29. Beginner, Intermediate Vs Advance Object Localization and Detection

30. Beginner, Intermediate Vs Advance Image Segmentation

31. AI For Medical Treatment

32. Implement yourseld Advance Object detection and Segmentation Metrics

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: who can take this course

Lecture 3: course description and why this course is higly flexible for your needs

Lecture 4: Detection and Tracking

Lecture 5: YOLOP Model for Detect and Segment ONNX Inference

Lecture 6: Practice, Practice And Again Practice

Lecture 7: Course Github Projects

Lecture 8: YoloV7 Fast Video Inference Detect and Track

Lecture 9: My Second Course – Generative AI

Chapter 2: Course Rating Evalutions

Lecture 1: How to rate this course?

Chapter 3: Onnx, TensorRT, Docker Overview

Lecture 1: Onnx, TensorRT, Docker Tutorial (part 1)

Lecture 2: Onnx, TensorRT, Docker Tutorial (part 2)

Lecture 3: Onnx, TensorRT, Docker Tutorial (part 3)

Lecture 4: Onnx, TensorRT, Docker Tutorial (part 4)

Chapter 4: NVIDIA Drivers

Lecture 1: how to install nvidia drivers and set up

Lecture 2: Download Nvidia Driver (Part two)

Lecture 3: Verify Installation of Nvdia Driver and Nouveau Driver

Lecture 4: Verify Installation of Nvidia Driver (Part two)

Chapter 5: Learn Nvidia Drivers deeply, by doing quizzies

Chapter 6: Nvidia Hardware and Software, Cuda programming API Levels

Lecture 1: Docker and Nvidia Stack (Part One)

Lecture 2: Docker and Nvidia Gpu Stack (Part two)

Lecture 3: Docker and Nvidia Gpu Stack (Part three)

Chapter 7: Learn Cuda Runtime by doing Quizzies

Chapter 8: Docker Installation and Configuration

Lecture 1: Docker Images Installation and Configuration

Lecture 2: Docker SetUp and Configuration with Sudo on Local Machine

Lecture 3: Setup Docker Successfuly on your local Machine

Chapter 9: Learn-Repeat OpenCV-ONNX mixed features with Quizzies

Chapter 10: Installation of Docker Cuda Toolkit & Setup DockerFile with required packages

Lecture 1: Installing Docker Cuda Toolkit-Nvidia GPU

Lecture 2: Install, Configure,Validate Tensorflow-GPU Docker Image

Lecture 3: What is Docker? and why we need to use Docker Server-Docker Commands Tutorial

Lecture 4: Configuration of Docker Working Directories and DockerFiles

Lecture 5: Organization of Docker files with required packages installations (Part One)

Lecture 6: Organization of Docker files with required packages installations (Part Two)

Chapter 11: TensorRT & Onnx AI frameworks

Lecture 1: Driver , Kernel and Device Communication

Lecture 2: Deep Learning Frameworks

Lecture 3: Open Neural Network Exchange

Lecture 4: TensorRT – NVIDIA Inference

Lecture 5: TensorRT – NVIDIA Inference Floating Point Precision and AI Sectors

Lecture 6: Nvidia Software and Hardware Logic

Chapter 12: Resnet 18 with ONNX-TENSORRT

Lecture 1: Docker Configuration for Resnet 18

Lecture 2: Docker Configuration for Resnet 18 (Part 2)

Lecture 3: SetUp Visual Studio Code with Docker Container

Lecture 4: Resnet 18 with ONNX

Lecture 5: Resnet 18 Conversion from Onnx to TensorRT (Part 1)

Lecture 6: Resnet 18 Conversion from Onnx to TensorRT (Part 2)

Lecture 7: Resnet 18 Conversion from Onnx to TensorRT (Part 3)

Lecture 8: Resnet 18 Conversion from Onnx to TensorRT (Part 4)

Chapter 13: Resnet 18 TensorRT Inference

Lecture 1: TensorrT Inference (Part 1)

Lecture 2: TensorrT Inference 2

Lecture 3: TensorrT Inference 3

Lecture 4: TensorrT Inference 4

Lecture 5: TensorrT Inference 5

Lecture 6: TensorrT Inference 6

Lecture 7: TensorrT Inference 7

Lecture 8: TensorrT Inference-TtrtExec API 8

Chapter 14: YOLOV4 ONNX DNN

Lecture 1: YOLOV4 ONNX DNN Inference (Part One)

Lecture 2: YOLOV4 ONNX DNN Inference (Part Two)

Lecture 3: YOLOV4 ONNX DNN Inference (Part Three)

Lecture 4: YOLOV4 ONNX DNN Inference (Part Four)

Lecture 5: YOLOV4 ONNX DNN Inference (Part Fifth)

Lecture 6: YOLOV4 ONNX DNN Inference (Part Six)

Lecture 7: YOLOV4 ONNX DNN Inference (Part Seven)

Lecture 8: YOLOV4 ONNX DNN Inference (Part Eight)

Chapter 15: YOLOV4 ONNX DNN Video

Lecture 1: Yolov4 Video Inference part 1

Lecture 2: Yolov4 Video Inference part 2

Lecture 3: Yolov4 Video Inference part 3

Lecture 4: Yolov4 Video Inference part 4

Chapter 16: YOLOv5 Onnx Inference – OpenCV

Lecture 1: SetUp Workign Directory of yolov5 (2)

Lecture 2: YOLOV4 ONNX DNN Inference (Part Nine)

Lecture 3: YOLOv5 Onnx Inference – OpenCV (Part 3)

Lecture 4: YOLOv5 Onnx Inference – OpenCV (Part 4)

Lecture 5: YOLOv5 Onnx Inference – OpenCV (Part 5)

Lecture 6: YOLOv5 Onnx Inference – OpenCV (Part 6)

Lecture 7: YOLOv5 Onnx Inference – OpenCV (Part 7)

Lecture 8: YOLOv5 Onnx Inference – OpenCV (Part 8)

Lecture 9: Yolov5 Onnx Inference (Part 9)

Lecture 10: Prepare Yolov5 For Inference ( Part 1)

Chapter 17: Yolov5 TensorRT Inference on Images

Lecture 1: TensorRT-yoloV5 Inference (Part 1) && what is OpenCL and OpenGL(their usage)

Lecture 2: TensorRT-yoloV5 Inference (Part 2)

Lecture 3: TensorRT-yoloV5 Inference (Part 3)

Lecture 4: TensorRT-yoloV5 Inference (Part 4)

Lecture 5: TensorRT-yoloV5 Inference (Part 5)

Instructors

PhD Researcher AI & Robotics Scientist Fikrat Gasimov
Senior PhD AI & Robotics Scientist & Embedded Software

Rating Distribution

1 stars: 3 votes

2 stars: 6 votes

3 stars: 6 votes

4 stars: 6 votes

5 stars: 37 votes

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