Deep learning :End to End Object Detection Masters
Deep learning :End to End Object Detection Masters, available at $59.99, has an average rating of 4.2, with 107 lectures, based on 43 reviews, and has 296 subscribers.
You will learn about Object Detection Building AI Applications Tensorflow1.x Object Detection Tensorflow 2.x Object Detection Facebooks's Detectron2 YoloV5 Working with Image Datasets Building Flask Web Applications API Testing with Postman Data Annotation & Labeling Computer vision Deep learning State of the art computer vision Object detection This course is ideal for individuals who are Data Scientists or Coputer Vision Engineers or Machine Learning Engineers or Python Developers or Deep Learing Engineers or Artificial Intelligence Engineers or Anyone interested in earning Practical Object Detection It is particularly useful for Data Scientists or Coputer Vision Engineers or Machine Learning Engineers or Python Developers or Deep Learing Engineers or Artificial Intelligence Engineers or Anyone interested in earning Practical Object Detection.
Enroll now: Deep learning :End to End Object Detection Masters
Summary
Title: Deep learning :End to End Object Detection Masters
Price: $59.99
Average Rating: 4.2
Number of Lectures: 107
Number of Published Lectures: 107
Number of Curriculum Items: 107
Number of Published Curriculum Objects: 107
Original Price: $19.99
Quality Status: approved
Status: Live
What You Will Learn
- Object Detection
- Building AI Applications
- Tensorflow1.x Object Detection
- Tensorflow 2.x Object Detection
- Facebooks's Detectron2
- YoloV5
- Working with Image Datasets
- Building Flask Web Applications
- API Testing with Postman
- Data Annotation & Labeling
- Computer vision
- Deep learning
- State of the art computer vision
- Object detection
Who Should Attend
- Data Scientists
- Coputer Vision Engineers
- Machine Learning Engineers
- Python Developers
- Deep Learing Engineers
- Artificial Intelligence Engineers
- Anyone interested in earning Practical Object Detection
Target Audiences
- Data Scientists
- Coputer Vision Engineers
- Machine Learning Engineers
- Python Developers
- Deep Learing Engineers
- Artificial Intelligence Engineers
- Anyone interested in earning Practical Object Detection
Become an Object Detection Guru with the latest frameworks available like Tensorflow, Detectron2, and YoloV5. In this course, you will be learning to create four different object detectors using multiple frameworks from scratch. Creating end-to-end web applications for object detectors using multiple deep learning frameworks in this practical-oriented course. You will be a wizard of building State of the art object detection applications.
4 Real Time Projects Included for 4 different frameworks.
More updates coming soon with more content and sections
1. detecto (May 2021 Update)
2. d2go (May 2021 Update)
3. mmdetection (June 2021 Update)
4. How to use Paperspace for training? (May 2021 Update)
5. How to use DataCruch for training? (May 2021 Update)
6. Moving from Flask to FastAPI (June 2021 Update)
7. Dockerizing your Applications (June 2021 Update)
8. Deploying your Applications in Cloud (July 2021 Update)
This course will show you the strategies used by real data scientists and machine learning professionals in the tech industry – and train you for a leap into this trendy career path if you have any programming experience.
Over 100 lectures are included in this detailed object detection tutorial. The emphasis is on practical understanding and implementation.
This course was created to assist you in learning how to train and evaluate object detection models.
This is accomplished by assisting you in a variety of ways, including:
Developing the requisite intuition to address most questions about object detection using deep learning, which is a common subject in interviews for roles in computer vision and deep learning.
By showing you how to create your own models using your own data.
You’ll be able to develop some effective Computer Vision solutions as a result of this.
You’ll also have access to the Skype Group, which will enable you to communicate with me and your classmates.
So, what exactly are you waiting for?
Enroll right now!
Course Curriculum
Chapter 1: Introduction and Setup
Lecture 1: Introduction to the Course
Lecture 2: Who is this Course for?
Lecture 3: Course Overview
Lecture 4: Course Outcome
Lecture 5: Installing Anaconda, Pycharm & Postman
Lecture 6: Working with Conda Environments
Lecture 7: Pycharm Introduction
Lecture 8: Pycharm with Conda
Lecture 9: Pycharm with Venv
Lecture 10: Pycharm with pipenv
Lecture 11: Download Section wise Resources/Materials
Chapter 2: Covering Python Basics
Lecture 1: Introduction
Lecture 2: Building the Calculator
Lecture 3: Command Line Arguments
Lecture 4: Flask App Development
Lecture 5: Testing with Postman
Lecture 6: Learn To Debug
Lecture 7: Adding UI to our App
Chapter 3: Introduction to Object Detection
Lecture 1: Introduction
Lecture 2: What is Object Detection?
Lecture 3: Bounding Box
Lecture 4: Metrics Used in Object Detection
Lecture 5: Applications of Object Detection
Chapter 4: Object Detection using Tensorflow 1.x
Lecture 1: Introduction
Lecture 2: Introduction to TFOD1.x
Lecture 3: Using Google Colab & Google Drive
Lecture 4: Installing Libraries in Google Colab
Lecture 5: TFOD1.x Setup in Google Colab
Lecture 6: Visiting Model Zoo
Lecture 7: Inferencing in Google Colab
Lecture 8: Inferencing in Local PC
Lecture 9: Important Configuration Files
Lecture 10: Webcam Testing
Chapter 5: Training Mask Detector using TFOD1.x
Lecture 1: Introduction
Lecture 2: About Our Dataset
Lecture 3: Data Annotation & Labeling
Lecture 4: Dataset Preparation
Lecture 5: Selection of Pretrained Model
Lecture 6: Files Setup For Training
Lecture 7: Let's Start Training
Lecture 8: Stop Or Resume Training
Lecture 9: Convert Checkpoint to Inference Graph
Lecture 10: Inferencing with our custom Trained Model
Chapter 6: Building a TFOD1.x Web Application
Lecture 1: Introduction
Lecture 2: Building the Flask Application
Lecture 3: Debugging our App
Lecture 4: Testing with Postman
Lecture 5: Adding an UI to our App
Chapter 7: Object Detection using Tensorflow 2.x
Lecture 1: Introduction
Lecture 2: Introduction to TFOD2.x
Lecture 3: Installing Libraries in Google Colab
Lecture 4: Visiting the Model Zoo
Lecture 5: Inferencing with Pretrained Model
Lecture 6: Important Configuration Files
Lecture 7: Inferencing in Local PC
Chapter 8: Custom Training with TFOD2.x
Lecture 1: Introduction
Lecture 2: Exploring our Chess Piece Detector Dataset
Lecture 3: Data Annotation & Labeling
Lecture 4: Dataset Preparation
Lecture 5: Selection of Pretrained Model
Lecture 6: Files Setup For Training
Lecture 7: Let's Start Training
Lecture 8: Resume or Stop Training
Lecture 9: Convert Checkpoint to Saved Model
Lecture 10: Inferencing in Google Colab
Lecture 11: Inferencing in Local PC
Chapter 9: Creating a Web Application with TFOD2.x
Lecture 1: Introduction
Lecture 2: Creating a Project
Lecture 3: Building the Flask Application
Lecture 4: Debugging our App
Lecture 5: Testing with Postman
Lecture 6: Adding an UI to our App
Chapter 10: Object Detection using Facebooks's Detectron2
Lecture 1: Introduction
Lecture 2: Detecron2 Introduction
Lecture 3: Installing Libraries in Google Colab
Lecture 4: Visiting the Model Zoo
Lecture 5: Inferencing with Pretrained Model
Chapter 11: Training Detecron2 with Custom Mixed Dataset
Lecture 1: Introduction
Lecture 2: Exploring our Mixed Dataset
Lecture 3: Data Annotation & Labeling
Lecture 4: Registering the Dataset
Lecture 5: Selection of Pretrained Model
Lecture 6: Let's Start Training
Lecture 7: Stop Or Resume Training
Lecture 8: Inferencing with custom trained model
Lecture 9: Evaluating your trained model
Chapter 12: Creating a Web Application with Detectron2
Lecture 1: Introduction
Lecture 2: Project Setup
Instructors
-
Ineuron Intelligence
iNeuron is an internationally recognized training institute
Rating Distribution
- 1 stars: 1 votes
- 2 stars: 2 votes
- 3 stars: 5 votes
- 4 stars: 15 votes
- 5 stars: 20 votes
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