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 Facebookss 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

Facebookss 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: Lets 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: Lets 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 Facebookss 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: Lets 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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