End to End Object Detection

Become an Object Detection Guru with the latest frameworks available like Tensorflow, Detectron2 and Yolo. In this course you will be learning to create four different object detector using multiple frameworks from scratch.

3.75 (4 Reviews)
Language: English
Price

INR 500

Course Features
  • Lifetime Dashboard Access
  • Certificate
  • End to End Project
  • Self paced classes

Course Overview

Creating end to end web applications for object detectors using multiple frameworks like Tensorflow, Detectron2 and Yolo in this practical oriented course. You will be a wizard of building State of the art object detection applications.
What you'll learn
  • Python Basics
  • Flask Development
  • Pycharm Basics
  • Debug Applications
  • Tensorflow1.x Object Detection
  • Tensorflow2.x Object Detection
  • Detectro2 Object Detection/Segmentation
  • Yolo Object Detection
  • Working with Images
  • Working with Videos
Requirements
  • Computer with Internet Connectivity
  • Basic Python Knowledge
  • 8GB RAM preferred
  • Intel Core i5 preferred
  • Windows/Linux/MAC Preferred

Course Curriculum

  • Introduction to Course
  • Who is this Course for?
  • Course Overview
  • Course Outcome
  • Installing Anaconda, Pycharm & Postman
  • Working with Conda Envs
  • Pycharm Introduction
  • Pycharm with Conda
  • Pycharm with venv
  • Pycharm with Pipenv
  • Introduction
  • Building a Calculator
  • Working with Command Line Arguments
  • Building the Flask Application
  • Testing our App in POSTMAN
  • Learn to Debug with Pycharm
  • Adding an UI to our Web App
  • Introduction
  • What is Object Detection?
  • What are Bounding Boxes?
  • Metrics used in Object Detection
  • Applications of Object Detection
  • Introduction
  • Introduction to TFOD1.x
  • Using Google Colab with Google Drive
  • Installation of Libraries in Colab
  • TFOD1.x Setup in Colab
  • Visiting the Model Zoo
  • Inferencing in Colab
  • Inferencing in Local
  • Important Configuration Files
  • Webcam Testing
  • Introduction
  • Our Custom Dataset
  • Doing Annotations or labeling data
  • Preparing the Dataset for Training
  • Selection of Pretrained Model from Model Zoo
  • Files Setup for Training
  • Let's start Training
  • Resume or Stop Training
  • Converting CKPT to Frozen Inference Graph
  • Inferencing with our trained model
  • Introduction
  • Creating a Pycharm project & Environment Setup
  • Debugging our Application
  • Testing our App with PoSTmaN
  • Adding an UI to our Web APP
  • Introduction
  • Introduction to TFOD2.x
  • Installation of Libraries in Colab
  • Visting TFOD2.x Model Garden
  • Inference using Pretrained Model
  • Important Configuration Files
  • Inferencing in Local with a pretrained model
  • Introduction
  • Our Custom Dataset
  • Doing Annotations or labeling data
  • Preparing the Dataset for Training
  • Selection of Pretrained Model from Model Zoo
  • File Setup for Training
  • Let's start Training
  • Stop Training or resume Training
  • Convert CKPT to Saved Model
  • Inferencing using the Custom Trained Model in Colab
  • Inferencing using the Custom Trained Model in Local PC
  • Introduction
  • Creating a Pycharm project & Environment Setup
  • Building a Flask Application
  • Debugging our Application
  • Testing our App with PoSTmaN
  • Adding an UI to our Web APP
  • Introduction
  • Introduction to Detectron2
  • Installing libraries in Google Colab
  • Visiting the Model Zoo
  • Inferencing using Pre Trained Model
  • Introduction
  • Our Custom Dataset
  • Doing Annotations or labeling data
  • Registering Dataset for Training
  • Selection of Pretrained Model from Model Zoo
  • Let's start Training
  • Stop Training or resume Training
  • Inferencing using the Custom Trained Model in Colab
  • Evaluating the Model
  • Introduction
  • Creating a Pycharm project & Environment Setup
  • Building a Flask Application
  • Debugging our Application
  • Testing our App with PoSTmaN
  • Adding an UI to our Web APP
  • Introduction
  • Introduction to YoloV5
  • Inferencing using Pre Trained Model
  • Introduction
  • Our Custom Dataset
  • Doing Annotations or labeling data
  • Preparing the Dataset for Training
  • Let's start Training
  • Inferencing using the Custom Trained Model in Colab
  • Introduction
  • Creating a Pycharm project & Environment Setup
  • Building a Flask Application
  • Debugging our Application
  • Testing our App with PoSTmaN
  • Adding an UI to our Web APP

Course Projects

3.75 out of 5.0
1 Star 0.0%
2 Star 0.0%
3 Star 50.0%
4 Star 25.0%
5 Star 25.0%
Sourangshu Paul

Having 3+ years of experience in Computer Vision and Analytics with product architecture design and delivery. Skilled in classical visual computing techniques and building state of the art solution using deep learning and helping them to make a career transition.

Reviews

Kathiravan Selvam
July 22,2021
5.00

" Great Course. Thanks to Paul. The course is really crisp and gives high level overview of the object detection with custom example... "

Swatik Sahoo
July 01,2021
4.00

" Please start a course on how to study the GitHub repository and successfully execute them. This course on end to end object detection was an interesting step towards this hope to see more such courses from paul sir "

radheshyam Tiwari
June 22,2021
3.00

" Good teaching style . He discussed basic but didn\'t discuss project coding and handling Errors while execution of the project. please understand we are not an expert, we are here to learn and most of the time we are not able to run the redeemed project on our local system. so also focus on those are how to solve various environment error while installing requerment.txt and other. If we are not able to learn these projects on our system how can we explore them through various experiments? We also need recordings of the project code summary discussion. thanks "

Submit Reviews

You can not rate this course before login

Join Thousand of Happy Students!

Subscribe our newsletter & get latest news and updation!