Deep learning :End to End Object Detection Masters

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