Deep Learning Object Detection by Training Deploying YOLOX

Deep Learning Object Detection by Training & Deploying YOLOX, available at $64.99, has an average rating of 4.33, with 23 lectures, based on 8 reviews, and has 134 subscribers.

You will learn about Master the basics of Object detection Understanding pre-deep learning algorithms like haarcascades Understanding deep learning algorithms like YOLO and YOLOX Create your own dataset with Remo Understanding the Pascal VOC dataset Convert your custom dataset to Pascal VOC Format Testing and training YOLOX model on custom dataset Integrating Wandb for experiment tracking Converting trained model to Onnx format Understanding how APIs work Building Object detection API with Fastapi Deploying API to the Cloud Load testing the API with Locust Running the object detection model in c++ This course is ideal for individuals who are Beginner Python Developers curious about applying deep learning techniques like YOLO or Software developers interested in using A.I and deep learning for object detection or Students interested in learning about object detection and how it can be applied practically or AI Practitioners wanting to master how to deploy AI Models to the cloud very easily or Software developers who want to learn how state of art object detection models are built and trained using deep learning. or Students who study different Object Detection Algorithms and want to Train YOLO with Custom Data. or Students who study Computer Vision and want to know how to use YOLO and its variants like YOLOX for Object Detection It is particularly useful for Beginner Python Developers curious about applying deep learning techniques like YOLO or Software developers interested in using A.I and deep learning for object detection or Students interested in learning about object detection and how it can be applied practically or AI Practitioners wanting to master how to deploy AI Models to the cloud very easily or Software developers who want to learn how state of art object detection models are built and trained using deep learning. or Students who study different Object Detection Algorithms and want to Train YOLO with Custom Data. or Students who study Computer Vision and want to know how to use YOLO and its variants like YOLOX for Object Detection.

Enroll now: Deep Learning Object Detection by Training & Deploying YOLOX

Summary

Title: Deep Learning Object Detection by Training & Deploying YOLOX

Price: $64.99

Average Rating: 4.33

Number of Lectures: 23

Number of Published Lectures: 23

Number of Curriculum Items: 23

Number of Published Curriculum Objects: 23

Original Price: $89.99

Quality Status: approved

Status: Live

What You Will Learn

Master the basics of Object detection

Understanding pre-deep learning algorithms like haarcascades

Understanding deep learning algorithms like YOLO and YOLOX

Create your own dataset with Remo

Understanding the Pascal VOC dataset

Convert your custom dataset to Pascal VOC Format

Testing and training YOLOX model on custom dataset

Integrating Wandb for experiment tracking

Converting trained model to Onnx format

Understanding how APIs work

Building Object detection API with Fastapi

Deploying API to the Cloud

Load testing the API with Locust

Running the object detection model in c++

Who Should Attend

Beginner Python Developers curious about applying deep learning techniques like YOLO

Software developers interested in using A.I and deep learning for object detection

Students interested in learning about object detection and how it can be applied practically

AI Practitioners wanting to master how to deploy AI Models to the cloud very easily

Software developers who want to learn how state of art object detection models are built and trained using deep learning.

Students who study different Object Detection Algorithms and want to Train YOLO with Custom Data.

Students who study Computer Vision and want to know how to use YOLO and its variants like YOLOX for Object Detection

Target Audiences

Beginner Python Developers curious about applying deep learning techniques like YOLO

Software developers interested in using A.I and deep learning for object detection

Students interested in learning about object detection and how it can be applied practically

AI Practitioners wanting to master how to deploy AI Models to the cloud very easily

Software developers who want to learn how state of art object detection models are built and trained using deep learning.

Students who study different Object Detection Algorithms and want to Train YOLO with Custom Data.

Students who study Computer Vision and want to know how to use YOLO and its variants like YOLOX for Object Detection

Object detection algorithms are everywhere. With creation of much more efficient models from the early 2010s, these algorithms which now are built using deep learning models are achieving unprecedented performances.

In this course, we shall take you through an amazing journey in which you’ll master different concepts with a step by step approach. We shall start from understanding how object detection algorithms work, to deploying them to the cloud, while observing best practices.

You will learn:

Pre-deep learning object detection algorithms like Haarcascades

Deep Learning algorithms like Convolutional neural networks, YOLO and YOLOX

Object detection labeling formats like Pascal VOC.

Creation of a custom dataset with Remo

Conversion of our custom dataset to the Pascal VOC format.

Finetuning and testing YOLOX model with custom dataset

Conversion of finetuned model to Onnx format

Experiment tracking with Wandb

How APIs work and building your own API with Fastapi

Deploying an API to the Cloud

Load testing a deployed API with Locust

Running object detection model in c++

If you are willing to move a step further in your career, this course is destined for you and we are super excited to help achieve your goals!

This course is offered to you by Neuralearn. And just like every other course by Neuralearn, we lay much emphasis on feedback. Your reviews and questions in the forum, will help us better this course. Feel free to ask as many questions as possible on the forum. We do our very best to reply in the shortest possible time.

Enjoy!!!

Course Curriculum

Chapter 1: Introduction

Lecture 1: Welcome

Lecture 2: General Introduction

Lecture 3: About this Course

Lecture 4: Link to code

Chapter 2: Theoritical background

Lecture 1: Haar Cascades and Histogram of gradients

Lecture 2: Convolutional Neural Networks

Lecture 3: RCNN,FastRCNN, FaterRCNN

Chapter 3: Single Stage Algorithms

Lecture 1: Understanding YOLO (You Only look once)

Lecture 2: Understanding YOLOX

Chapter 4: Dataset Preparation

Lecture 1: Pascal VOC dataset

Lecture 2: Preparing a custom dataset with Remo

Lecture 3: Preparing a custom dataset with LabelImg

Lecture 4: Assignment

Chapter 5: Finetuning and Testing

Lecture 1: Testing and FInetuning on Custom Dataset

Lecture 2: Wandb integration

Lecture 3: Running inference on Onnx model

Lecture 4: Assignment

Chapter 6: Deployment

Lecture 1: Understanding how APIs work

Lecture 2: Building an API with Fastapi

Lecture 3: Deploying on heroku

Lecture 4: Load testing with Locust

Lecture 5: Integration with C++

Lecture 6: Assignment

Instructors

Neuralearn Dot AI
Helping millions of learners, master Deep Learning.

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