Train And Deploy Yolox Object Detection Models To The Cloud

Published 1/2023
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz
Language: English | Size: 3.40 GB | Duration: 6h 43m
Finetuning and testing a YOLOX model on custom built dataset. Creating and deploying object detection API to cloud

What you’ll 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++
Requirements
Basic Knowledge of Python
Access to an internet connection, as we shall be using Google Colab (free version)
Description
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 HaarcascadesDeep Learning algorithms like Convolutional neural networks, YOLO and YOLOXObject detection labeling formats like Pascal VOC.Creation of a custom dataset with RemoConversion of our custom dataset to the Pascal VOC format.Finetuning and testing YOLOX model with custom datasetConversion of finetuned model to Onnx formatExperiment tracking with WandbHow APIs work and building your own API with FastapiDeploying an API to the CloudLoad testing a deployed API with LocustRunning 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.YOU’LL ALSO GET:Lifetime access to This CourseFriendly and Prompt support in the Q&A sectionUdemy Certificate of Completion available for download30-day money back guaranteeEnjoy!!!
Overview
Section 1: Introduction
Lecture 1 Welcome
Lecture 2 General Introduction
Lecture 3 About this Course
Lecture 4 Link to code
Section 2: Theoritical background
Lecture 5 Haar Cascades and Histogram of gradients
Lecture 6 Convolutional Neural Networks
Lecture 7 RCNN,FastRCNN, FaterRCNN
Section 3: Single Stage Algorithms
Lecture 8 Understanding YOLO (You Only look once)
Lecture 9 Understanding YOLOX
Section 4: Dataset Preparation
Lecture 10 Pascal VOC dataset
Lecture 11 Preparing a custom dataset with Remo
Lecture 12 Assignment
Section 5: Finetuning and Testing
Lecture 13 Testing and FInetuning on Custom Dataset
Lecture 14 Wandb integration
Lecture 15 Running inference on Onnx model
Lecture 16 Assignment
Section 6: Deployment
Lecture 17 Understanding how APIs work
Lecture 18 Building an API with Fastapi
Lecture 19 Deploying on heroku
Lecture 20 Load testing with Locust
Lecture 21 Integration with C++
Lecture 22 Assignment
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

Homepage

https://www.udemy.com/course/train-and-deploy-yolox-object-detection-models-to-the-cloud/