Deep Learning Object Detection by Training & Deploying YOLOX

Finetuning and testing a YOLOX model on custom built dataset. Creating and deploying object detection API to cloud

Ratings: 4.43 / 5.00




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 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!!!


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