Object Recognition with Deep Learning using OpenCV and C#

Udemy
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Object Recognition with Deep Learning using OpenCV and C# Building Object Detection and Classification Applications using Computer Vision, Deep Learning, OpenCV and C#

What you’ll learn

  • Object Classification and Detection from images using Deep Learning, Computer Vision and C# Programming Language

Requirements

  • Some C# programming Skills
  • My Computer Vision Fundamentals with OpenCV and C# course

Description

This course is the first course in its field using C# programming language and OpenCV Deep Learning Module to teach you to develop object detection or recognition applications using Deep learning techniques

This course is going to teach you

  • How to develop Object Recognition Applications from images ( either static or webcam/video)  using popular computer vision library OpenCV, its Deep learning Module and C# Programming Language
  • Overview of how Neural Networks detect objects from images
  • Overview of   what Convolutional Neural Networks mean and how they are trained to detect objects from images
  • Overview of ImageNet image recognition competition, base image detection frameworks ( VGG, SSD, GoogLeNet)
  • Types of object detection frameworks such as YOLO, Caffe, DarkNet
  • You will be able to develop object classification and detection using Caffe and DarkNet framework models:
    • We’ll be creating an object classification application using Caffe framework and GoogleNet base’s framework that can distinguish between 1000 objects.
    • We’ll be creating an object detection application using MobileSSD base and Caffe framework which can detect and label objects if they belong to any of the 80 different classes of objects and their probability of match.
    • We’ll be converting the object detection application to an app which can work on webcam images  or video streams in real-time(To be uploaded by next week)
    • We’ll be using DarkNet framework and YOLO(You Only Look Once) algorithm  v2 to detect images in real-time for faster results(To be uploaded by next week)
  • Enjoy the course!

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