Object Recognition with Deep Learning using OpenCV and C#
Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: aac, 48000 Hz
Language: English | VTT | Size: 870 MB | Duration: 1h 21m
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!
Who this course is for:
C# developers wanting to develop Deep Learning Computer Vision applications
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