Computer Vision: YOLO Custom Object Detection with Colab GPU
Video: .MP4, 1280x720 30 fps | Audio: AAC, 44.1 kHz, 2ch | Duration: 04:12:21
Genre: eLearning | Language: English | Size: 2.65 GB

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YOLO: Pre-Trained Coco Dataset and Custom Trained Coronavirus Object Detection Model with Google Colab GPU Training What you'll learn
Python based YOLO Object Detection using Pre-trained Dataset Models as well as Custom Trained Dataset Models. Case study of coronavirus detector using YOLO

Requirements
A decent configuration computer (preferably Windows) and an enthusiasm to dive into the world Image and Object Recognition using Python
Description
Hi There!
welcome to my new course 'YOLO Custom Object Detection Quick Starter with Python'. This is the fourth course from my Computer Vision series.
As you know Object Detection is the most used applications of Computer Vision, in which the computer will be able to recognize and classify objects inside an image.
We will be specifically focusing on (YOLO), You only look once which is an effective real-time object recognition algorithm which is featured in Darknet, an open source neural network framework
This course is equally divided into two halves. The first half will deal with object recognition using a predefined dataset called the coco dataset which can classify 80 classes of objects. And the second half we will try to create our own custom dataset and train the YOLO model. We will try to create our own coronavirus detection model.
Let's now see the list of interesting topics that are included in this course.
At first we will have an introductory theory session about YOLO Object Detection system.
After that, we are ready to proceed with preparing our computer for python coding by downloading and installing the anaconda package and will check and see if everything is installed fine.
Most of you may not be coming from a python based programming background. The next few sessions and examples will help you get the basic python programming skill to proceed with the sessions included in this course. The topics include Python assignment, flow-control, functions and data structures.
Then we will install install OpenCV, which is the Open Source Computer Vision library in Python.
Then we will have an introduction to Convolutional Neural Networks , its working and the different steps involved.
Now we will proceed with the part 1 that involves Object Detection and Recognition using YOLO pre-trained model. we will have an overview about the yolo model in the next session and then we will implement yolo object detection from a single image.
Often YOLO gives back more than one successful detection for a single object in an image. This can be fixed using
a technique called as NMS or Non Maxima Suppression. We will implement that in our next session.
And using that as the base, we will try the yolo model for object detection from a real time webcam video and we will check the performance. Later we will use it for object recognition from the pre-saved video file.
Then we will proceed with part 2 of the course in which we will attempt to train a darknet YOLO model. A model which can detect coronavirus from an electron microscope image or video output.
Before we proceed with the implementation, we will discuss the pros and con