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English

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Data Science

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Computer Vision: YOLO Custom Object Detection with Colab GPU

YOLO: Pre-Trained Coco Dataset and Custom Trained Coronavirus Object Detection Model with Google Colab GPU Training

4.42 (117 reviews)

Students

4 hours

Content

Jul 2021

Last Update
Regular Price

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What you will 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


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 cons of using a pre-trained dataset model and a custom dataset trained model. Also about the free GPU offered by google colab and its features.


In the next session we will start with phase 1 of our custom model in which we will do the preparation steps to implement custom model. We will at first download the darknet source from github and prepare it. We will then download the weight files required for both testing and training. And then we will edit the required configurations files to make it ready for our custom coronavirus detector.


In the second phase for our custom model, we will start collecting the required data to train the model. We will collect coronavirus images from the internet as much as we could and organize them into folder. Then we will label or annotate the coronavirus object inside these images using an opensource annotation tool called labelImg. Then we will split the gathered dataset, 80% for training and 20% for testing. And finally will edit the prepare the files with the location of training and testing datasets.


Now that we have all our files ready, in our third  phase, we will zip and upload them into google drive. After that we will create a google colab notebook and configure the colab runtime to use the fast, powerful, yet free GPU service provided by google. Then we will mount our google drive to our colab runtime and unzip the darknet zip we uploaded.


Sometimes files edited in non unix environments may be having problems when compiling the darknet. We have to convert the encoding from dos to unix as our next step. Then we will complile the darknet framework source code and proceed with testing the darknet framework with a sample image in our fourth phase.


The free GPU based runtime provided by google colab is volatile. It will get reset every 12 hours. So we need to save our weights periodically during training to our google drive which is a permanent storage. So in our phase five, we will link a backup folder in google drive to the colab runtime.


Finally in our phase 6, we are ready to proceed with training our custom coronavirus model. We will keep on monitoring the loss for every iteration or epoch as we call it in nerual network terms. Our model will automatically save the weights every 100th epoch securely to our google drive backup folder.


We can see a continues decrease in the loss values as we go through the epoch. And after many number of iterations, our model will come into a convergence or flatline state in which there is no further improvement in loss. at that time we will obtain a final weight


Later we will use that weight to do prediction for an image that contains coronavirus in it. We can see that our model clearly detects objects. We will even try this with a video file also.


We cannot claim that its a fully fledged flawless production ready coronavirus detection model. There is still room for improvement. But anyway, by building this custom model, we came all the way through the steps and process of making a custom yolo model which will be a great and valuable experience for you.


And then later in a quick session, we will also discuss few other case studies in which we can implement a custom trained YOLO model, the changes we may  need to make for training those models etc.

That's all about the topics which are currently included in this quick course. The code, images and weights used in this course has been uploaded and shared in a folder. I will include the link to download them in the last session or the resource section of this course. You are free to use the code in your projects with no questions asked.


Also after completing this course, you will be provided with a course completion certificate which will add value to your portfolio.


So that's all for now, see you soon in the class room. Happy learning and have a great time.


Screenshots

Computer Vision: YOLO Custom Object Detection with Colab GPU
Computer Vision: YOLO Custom Object Detection with Colab GPU
Computer Vision: YOLO Custom Object Detection with Colab GPU
Computer Vision: YOLO Custom Object Detection with Colab GPU

Content

Course Introduction and Table of Contents

Course Introduction and Table of Contents

Introduction to YOLO Object Detection

Introduction to YOLO Object Detection

Environment Setup - Installing Anaconda

Environment Setup - Installing Anaconda

Python Basics (Optional)

Python Basics - Assignment

Python Basics - Flow Control

Python Basics - Data Structures

Python Basics - Functions

Installing OpenCV Library

Installing OpenCV Library

Introduction to CNN - Theory Session

Introduction to CNN

YOLO Pre-trained Object Detection From Image

YOLO Pre-trained Object Detection From Image - Part 1

YOLO Pre-trained Object Detection From Image - Part 2

YOLO Pre-trained Object Detection From Image - Part 3

YOLO Pre-trained Object Detection From Image - Part 4

YOLO Pre-trained Object Detection From Image - NMS

YOLO Pre-trained Object Detection From Image - NMS - Part 1

YOLO Pre-trained Object Detection From Image - NMS - Part 2

YOLO Pre-trained Object Detection From Realtime Webcam Video

YOLO Pre-trained Object Detection From Real-time Webcam Video

YOLO Pre-trained Object Detection From Pre-saved Video

YOLO Pre-trained Object Detection From Pre-saved Video

Introduction to Custom Trained YOLO Model

Introduction to Custom Trained YOLO Model

YOLO Custom Training Phase 1 - Preparing Darknet

YOLO Custom Training Phase 1 - Preparing Darknet - Part 1

YOLO Custom Training Phase 1 - Preparing Darknet - Part 2

YOLO Custom Training Phase 1 - Preparing Darknet - Part 3

YOLO Custom Training Phase 2 - Data Collection

YOLO Custom Training Phase 2 - Data Collection - Part 1

YOLO Custom Training Phase 2 - Data Collection - Part 2

YOLO Custom Training Phase 2 - Image Labeling

YOLO Custom Training Phase 2 - Image Labeling - Part 1

YOLO Custom Training Phase 2 - Image Labeling - Part 2

YOLO Custom Training Phase 2 - Train Test Split

YOLO Custom Training Phase 2 - Train Test Split

YOLO Custom Training Phase 2 - Data Preparation

YOLO Custom Training Phase 2 - Data Preparation - Part 1

YOLO Custom Training Phase 2 - Data Preparation - Part 2

YOLO Custom Training Phase 3 - Data Sync

YOLO Custom Training Phase 3 - Data Sync - Part 1

YOLO Custom Training Phase 3 - Data Sync - Part 2

YOLO Custom Training Phase 4 - Compile and Test Darknet

YOLO Custom Training Phase 4 - Compile and Test Darknet - Part 1

YOLO Custom Training Phase 4 - Compile and Test Darknet - Part 2

YOLO Custom Training Phase 4 - Compile and Test Darknet - Part 3

YOLO Custom Training Phase 5 - Create Symbolic Link to Drive

YOLO Custom Training Phase 5 - Create Symbolic Link to Drive

YOLO Custom Training Phase 6 - Start Training

YOLO Custom Training Phase 6 - Start Training - part 1

YOLO Custom Training Phase 6 - Start Training - part 2

YOLO Custom Training Phase 6 - Start Training - part 3

YOLO Custom Training Phase 6 - Resume Training and Save Model - Part 4

YOLO Custom Corona virus Detection from Image

YOLO Custom Corona virus Detection from Image

YOLO Custom Corona virus Detection from Video

YOLO Custom Corona virus Detection from Video

Other Real-world Case Studies and Scenarios

Other Real-world Case Studies and Scenarios

SOURCE CODE AND FILES ATTACHED

SOURCE CODE AND FILES ATTACHED


Reviews

P
Puranam4 July 2021

Excellent. A lot of content in very less time, explanation is so good. Can learn a lot in limited time from the instructor. thanks a lot for giving practical guidance as well apart from delivering a beautiful course.

K
Kaleb3 June 2021

It would have been nice if the course went over how to train on your own computer in addition to using Colab. The course is mostly telling you how to get yolo working and doesn't really teach in a way that provides deep understanding. It is still a good resource for those just getting started. I feel competent using YOLO after taking this course.

G
Georgios3 June 2021

Very good thus far only in some parts of the code some additional comments may come in handy some a

Z
Zied5 February 2021

it's good, but very fast, many mistakes corrected by forms in the video make us lost. But in general it's not bad

P
Parsa4 December 2020

I benefited a lot from this course. It helped me in understanding YOLOV4 implementation and application areas for this Detection algorithm. The instructor is such a talented person and makes the content very simple to understand and clarifies all the doubts of the students. Looking forward to more courses from the Instructor on medical Image semantic segmentation and related fields.

B
Berk7 October 2020

I really recommend this course who wants to start learning object detection with YOLO. Lectures are quite simple and understandable and lecturer is helpful with your questions !

S
Suvankar13 September 2020

Well connected from pre-trained model to custom training. In Linux system labellmg only provides .xml file and there is nothing for yolo. How to convert those things to yolo format that is missing. In your face recognition course you clearly mentioned for every OS but here it is missing. You may also provide how to use other GPU like AWS for training different kind of models like FRCNN. Otherwise course is superb.

S
Swarnava28 August 2020

This is a very to the point course, if you want to know how to implement YOLO on custom object detection, it definitely is the course here on Udemy.


Coupons

DateDiscountStatus
6/21/2020100% OFFExpired

3129498

Udemy ID

5/15/2020

Course created date

6/21/2020

Course Indexed date
Angelcrc Seven
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