Computer Vision- YOLO Custom Object Detection with Colab GPU

Computer Vision: YOLO Custom Object Detection with Colab GPU, available at $69.99, has an average rating of 4.45, with 48 lectures, based on 271 reviews, and has 3775 subscribers.

You will learn about Python based YOLO Object Detection using Pre-trained Dataset Models as well as Custom Trained Dataset Models. Case study of coronavirus detector using YOLO YOLO Custom Training YOLO V4 Object Detection YOLO V4 Object Recognition This course is ideal for individuals who are Beginners or who those wants to start with Python based Object Recognition and want to develop custom object detection models It is particularly useful for Beginners or who those wants to start with Python based Object Recognition and want to develop custom object detection models.

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

Summary

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

Price: $69.99

Average Rating: 4.45

Number of Lectures: 48

Number of Published Lectures: 48

Number of Curriculum Items: 48

Number of Published Curriculum Objects: 48

Original Price: $109.99

Quality Status: approved

Status: Live

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

YOLO Custom Training

YOLO V4 Object Detection

YOLO V4 Object Recognition

Who Should Attend

Beginners or who those wants to start with Python based Object Recognition and want to develop custom object detection models

Target Audiences

Beginners or who those wants to start with Python based Object Recognition and want to develop custom object detection models

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.

Course Curriculum

Chapter 1: Course Introduction and Table of Contents

Lecture 1: Course Introduction and Table of Contents

Chapter 2: Introduction to YOLO Object Detection

Lecture 1: Introduction to YOLO Object Detection

Chapter 3: Environment Setup – Installing Anaconda

Lecture 1: Environment Setup – Installing Anaconda

Chapter 4: Python Basics (Optional)

Lecture 1: Python Basics – Assignment

Lecture 2: Python Basics – Flow Control

Lecture 3: Python Basics – Data Structures

Lecture 4: Python Basics – Functions

Chapter 5: Installing OpenCV Library

Lecture 1: Installing OpenCV Library

Chapter 6: Introduction to CNN – Theory Session

Lecture 1: Introduction to CNN

Chapter 7: YOLO Pre-trained Object Detection From Image

Lecture 1: YOLO Pre-trained Object Detection From Image – Part 1

Lecture 2: YOLO Pre-trained Object Detection From Image – Part 2

Lecture 3: YOLO Pre-trained Object Detection From Image – Part 3

Lecture 4: Error: invalid index to scalar variable

Lecture 5: YOLO Pre-trained Object Detection From Image – Part 4

Chapter 8: YOLO Pre-trained Object Detection From Image – NMS

Lecture 1: YOLO Pre-trained Object Detection From Image – NMS – Part 1

Lecture 2: YOLO Pre-trained Object Detection From Image – NMS – Part 2

Chapter 9: YOLO Pre-trained Object Detection From Realtime Webcam Video

Lecture 1: YOLO Pre-trained Object Detection From Real-time Webcam Video

Chapter 10: YOLO Pre-trained Object Detection From Pre-saved Video

Lecture 1: YOLO Pre-trained Object Detection From Pre-saved Video

Chapter 11: Introduction to Custom Trained YOLO Model

Lecture 1: Introduction to Custom Trained YOLO Model

Chapter 12: YOLOv4 Custom Training Phase 1 – Preparing Darknet

Lecture 1: Yolov4 Custom Training Phase 1 – Yolov4 Introduction and Downloading Weights

Lecture 2: Darknet Yolo v4 Weights and Config Download Links

Lecture 3: YOLOv4 Custom Training Phase 1 – Yolov4 Preparing Darknet

Chapter 13: YOLOv4 Custom Training Phase 2 – Data Collection

Lecture 1: YOLOv4 Custom Training Phase 2 – Data Collection – Part 1

Lecture 2: YOLOv4 Custom Training Phase 2 – Data Collection – Part 2

Chapter 14: YOLOv4 Custom Training Phase 2 – Image Labelling

Lecture 1: YOLOv4 Custom Training Phase 2 – Image Labelling – Part 1

Lecture 2: Installing LabelIM – The Easy Way

Lecture 3: YOLOv4 Custom Training Phase 2 – Image Labelling – Part 2

Chapter 15: YOLOv4 Custom Training Phase 2 – Train Test Split

Lecture 1: YOLOv4 Custom Training Phase 2 – Train Test Split

Chapter 16: YOLOv4 Custom Training Phase 2 – Data Preparation

Lecture 1: YOLOv4 Custom Training Phase 2 – Data Preparation – Part 1

Lecture 2: YOLOv4 Custom Training Phase 2 – Data Preparation – Part 2

Chapter 17: YOLOv4 Custom Training Phase 3 – Data Sync

Lecture 1: YOLOv4 Custom Training Phase 3 – Preparing Files Sync to Drive

Lecture 2: YOLOv4 Custom Training Phase 3 – Connecting Colab and Drive

Chapter 18: YOLOv4 Custom Training Phase 4 – Compile and Test Darknet

Lecture 1: YOLOV4 Custom Training Phase 4 – Compile and Test Darknet – Part 1

Lecture 2: YOLOV4 Custom Training Phase 4 – Compile and Test Darknet – Part 2

Lecture 3: YOLOV4 Custom Training Phase 4 – Compile and Test Darknet – Part 3

Chapter 19: YOLO Custom Training Phase 5 – Start Training and Monitoring

Lecture 1: YOLOv4 Custom Training Phase 5 – Chart and Training Progress Analysis

Chapter 20: YOLOv4 Custom Training Phase 5 – Finalizing Training Download Weights

Lecture 1: NB: The Output log from colab may seems not progressing. But dont worry !

Lecture 2: NB: Error Saving Weights file to /backup folder during training

Lecture 3: YOLOv4 Custom Training Phase 5 – Finalizing Training Download Weights

Chapter 21: Colab GPU Usage Limit Issue

Lecture 1: Colab GPU Usage Limit Issue

Chapter 22: OpenCV upgrade for YOLOv4

Lecture 1: OpenCV upgrade for YOLOv4

Chapter 23: YOLOv4 Pretrained Object Recognition from Image and Video

Lecture 1: YOLOv4 Pretrained Object Recognition from Image and Video

Chapter 24: YOLOv4 Custom Coronavirus Detection from Image

Lecture 1: YOLOv4 Custom Coronavirus Detection from Image

Chapter 25: YOLOv4 Custom Coronavirus Detection from Video

Lecture 1: YOLOv4 Custom Coronavirus Detection from Video

Chapter 26: Other Sample Realworld Case Studies

Lecture 1: Other Sample Realworld Case Studies

Lecture 2: Python code change for a 3 objects sample

Chapter 27: SOURCE CODE AND FILES ATTACHED

Lecture 1: custom_cov_model folder with colab notebook file shared link

Lecture 2: ALL SOURCE CODE AND FILES ATTACHED

Instructors

Abhilash Nelson
Computer Engineering Master & Senior Programmer at Dubai

Rating Distribution

1 stars: 4 votes

2 stars: 1 votes

3 stars: 34 votes

4 stars: 98 votes

5 stars: 134 votes

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