Yolo V5: Label, Train And Test

Train & test YOLO v5 object detector with your own-custom data and by few code lines only: CPU & GPU

What you'll learn

Train YOLO v5 with few code lines

Label own dataset in YOLO format

Create custom dataset in YOLO format

Test YOLO v5 on image, video and by camera

Requirements

Basics of Python v3

Basics on how to work with Anaconda Environments

Basics on how to work with terminal window or Anaconda Prompt

Basics on how to work with Jupyter Notebook

Basics of Object Detection algorithms

Description

In this completely practical course, you'll train your own object detector by YOLO v5 as the state-of-the-art algorithm.As for the quick start, you’ll test already trained YOLO v5 to detect objects on image, video and in real time by camera.After that, you’ll label your own dataset in YOLO format and create custom dataset from huge existing one.Next, you’ll train YOLO v5 in local machine as well as in cloud machine.Then, you’ll test YOLO v5 detector that was trained on your own data.As for the bonus part, you’ll pass practice test and plan your next steps.All the code templates can be modified and applied in your future work. The course can supplement your own project that you can represent as the results to your supervisor, or to make a presentation in front of classmates, or even mention it in your resume.Content OrganizationEach Section of the course contains:Video lecturesCode templates and coding activitiesQuizzesDownloadable instructionsDiscussion opportunitiesSMART lecturesVideo lectures of the course have SMART objectives:S - specific (the lecture has specific objectives)M - measurable (results are reasonable and can be quantified)A - attainable (the lecture has clear steps to achieve the objectives)R - result-oriented (results can be obtained by the end of the lecture)T - time-oriented (results can be obtained within the visible time frame)Principle questionsWhat pain point, need, or desire is addressed in the course?The course solves the student’s pain point who want to use YOLO v5 algorithm with his/her custom data for object detection but don't know where to start.What is the prior knowledge that student has to have before starting the course?The student has written good amount of the code in Python. May or may not already have some practice of implementing object detection algorithms (good to have but not obligatory).Who is the course for?Student who studies computer vision and:wants to use YOLO v5 for object detection;wants to train YOLO v5 with completely new data;wants to label own data in YOLO format;wants to convert existing data in YOLO format;wants to test YOLO v5 on image, video and by camera.What are the aspirations for taking the course?The student's aspirations are:to build complete application for object detection with YOLO v5;to write scientific paper about different approaches for object detection;to accomplish final project about object detection that he/she might doing now;to improve his/her hard skills in object detection with YOLO v5 before the next interview for the internship or dream job.What will I be able to do at the end of the course?At the end of the course, you will be able to:apply trained YOLO v5 to detect objects on image, video and in real time by camera;label own dataset and structure files in YOLO format;create custom dataset in YOLO format;convert existing dataset of traffic signs in YOLO format;train YOLO v5 detector with custom data and few lines of the code;train and test both: in local machine and in cloud machine.

Overview

Section 1: You are welcome to the course

Lecture 1 Interview with international students

Lecture 2 Introduction to the course

Lecture 3 Quick Start: Test already trained YOLO v5

Lecture 4 Manage conda environments

Lecture 5 Set up Jupyter Notebook

Lecture 6 How to study the course?

Lecture 7 Outro & key takeaways

Section 2: Label your own dataset in YOLO format

Lecture 8 Intro & objectives: labelling

Lecture 9 What is YOLO format?

Lecture 10 Install labelling toolkit

Lecture 11 Label objects on image

Lecture 12 How to label video?

Lecture 13 Outro & key takeaways: labelling

Section 3: Create custom dataset in YOLO format

Lecture 14 Intro & objectives: custom dataset

Lecture 15 Install downloading toolkit

Lecture 16 Create custom dataset

Lecture 17 Convert custom dataset in YOLO

Lecture 18 Traffic Signs dataset in YOLO

Lecture 19 Outro & key takeaways: custom dataset

Section 4: Train YOLO v5 locally

Lecture 20 Intro & objectives: training locally

Lecture 21 Arrange dataset files

Lecture 22 Customize configuration file

Lecture 23 Install visualization toolkit

Lecture 24 Train YOLO v5 locally

Lecture 25 Outro & key takeaways: training locally

Section 5: Train YOLO v5 cloudy

Lecture 26 Intro & objectives: training cloudy

Lecture 27 Train YOLO v5 in Colaboratory

Lecture 28 Outro & key takeaways: training cloudy

Section 6: Test YOLO v5

Lecture 29 Intro & objectives: testing

Lecture 30 Test YOLO v5 locally

Lecture 31 Test YOLO v5 in Colaboratory

Lecture 32 Outro & key takeaways: testing

Section 7: Practice Test

Lecture 33 Recall all the learned skills

Bachelor students, Master students, Postgraduates and young researchers in the field of Information Technology and Computer Science,Students who want to know how to train YOLO v5 with new data,Students who want to label own data in YOLO format,Students who want to convert existing data in YOLO format