Published 11/2023
Created by Valentyn Sichkar
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English | Duration: 33 Lectures ( 5h 7m ) | Size: 9.3 GB
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.
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