Train YOLO for Object Detection with Custom Data

Train YOLO for Object Detection with Custom Data, available at $84.99, has an average rating of 4.6, with 60 lectures, 6 quizzes, based on 1191 reviews, and has 6730 subscribers.

You will learn about Apply already trained YOLO v3-v4 for Object Detection on image, video and in real time with camera Label own dataset and structure files in YOLO format Train YOLO v3-v4 detector in Darknet framework Assemble custom dataset in YOLO format Convert existing dataset of Traffic Signs in YOLO format Build individual PyQt graphical user interface for Object Detection based on YOLO v3-v4 algorithm This course is ideal for individuals who are Students who study Computer Vision and want to know how to use YOLO for Object Detection or Students who know basics of Object Detection but want to know how to Train YOLO with New Data or Students who study YOLO and want to Label Own Data in YOLO format or Students who use already existing datasets for Object Detection but want to Convert them in YOLO format or Young Researchers who study different Object Detection Algorithms and want to Train YOLO with Custom Data and Compare results with different approaches It is particularly useful for Students who study Computer Vision and want to know how to use YOLO for Object Detection or Students who know basics of Object Detection but want to know how to Train YOLO with New Data or Students who study YOLO and want to Label Own Data in YOLO format or Students who use already existing datasets for Object Detection but want to Convert them in YOLO format or Young Researchers who study different Object Detection Algorithms and want to Train YOLO with Custom Data and Compare results with different approaches.

Enroll now: Train YOLO for Object Detection with Custom Data

Summary

Title: Train YOLO for Object Detection with Custom Data

Price: $84.99

Average Rating: 4.6

Number of Lectures: 60

Number of Quizzes: 6

Number of Published Lectures: 59

Number of Published Quizzes: 6

Number of Curriculum Items: 66

Number of Published Curriculum Objects: 65

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will Learn

Apply already trained YOLO v3-v4 for Object Detection on image, video and in real time with camera

Label own dataset and structure files in YOLO format

Train YOLO v3-v4 detector in Darknet framework

Assemble custom dataset in YOLO format

Convert existing dataset of Traffic Signs in YOLO format

Build individual PyQt graphical user interface for Object Detection based on YOLO v3-v4 algorithm

Who Should Attend

Students who study Computer Vision and want to know how to use YOLO for Object Detection

Students who know basics of Object Detection but want to know how to Train YOLO with New Data

Students who study YOLO and want to Label Own Data in YOLO format

Students who use already existing datasets for Object Detection but want to Convert them in YOLO format

Young Researchers who study different Object Detection Algorithms and want to Train YOLO with Custom Data and Compare results with different approaches

Target Audiences

Students who study Computer Vision and want to know how to use YOLO for Object Detection

Students who know basics of Object Detection but want to know how to Train YOLO with New Data

Students who study YOLO and want to Label Own Data in YOLO format

Students who use already existing datasets for Object Detection but want to Convert them in YOLO format

Young Researchers who study different Object Detection Algorithms and want to Train YOLO with Custom Data and Compare results with different approaches

In this hands-on course, you’ll train your own Object Detector using YOLO v3-v4 algorithms.

1. As for beginning, you’ll implement already trained YOLO v3-v4 on COCO dataset. You’ll detect objects on image, video and in real time by OpenCV deep learning library. The code templates you can integrate later in your own future projects and use them for your own trained YOLO detectors.

2. After that, you’ll label individual dataset as well as create custom one by extracting needed images from huge existing dataset.

3. Next, you’ll convert Traffic Signs dataset into YOLO format. Code templates for converting you can modify and apply for other datasets in your future work.

4. When datasets are ready, you’ll train and test YOLO v3-v4 detectors in Darknet framework.

5. As for Bonus part, you’ll build graphical user interface for Object Detection by YOLO and by the help of PyQt. This project you can represent as your results to your supervisor or to make a presentation in front of classmates or even mention it in your resume.

Content Organization. Each Section of the course contains:

Video Lectures

Coding Activities

Code Templates

Quizzes

Downloadable Instructions

Discussion Opportunities

Video 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)

Course Curriculum

Chapter 1: Welcome

Lecture 1: Introduction to the course

Lecture 2: Quick Win – Step 1: Simple Object Detection by thresholding with mask

Lecture 3: Quick Win – Step 2: Simple Object Detection by thresholding with mask

Lecture 4: Activity: Lets get acquainted

Lecture 5: Installing Miniconda, Python, PyCharm, OpenCV

Chapter 2: Objects Detection with YOLO v3-v4

Lecture 1: Introduction: What are the differences between approaches?

Lecture 2: Objects Detection on Image with YOLO v3 and OpenCV

Lecture 3: Activity: Detect Objects on this image

Lecture 4: Objects Detection on Video with YOLO v3 and OpenCV

Lecture 5: Activity: Detect Objects on this video

Lecture 6: Objects Detection in Real Time with YOLO v3 and OpenCV

Lecture 7: Conclusion: key takeaways for Objects Detection with dnn library in OpenCV

Lecture 8: YOLO v4: instructions

Chapter 3: Labelling new dataset in YOLO format

Lecture 1: Introduction: Data Annotation

Lecture 2: How does labelled image in YOLO format looks like?

Lecture 3: Useful resources for labelling

Lecture 4: Labelling image in YOLO format

Lecture 5: Activity: Label Objects on this image

Lecture 6: Labelling video in YOLO format

Lecture 7: Activity: Label Objects on this video

Lecture 8: Preparing files for training

Lecture 9: Conclusion: key takeaways for labelling data in YOLO format

Lecture 10: YOLO v4: instructions

Chapter 4: Creating custom dataset in YOLO format

Lecture 1: Introduction: How to create custom dataset?

Lecture 2: Toolkit for downloading images

Lecture 3: Downloading images from huge dataset

Lecture 4: Activity: Download Images for these classes

Lecture 5: Converting downloaded files to YOLO format

Lecture 6: Preparing files for training

Lecture 7: Joining datasets for training

Lecture 8: Conclusion: key takeaways for creating custom dataset and converting it to YOLO

Lecture 9: YOLO v4: instructions

Chapter 5: Converting Traffic Signs dataset in YOLO format

Lecture 1: Introduction: One more custom dataset to be converted

Lecture 2: Downloading Traffic Signs dataset

Lecture 3: Converting downloaded Traffic Signs dataset to YOLO format

Lecture 4: Preparing files for training

Lecture 5: Conclusion: key takeaways for converting Traffic Signs dataset in YOLO format

Lecture 6: YOLO v4: instructions

Chapter 6: Training YOLO v3-v4 in Darknet framework

Lecture 1: Introduction: What is Darknet framework?

Lecture 2: Installing Darknet

Lecture 3: Checking installation

Lecture 4: Preparing files for training

Lecture 5: Setting up configuration files

Lecture 6: Running training process

Lecture 7: When do we stop training?

Lecture 8: Activity: Test trained custom models on these images

Lecture 9: Activity: Test trained custom models on these videos

Lecture 10: Conclusion: key takeaways for training YOLO v3 in Darknet framework

Lecture 11: YOLO v4: instructions

Chapter 7: Building PyQt user interface for Objects Detection with YOLO v3-v4

Lecture 1: Congratulation word and recap of learned skills

Lecture 2: What is next?

Lecture 3: Installing PyQt for building user interface

Lecture 4: Creating PyQt interface

Lecture 5: Integrating YOLO v3 into PyQt interface

Lecture 6: Running experiments with PyQt interface for Objects Detection

Lecture 7: YOLO v4: instructions

Chapter 8: How does it work?

Lecture 1: How does YOLO v3 work?

Chapter 9: YOLO v4

Lecture 1: How to train YOLO v4: Instructions

Chapter 10: YOLO v5

Lecture 1: How to train YOLO v5?

Instructors

Valentyn Sichkar
Computer Vision, Machine Learning, Image Processing

Rating Distribution

1 stars: 30 votes

2 stars: 28 votes

3 stars: 100 votes

4 stars: 354 votes

5 stars: 679 votes

Frequently Asked Questions

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You can view and review the lecture materials indefinitely, like an on-demand channel.

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