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.
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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.
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After that, you’ll label individual dataset as well as create custom one by extracting needed images from huge existing dataset.
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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.
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When datasets are ready, you’ll train and test YOLO v3-v4 detectors in Darknet framework.
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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:
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Video Lectures
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Coding Activities
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Code Templates
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Quizzes
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Downloadable Instructions
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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: Let's 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
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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
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