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Perform Real-Time Object Detection with YOLOv3
Snehan Kekre
Snehan Kekre
In this 1-hour long project-based course, you will perform real-time object detection with YOLOv3: a state-of-the-art, real-time object detection system. Specifically, you will detect objects with the YOLO system using pre-trained models on a GPU-enabled...
Read more
In this 1-hour long project-based course, you will perform real-time object detection with YOLOv3: a state-of-the-art, real-time object detection system. Specifically, you will detect objects with the YOLO system using pre-trained models on a GPU-enabled...
Read more
In this 1-hour long project-based course, you will perform real-time object detection with YOLOv3: a state-of-the-art, real-time object detection system. Specifically, you will detect objects with the YOLO system using pre-trained models on a GPU-enabled workstation. To apply YOLO to videos and save the corresponding labelled videos, you will build a custom command-line application in Python that employs a pre-trained model to detect, localize, and classify objects. It will use OpenCV to read the video streams, draw bounding boxes around detected objects, label the objects along with confidence scores, and save the labelled videos to disk.
This course runs on Coursera's hands-on project platform called Rhyme. On Rhyme, you do projects in a hands-on manner in your browser. You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project. Everything is already set up directly in your internet browser so you can just focus on learning. For this project, you’ll get instant access to a cloud desktop with Python, Jupyter, and Keras pre-installed.
Notes:
- You will be able to access the cloud desktop 5 times. However, you will be able to access instructions videos as many times as you want.
- This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/ynr00w/perform
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Builds a strong foundation in using Python, Jupyter, and Keras for object detection
Provides foundational knowledge and skills in implementing YOLOv3 for real-time object detection
Develops expertise in object detection algorithms and their practical implementation
Focuses on practical application, with the creation of a custom command-line application for object detection
Features expert instruction by Snehan Kekre, renowned for his work in object detection
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Reviews summary
Quick hands-on with object detection
This mostly well-received 1-hour project is a brief introduction to real-time object detection using YOLOv3. It has a hands-on approach using a pre-configured cloud desktop where you will detect objects using pre-trained models and Python. Though it can be useful for a quick intro to YOLO, it could benefit from a more in-depth explanation and coverage of the wider objectives it outlines in the description.
Covers fundamentals of real-time object detection with YOLOv3
Uses a cloud desktop for hands-on project work
"You will get instant access to pre-configured cloud desktops containing all of the software and data you need for the project."
The course description may not accurately reflect the content
"The course description was misleading."
"There is a severe disconnect between the description of the project and the actual material."
Some users reported technical issues with the cloud desktop
"I wasn't able to run the code on my local machine."
May not provide enough depth for experienced learners
"This course expects you to have foreknowledge of what YOLO is and how is it run."
Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Perform Real-Time Object Detection with YOLOv3 with these
activities:
Review Python basics
Show steps
Reviewing Python basics will help you feel comfortable with the programming fundamentals that are used throughout this course.
Browse courses on
Python
Show steps
-
Read through an online Python tutorial or book
-
Create a simple Python program
-
Review Python data types and structures
Review YOLOv3
Show steps
Review of YOLOv3 will help you recall the foundational knowledge of the system being used in this course.
Browse courses on
Yolo
Show steps
-
Re-read the introduction section of the YOLOv3 paper
-
Go through the slides from a recent tutorial or talk on YOLOv3.
-
Implement a simple object detection script using YOLOv3.
-
Find and watch a video explaining the architecture of YOLOv3.
-
Take an online quiz or test to validate your understanding of YOLOv3.
Compile resources for the course
Show steps
Organize resources, assignments, notes, and quizzes to facilitate effective learning in this course on real-time object detection with YOLOv3.
Show steps
-
Gather lecture notes, readings, and assignments
-
Organize materials in a digital or physical filing system
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Practice using Python for object detection
Show steps
Reinforce your understanding of Python programming and its application in object detection, a critical skill for this course on real-time object detection with YOLOv3.
Browse courses on
Python
Show steps
-
Solve coding problems on platforms like LeetCode or HackerRank
-
Work through practice exercises provided in the course materials
Practice object detection with YOLOv3
Show steps
Practicing object detection with YOLOv3 will help you solidify your understanding of the algorithm and improve your skills in applying it to real-world problems.
Browse courses on
Object Detection
Show steps
-
Find a dataset with labeled objects
-
Train a YOLOv3 model on the dataset
-
Evaluate the model's performance
Join a study group or online forum
Show steps
Collaborate with fellow learners to reinforce concepts, share knowledge, and enhance your comprehension of object detection with YOLOv3.
Show steps
-
Join online forums or discussion groups related to the course
-
Form or join study groups with classmates to discuss concepts and assignments
Learn best practices for using YOLO in real-world projects
Show steps
This will expose you to the latest developments, techniques, and tricks being used by practitioners.
Show steps
-
Search for and enroll in a free or paid course or tutorial on using YOLO in real-world projects.
-
Follow along with the tutorials, completing all exercises and assignments.
-
Apply what you've learned to a personal project or by contributing to an open-source project.
-
Get feedback on your work from experienced practitioners through online forums or code review platforms.
Practice object detection with YOLOv3 on different datasets
Show steps
Developing proficiency in using YOLOv3 on various datasets will help you master the material taught in the course.
Show steps
-
Find various datasets for object detection, such as COCO, Pascal VOC, or ImageNet.
-
Set up your development environment with the necessary libraries and tools for object detection.
-
Train and evaluate YOLOv3 models on these datasets, experimenting with different hyperparameters and architectures.
-
Compare the performance of your models and identify areas for improvement.
-
Share your findings and insights on online forums or in a blog post.
Learn about advanced object detection techniques
Show steps
Expand your knowledge of object detection beyond the course content by exploring advanced techniques and algorithms, enhancing your understanding of the field.
Browse courses on
Object Detection
Show steps
-
Follow tutorials on platforms like Coursera, edX, or YouTube
-
Read research papers and articles on the latest advancements
Create a blog post on object detection with YOLOv3
Show steps
Creating a blog post on object detection with YOLOv3 will help you synthesize your knowledge of the topic and share it with a wider audience.
Browse courses on
Object Detection
Show steps
-
Choose a specific topic related to object detection with YOLOv3
-
Write a detailed outline of your blog post
-
Research and gather information from reputable sources
-
Write the first draft of your blog post
-
Edit and proofread your blog post
Contribute to open-source projects related to object detection
Show steps
Deepen your practical skills and gain valuable experience by contributing to real-world object detection projects, enriching your understanding of the field.
Browse courses on
Object Detection
Show steps
-
Identify open-source projects on platforms like GitHub or GitLab
-
Offer your expertise in coding, documentation, or testing
-
Collaborate with other developers and learn from their contributions
Create a custom object detection application using YOLOv3
Show steps
Hands-on experience in building an end-to-end object detection application will reinforce your understanding of the course material.
Show steps
-
Define the requirements and scope of your application.
-
Design the architecture and user interface of your application.
-
Implement the object detection functionality using YOLOv3.
-
Integrate additional features and functionality, such as image preprocessing, post-processing, and data visualization.
-
Test and evaluate your application, making necessary adjustments to improve its performance and accuracy.
Contribute to the YOLOv3 open-source project
Show steps
Contributing to the YOLOv3 project will deepen your understanding and give you hands-on experience with the latest developments.
Show steps
-
Find an issue or feature request that you would like to contribute to.
-
Fork the YOLOv3 repository and create a branch for your changes.
-
Implement your changes or fixes and write unit tests to validate your work.
-
Submit a pull request to the main YOLOv3 repository.
-
Respond to feedback and make necessary revisions to your pull request.
Mentor a beginner in object detection
Show steps
Mentoring a beginner in object detection will help you reinforce your knowledge of the topic and develop your leadership skills.
Browse courses on
Object Detection
Show steps
-
Find a beginner who is interested in learning about object detection
-
Set up regular meetings to provide guidance and support
-
Create resources and materials to help your mentee learn
-
Provide feedback and encouragement
Review Python basics
Show steps
Reviewing Python basics will help you feel comfortable with the programming fundamentals that are used throughout this course.
Browse courses on
Python
Show steps
Show steps
Show steps
- Read through an online Python tutorial or book
- Create a simple Python program
- Review Python data types and structures
Review YOLOv3
Show steps
Review of YOLOv3 will help you recall the foundational knowledge of the system being used in this course.
Browse courses on
Yolo
Show steps
Show steps
Show steps
- Re-read the introduction section of the YOLOv3 paper
- Go through the slides from a recent tutorial or talk on YOLOv3.
- Implement a simple object detection script using YOLOv3.
- Find and watch a video explaining the architecture of YOLOv3.
- Take an online quiz or test to validate your understanding of YOLOv3.
Compile resources for the course
Show steps
Organize resources, assignments, notes, and quizzes to facilitate effective learning in this course on real-time object detection with YOLOv3.
Show steps
Show steps
Show steps
- Gather lecture notes, readings, and assignments
- Organize materials in a digital or physical filing system
11 other activities
Expand to see all activities and additional details
Show all 14 activities
Practice using Python for object detection
Show steps
Reinforce your understanding of Python programming and its application in object detection, a critical skill for this course on real-time object detection with YOLOv3.
Browse courses on
Python
Show steps
Show steps
Show steps
- Solve coding problems on platforms like LeetCode or HackerRank
- Work through practice exercises provided in the course materials
Practice object detection with YOLOv3
Show steps
Practicing object detection with YOLOv3 will help you solidify your understanding of the algorithm and improve your skills in applying it to real-world problems.
Browse courses on
Object Detection
Show steps
Show steps
Show steps
- Find a dataset with labeled objects
- Train a YOLOv3 model on the dataset
- Evaluate the model's performance
Join a study group or online forum
Show steps
Collaborate with fellow learners to reinforce concepts, share knowledge, and enhance your comprehension of object detection with YOLOv3.
Show steps
Show steps
Show steps
- Join online forums or discussion groups related to the course
- Form or join study groups with classmates to discuss concepts and assignments
Learn best practices for using YOLO in real-world projects
Show steps
This will expose you to the latest developments, techniques, and tricks being used by practitioners.
Show steps
Show steps
Show steps
- Search for and enroll in a free or paid course or tutorial on using YOLO in real-world projects.
- Follow along with the tutorials, completing all exercises and assignments.
- Apply what you've learned to a personal project or by contributing to an open-source project.
- Get feedback on your work from experienced practitioners through online forums or code review platforms.
Practice object detection with YOLOv3 on different datasets
Show steps
Developing proficiency in using YOLOv3 on various datasets will help you master the material taught in the course.
Show steps
Show steps
Show steps
- Find various datasets for object detection, such as COCO, Pascal VOC, or ImageNet.
- Set up your development environment with the necessary libraries and tools for object detection.
- Train and evaluate YOLOv3 models on these datasets, experimenting with different hyperparameters and architectures.
- Compare the performance of your models and identify areas for improvement.
- Share your findings and insights on online forums or in a blog post.
Learn about advanced object detection techniques
Show steps
Expand your knowledge of object detection beyond the course content by exploring advanced techniques and algorithms, enhancing your understanding of the field.
Browse courses on
Object Detection
Show steps
Show steps
Show steps
- Follow tutorials on platforms like Coursera, edX, or YouTube
- Read research papers and articles on the latest advancements
Create a blog post on object detection with YOLOv3
Show steps
Creating a blog post on object detection with YOLOv3 will help you synthesize your knowledge of the topic and share it with a wider audience.
Browse courses on
Object Detection
Show steps
Show steps
Show steps
- Choose a specific topic related to object detection with YOLOv3
- Write a detailed outline of your blog post
- Research and gather information from reputable sources
- Write the first draft of your blog post
- Edit and proofread your blog post
Contribute to open-source projects related to object detection
Show steps
Deepen your practical skills and gain valuable experience by contributing to real-world object detection projects, enriching your understanding of the field.
Browse courses on
Object Detection
Show steps
Show steps
Show steps
- Identify open-source projects on platforms like GitHub or GitLab
- Offer your expertise in coding, documentation, or testing
- Collaborate with other developers and learn from their contributions
Create a custom object detection application using YOLOv3
Show steps
Hands-on experience in building an end-to-end object detection application will reinforce your understanding of the course material.
Show steps
Show steps
Show steps
- Define the requirements and scope of your application.
- Design the architecture and user interface of your application.
- Implement the object detection functionality using YOLOv3.
- Integrate additional features and functionality, such as image preprocessing, post-processing, and data visualization.
- Test and evaluate your application, making necessary adjustments to improve its performance and accuracy.
Contribute to the YOLOv3 open-source project
Show steps
Contributing to the YOLOv3 project will deepen your understanding and give you hands-on experience with the latest developments.
Show steps
Show steps
Show steps
- Find an issue or feature request that you would like to contribute to.
- Fork the YOLOv3 repository and create a branch for your changes.
- Implement your changes or fixes and write unit tests to validate your work.
- Submit a pull request to the main YOLOv3 repository.
- Respond to feedback and make necessary revisions to your pull request.
Mentor a beginner in object detection
Show steps
Mentoring a beginner in object detection will help you reinforce your knowledge of the topic and develop your leadership skills.
Browse courses on
Object Detection
Show steps
Show steps
Show steps
- Find a beginner who is interested in learning about object detection
- Set up regular meetings to provide guidance and support
- Create resources and materials to help your mentee learn
- Provide feedback and encouragement
Career center
Learners who complete Perform Real-Time Object Detection with YOLOv3 will develop knowledge and skills
that may be useful to these careers:
Artificial Intelligence Researcher
Artificial Intelligence Researcher
An Artificial Intelligence Researcher conducts research and develops new techniques in the field of artificial intelligence. This course can help Artificial Intelligence Researchers build a foundation in object detection and classification, which are key areas of research in artificial intelligence.
Computer Vision Scientist
Computer Vision Scientist
A Computer Vision Scientist conducts research and develops new techniques in the field of computer vision. This course can help Computer Vision Scientists build a foundation in object detection and classification, which are key areas of research in computer vision.
Machine Learning Scientist
Machine Learning Scientist
A Machine Learning Scientist conducts research and develops new techniques in the field of machine learning. This course can help Machine Learning Scientists build a foundation in object detection and classification, which are key areas of research in machine learning.
Computer Vision Researcher
Computer Vision Researcher
A Computer Vision Researcher conducts research in the field of computer vision. This course can help Computer Vision Researchers build a foundation in object detection and classification, which are key areas of research in computer vision.
Machine Learning Researcher
Machine Learning Researcher
A Machine Learning Researcher conducts research in the field of machine learning. This course can help Machine Learning Researchers build a foundation in object detection and classification, which are key areas of research in machine learning.
Computer Vision Consultant
Computer Vision Consultant
A Computer Vision Consultant provides consulting services to businesses on how to use computer vision to solve business problems. This course can help Computer Vision Consultants build a foundation in object detection and classification, which are key skills for providing consulting services in this field.
Machine Learning Consultant
Machine Learning Consultant
A Machine Learning Consultant provides consulting services to businesses on how to use machine learning to solve business problems. This course can help Machine Learning Consultants build a foundation in object detection and classification, which are key skills for providing consulting services in this field.
Machine Learning Engineer
Machine Learning Engineer
A Machine Learning Engineer uses machine learning techniques to solve real-time problems. This course can help build a foundation in object detection and classification, which are key skills for Machine Learning Engineers. The hands-on experience gained in this course will also be beneficial for those looking to enter this field.
Data Science Consultant
Data Science Consultant
A Data Science Consultant provides consulting services to businesses on how to use data science to solve business problems. This course can help Data Science Consultants build a foundation in object detection and classification, which are skills that can be used to provide consulting services in this field.
Robotics Engineer
Robotics Engineer
A Robotics Engineer designs and builds robots. This course can help Robotics Engineers build a foundation in object detection and classification, which are skills that can be used to develop robots that can interact with the real world.
Computer Vision Engineer
Computer Vision Engineer
A Computer Vision Engineer designs and builds computer vision systems, which are used for object recognition, tracking, and classification. This course can provide a foundation of the YOLOv3 algorithm used to build such systems. The hands-on experience gained in this course will be beneficial for those looking to enter this field.
Data Scientist
Data Scientist
A Data Scientist uses data to extract insights and create predictive models. This course can help build a foundation in object detection and classification, which are skills that can be used in data science projects. The hands-on experience gained in this course will also be beneficial for those looking to enter this field.
Software Engineer
Software Engineer
A Software Engineer designs and builds software applications. This course can help Software Engineers build a foundation in object detection and classification, which are skills that can be used to develop a variety of applications.
Research Scientist
Research Scientist
A Research Scientist conducts research in a specific field. This course can help Research Scientists build a foundation in object detection and classification, which are skills that can be used in a variety of research projects.
Data Analyst
Data Analyst
A Data Analyst analyzes data to identify trends and patterns. This course can help Data Analysts build a foundation in object detection and classification, which are skills that can be used to analyze data in a variety of fields.
For more career information including salaries, visit:
OpenCourser.com/course/ynr00w/perform
Reading list
We've selected ten books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Perform Real-Time Object Detection with YOLOv3.
Save
Provides a comprehensive overview of deep learning. It covers the theoretical foundations, implementation details, and applications of deep learning, including object detection.
Save
Provides a comprehensive overview of computer vision. It covers the theoretical foundations, algorithms, and applications of computer vision, including object detection.
Provides a comprehensive overview of computer vision. It covers the theoretical foundations, algorithms, and applications of computer vision, including object detection.
Provides a comprehensive overview of object detection and recognition in digital images. It covers the theoretical foundations, algorithms, and applications of object detection and recognition.
Provides a practical guide to object detection with deep learning. It covers the theoretical foundations, implementation details, and applications of deep learning models for object detection.
Provides a practical guide to artificial intelligence for object detection. It covers the theoretical foundations, implementation details, and applications of artificial intelligence models for object detection.
Introduces the fundamentals of object detection with deep learning models. It covers various object detection architectures, including YOLO, and provides hands-on experience with building and evaluating object detection models.
Provides a foundational overview of pattern recognition and machine learning. It covers the theoretical concepts, algorithms, and applications of pattern recognition and machine learning, including object detection.
Provides a foundational overview of machine learning for computer vision. It covers the theoretical concepts, algorithms, and applications of machine learning for computer vision, including object detection.
Provides a foundational overview of pattern recognition and image analysis. It covers the theoretical concepts, algorithms, and applications of pattern recognition and image analysis, including object detection.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ynr00w/perform
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Time
1 hours
Via
Coursera
Institutions
Rhyme
Coursera Project Network
Instructor
Snehan Kekre
Good to know
Builds a strong foundation in using Python, Jupyter, and Keras for object detection
Provides foundational knowledge and skills in implementing YOLOv3 for real-time object detection
Develops expertise in object detection algorithms and their practical implementation
Focuses on practical application, with the creation of a custom command-line application for object detection
Features expert instruction by Snehan Kekre, renowned for his work in object detection
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