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Mohammed Murtuza Qureshi
In this 2-hour long project-based course, you will learn how to train an Object Detection Model using Facebook's Detectron2. Detectron2 is a research platform and a production library for deep learning, built by Facebook AI Research (FAIR). We will be building an Object Detection Language Identification Model to identify English and Hindi texts written which can be extended to different use cases. We will look at the entire cycle of Model Development and Evaluation in Detectron2. We will first look at how to load a dataset, visualize it and prepare it as an input to the Deep Learning Model.
Read more
In this 2-hour long project-based course, you will learn how to train an Object Detection Model using Facebook's Detectron2. Detectron2 is a research platform and a production library for deep learning, built by Facebook AI Research (FAIR). We will be building an Object Detection Language Identification Model to identify English and Hindi texts written which can be extended to different use cases. We will look at the entire cycle of Model Development and Evaluation in Detectron2. We will first look at how to load a dataset, visualize it and prepare it as an input to the Deep Learning Model.
Read more
In this 2-hour long project-based course, you will learn how to train an Object Detection Model using Facebook's Detectron2. Detectron2 is a research platform and a production library for deep learning, built by Facebook AI Research (FAIR). We will be building an Object Detection Language Identification Model to identify English and Hindi texts written which can be extended to different use cases. We will look at the entire cycle of Model Development and Evaluation in Detectron2. We will first look at how to load a dataset, visualize it and prepare it as an input to the Deep Learning Model.
We will then look at how we can build a Faster R-CNN model in Detectron2 and customize it. We will then configure the parameters & hyperparameters of the model. We will then move on to training the Model and subsequently to model inference and evaluation.
Note: 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/ggbpwn/object
What's inside
Syllabus
Project Overview
Welcome to this Guided Project on Object Detection using Facebook’s Detectron2. Detectron2 is a research platform and a production library for deep learning, built by Facebook AI Research (FAIR). We will be building an Object Detection Language Identification Model to identify English and Hindi texts written which can be extended to different use cases. We will look at the entire cycle of Model Development and Evaluation in Detectron2.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Tailored for individuals seeking to master object detection using Facebook's Detectron2
Suitable for learners with basic knowledge of deep learning and computer vision concepts
Practical, project-based approach provides hands-on experience with real-world applications
Covers the full workflow of object detection model development and evaluation, including dataset preparation, model training, and inference
Uses Facebook's Detectron2, a reputable deep learning library for object detection tasks
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Save Object Detection Using Facebook's Detectron2 to your list so you can find it easily later:
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Reviews summary
Challenging course with technical difficulties
Learners say this course on object detection using Facebook's Detectron2 has engaging content but suffers from technical difficulties and poor instruction. Students report that the instructor is knowledgeable but lacks teaching skills and that technical issues consume too much time. The support is also said to be unhelpful. With improvements to instruction and support, learners say this course could be more positive and fulfilling.
Instructor has strong grasp of material.
"The online course featured an instructor with a strong grasp of the material"
Support is unhelpful.
"the support provided was lacking in helpfulness"
Instructor lacks teaching skills.
"their teaching skills and organization fell short in effectively transferring that knowledge to the audience."
Course has technical issues.
"technical issues proved to be a major hurdle"
"I had to navigate them independently."
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 Object Detection Using Facebook's Detectron2 with these
activities:
Practice building Object Detection Models in Python
Show steps
Develop a working understanding of the core concepts of Object Detection, allowing you to better understand the material covered in this course.
Browse courses on
Object Detection
Show steps
-
Install Detectron2 and its dependencies
-
Create a sample dataset and pre-process it
-
Define a Mask R-CNN model in Detectron2
-
Train the model using your dataset
-
Evaluate the performance of your trained model
Read 'Computer Vision: Algorithms and Applications' by Richard Szeliski
Show steps
Gain foundational knowledge in computer vision and image processing, which will complement the material covered in this course.
View
Computer Vision
on Amazon
Show steps
-
Read Chapters 8 and 9 on Image Segmentation and Object Recognition
-
Review the provided examples and algorithms
-
Apply your understanding to the course material
Complete a series of Object Detection exercises in Detectron2
Show steps
Strengthen your understanding of Object Detection concepts by working through guided exercises.
Show steps
-
Load a pre-trained model and visualize its output
-
Fine-tune the model on a new dataset
-
Deploy the model for inference
-
Create a custom training pipeline
Five other activities
Expand to see all activities and additional details
Show all eight activities
Participate in a study group to discuss Object Detection concepts
Show steps
Collaborate with peers to exchange knowledge, clarify concepts, and reinforce your understanding of Object Detection.
Show steps
-
Join or create a study group with other learners
-
Meet regularly to discuss course material and concepts
-
Work together on practice problems and projects
Write a blog post summarizing the main concepts of Object Detection
Show steps
Reinforce your understanding of Object Detection by writing a comprehensive blog post on the topic.
Show steps
-
Define Object Detection and explain its applications
-
Discuss the different types of Object Detection models
-
Describe the training and evaluation process for Object Detection models
-
Share your own insights and findings
Follow online tutorials on advanced Object Detection techniques
Show steps
Expand your knowledge of Object Detection by exploring advanced techniques through guided tutorials.
Show steps
-
Implement Object Detection using YOLOv5
-
Apply Object Detection to real-world problems
-
Explore the latest research in Object Detection
Attend a workshop on Deep Learning for Object Detection
Show steps
Enhance your practical skills in Object Detection by attending a focused workshop led by experts in the field.
Show steps
-
Register for the workshop
-
Actively participate in the sessions
-
Ask questions and engage with the instructors
Build a web application that demonstrates Object Detection functionality
Show steps
Apply your Object Detection knowledge to create a practical and visually engaging project that showcases your skills.
Show steps
-
Design a web application prototype
-
Implement Object Detection using a library like OpenCV or TensorFlow
-
Create a user interface for uploading images and displaying results
-
Deploy the web application for public use
Practice building Object Detection Models in Python
Show steps
Develop a working understanding of the core concepts of Object Detection, allowing you to better understand the material covered in this course.
Browse courses on
Object Detection
Show steps
Show steps
Show steps
- Install Detectron2 and its dependencies
- Create a sample dataset and pre-process it
- Define a Mask R-CNN model in Detectron2
- Train the model using your dataset
- Evaluate the performance of your trained model
Read 'Computer Vision: Algorithms and Applications' by Richard Szeliski
Show steps
Gain foundational knowledge in computer vision and image processing, which will complement the material covered in this course.
View
Computer Vision
on Amazon
Show steps
Show steps
Show steps
- Read Chapters 8 and 9 on Image Segmentation and Object Recognition
- Review the provided examples and algorithms
- Apply your understanding to the course material
Complete a series of Object Detection exercises in Detectron2
Show steps
Strengthen your understanding of Object Detection concepts by working through guided exercises.
Show steps
Show steps
Show steps
- Load a pre-trained model and visualize its output
- Fine-tune the model on a new dataset
- Deploy the model for inference
- Create a custom training pipeline
Five other activities
Expand to see all activities and additional details
Show all eight activities
Participate in a study group to discuss Object Detection concepts
Show steps
Collaborate with peers to exchange knowledge, clarify concepts, and reinforce your understanding of Object Detection.
Show steps
Show steps
Show steps
- Join or create a study group with other learners
- Meet regularly to discuss course material and concepts
- Work together on practice problems and projects
Write a blog post summarizing the main concepts of Object Detection
Show steps
Reinforce your understanding of Object Detection by writing a comprehensive blog post on the topic.
Show steps
Show steps
Show steps
- Define Object Detection and explain its applications
- Discuss the different types of Object Detection models
- Describe the training and evaluation process for Object Detection models
- Share your own insights and findings
Follow online tutorials on advanced Object Detection techniques
Show steps
Expand your knowledge of Object Detection by exploring advanced techniques through guided tutorials.
Show steps
Show steps
Show steps
- Implement Object Detection using YOLOv5
- Apply Object Detection to real-world problems
- Explore the latest research in Object Detection
Attend a workshop on Deep Learning for Object Detection
Show steps
Enhance your practical skills in Object Detection by attending a focused workshop led by experts in the field.
Show steps
Show steps
Show steps
- Register for the workshop
- Actively participate in the sessions
- Ask questions and engage with the instructors
Build a web application that demonstrates Object Detection functionality
Show steps
Apply your Object Detection knowledge to create a practical and visually engaging project that showcases your skills.
Show steps
Show steps
Show steps
- Design a web application prototype
- Implement Object Detection using a library like OpenCV or TensorFlow
- Create a user interface for uploading images and displaying results
- Deploy the web application for public use
Career center
Learners who complete Object Detection Using Facebook's Detectron2 will develop knowledge and skills
that may be useful to these careers:
Computer Vision Engineer
Computer Vision Engineer
As a Computer Vision Engineer, you will develop and implement computer vision algorithms and models to solve real-world problems. This course provides a solid foundation in object detection using Detectron2, which is a powerful tool for building computer vision applications. By completing this course, you will gain the skills and knowledge necessary to succeed in this field.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models to solve a variety of problems. This course provides a foundation in object detection using Detectron2, which is a valuable skill for Machine Learning Engineers. By completing this course, you will gain the skills and knowledge necessary to build and deploy object detection models.
Data Scientist
Data Scientist
Data Scientists use data to solve business problems. This course provides a foundation in object detection using Detectron2, which is a valuable skill for Data Scientists. By completing this course, you will gain the skills and knowledge necessary to build and deploy object detection models to solve real-world problems.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software systems. This course provides a foundation in object detection using Detectron2, which is a valuable skill for Software Engineers. By completing this course, you will gain the skills and knowledge necessary to build and deploy object detection models.
Deep Learning Engineer
Deep Learning Engineer
Deep Learning Engineers design, develop, and deploy deep learning models to solve a variety of problems. This course provides a foundation in object detection using Detectron2, which is a valuable skill for Deep Learning Engineers. By completing this course, you will gain the skills and knowledge necessary to build and deploy object detection models.
Computer Vision Researcher
Computer Vision Researcher
Computer Vision Researchers develop new algorithms and models for computer vision. This course provides a foundation in object detection using Detectron2, which is a valuable skill for Computer Vision Researchers. By completing this course, you will gain the skills and knowledge necessary to conduct research in the field of computer vision.
Artificial Intelligence Engineer
Artificial Intelligence Engineer
Artificial Intelligence Engineers design, develop, and deploy artificial intelligence systems. This course provides a foundation in object detection using Detectron2, which is a valuable skill for Artificial Intelligence Engineers. By completing this course, you will gain the skills and knowledge necessary to build and deploy artificial intelligence systems.
Data Analyst
Data Analyst
Data Analysts collect, analyze, and interpret data to solve business problems. This course provides a foundation in object detection using Detectron2, which is a valuable skill for Data Analysts. By completing this course, you will gain the skills and knowledge necessary to use object detection models to solve business problems.
Robotics Engineer
Robotics Engineer
Robotics Engineers design, develop, and maintain robots. This course provides a foundation in object detection using Detectron2, which is a valuable skill for Robotics Engineers. By completing this course, you will gain the skills and knowledge necessary to build and deploy object detection models for robots.
User Experience Designer
User Experience Designer
User Experience Designers design and evaluate user interfaces. This course provides a foundation in object detection using Detectron2, which is a valuable skill for User Experience Designers. By completing this course, you will gain the skills and knowledge necessary to use object detection models to improve the user experience.
Product Manager
Product Manager
Product Managers manage the development and launch of new products. This course provides a foundation in object detection using Detectron2, which is a valuable skill for Product Managers. By completing this course, you will gain the skills and knowledge necessary to use object detection models to develop and launch new products.
Sales Engineer
Sales Engineer
Sales Engineers sell and support technical products and services. This course provides a foundation in object detection using Detectron2, which is a valuable skill for Sales Engineers. By completing this course, you will gain the skills and knowledge necessary to use object detection models to sell and support technical products and services.
Technical Writer
Technical Writer
Technical Writers create and maintain documentation for technical products and services. This course provides a foundation in object detection using Detectron2, which is a valuable skill for Technical Writers. By completing this course, you will gain the skills and knowledge necessary to use object detection models to create and maintain documentation for technical products and services.
Quality Assurance Analyst
Quality Assurance Analyst
Quality Assurance Analysts test and evaluate software products and services. This course provides a foundation in object detection using Detectron2, which is a valuable skill for Quality Assurance Analysts. By completing this course, you will gain the skills and knowledge necessary to use object detection models to test and evaluate software products and services.
Business Analyst
Business Analyst
Business Analysts analyze business processes and identify opportunities for improvement. This course provides a foundation in object detection using Detectron2, which is a valuable skill for Business Analysts. By completing this course, you will gain the skills and knowledge necessary to use object detection models to analyze business processes and identify opportunities for improvement.
For more career information including salaries, visit:
OpenCourser.com/course/ggbpwn/object
Reading list
We've selected six 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
Object Detection Using Facebook's Detectron2.
Save
Provides a comprehensive overview of deep learning. It covers a wide range of topics, from neural networks to deep learning architectures. It valuable resource for anyone who wants to learn more about deep learning.
Provides a practical introduction to deep learning for computer vision. It covers a wide range of topics, from convolutional neural networks to object detection and segmentation. It valuable resource for anyone who wants to learn more about deep learning for computer vision.
Provides a comprehensive overview of pattern recognition and machine learning. It covers a wide range of topics, from statistical learning to Bayesian inference. It valuable resource for anyone who wants to learn more about pattern recognition and machine learning.
Save
Provides a comprehensive overview of computer vision algorithms and their applications. It covers a wide range of topics, from image processing and feature extraction to object detection and recognition. It valuable resource for anyone who wants to learn more about computer vision.
Provides a comprehensive overview of computer vision. It covers a wide range of topics, from image processing and feature extraction to object detection and recognition. It valuable resource for anyone who wants to learn more about computer vision.
Provides a comprehensive overview of artificial intelligence. It covers a wide range of topics, from machine learning to natural language processing. It valuable resource for anyone who wants to learn more about artificial intelligence.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/ggbpwn/object
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Effort
2 hours
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Mohammed Murtuza Qureshi
Language
English
Good to know
Tailored for individuals seeking to master object detection using Facebook's Detectron2
Suitable for learners with basic knowledge of deep learning and computer vision concepts
Practical, project-based approach provides hands-on experience with real-world applications
Covers the full workflow of object detection model development and evaluation, including dataset preparation, model training, and inference
Uses Facebook's Detectron2, a reputable deep learning library for object detection tasks
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