Hands-on Data Centric Visual AI
Master data-centric approaches to improve visual AI models through dataset quality management and advanced annotation techniques.
Master data-centric approaches to improve visual AI models through dataset quality management and advanced annotation techniques.
This course provides a comprehensive, hands-on guide to developing and maintaining high-quality datasets for visual AI applications. Students will learn to implement various labeling approaches, assess and improve annotation quality for object detection tasks, and analyze the impact of data quality on model performance. The curriculum covers advanced tools like FiftyOne and CVAT for dataset exploration and refinement, addressing complex challenges in computer vision such as overlapping detections and small object detection. Learners will gain practical skills in data augmentation, sample hardness analysis, and entropy-based dataset curation to optimize visual AI model accuracy and reliability.
Instructors:
English
Powered by
What you'll learn
Implement and evaluate various labeling approaches for visual AI datasets
Assess and improve annotation quality for object detection tasks
Analyze the impact of bounding box quality on model performance
Use advanced tools like FiftyOne and CVAT for dataset exploration and refinement
Address complex challenges in computer vision, including overlapping detections and small object detection
Apply data augmentation techniques to improve model robustness and generalization
Skills you'll gain
This course includes:
5.58 Hours PreRecorded video
12 assignments
Access on Mobile, Tablet, Desktop
FullTime access
Shareable certificate
Get a Completion Certificate
Share your certificate with prospective employers and your professional network on LinkedIn.
Created by
Provided by
Top companies offer this course to their employees
Top companies provide this course to enhance their employees' skills, ensuring they excel in handling complex projects and drive organizational success.
There are 4 modules in this course
This course offers a comprehensive exploration of data-centric approaches to visual AI, focusing on developing and maintaining high-quality datasets for computer vision applications. Students will learn advanced techniques for dataset management, annotation quality assessment, and model performance optimization. The curriculum covers a wide range of topics, including various labeling approaches, handling complex scenarios like overlapping detections and occlusions, and addressing challenges in small object detection. Learners will gain hands-on experience with tools like FiftyOne and CVAT for dataset exploration, error correction, and annotation refinement. The course also delves into advanced concepts such as data augmentation, sample hardness analysis, and entropy-based dataset curation. Through a combination of theoretical knowledge and practical exercises, students will develop the skills to create, maintain, and optimize datasets that lead to more accurate and reliable visual AI models.
Getting Started and the Data-Centric AI Paradigm
Module 1 · 6 Hours to complete
Image Quality and Its Impact on Model Performance
Module 2 · 8 Hours to complete
Label Quality and Its Impact on Model Performance
Module 3 · 5 Hours to complete
Putting It All Together
Module 4 · 1 Hours to complete
Fee Structure
Payment options
Financial Aid
Instructor
Hacker-in-Residence at Voxel51 Specializing in Visual and Generative AI
Harpreet Sahota is a Hacker-in-Residence at Voxel51 and has been actively involved in Developer Relations within the AI space since 2020, having worked with notable companies such as Deci AI and Comet. With a strong background in visual and generative AI, Harpreet brings extensive hands-on experience to his role. He holds Master's degrees in Mathematics and Statistics, and prior to his current position, he has served in various roles including data scientist, statistician, and actuary. Harpreet teaches the course Hands-on Data Centric Visual AI, which focuses on practical applications of data-centric approaches in visual AI.
Testimonials
Testimonials and success stories are a testament to the quality of this program and its impact on your career and learning journey. Be the first to help others make an informed decision by sharing your review of the course.
Frequently asked questions
Below are some of the most commonly asked questions about this course. We aim to provide clear and concise answers to help you better understand the course content, structure, and any other relevant information. If you have any additional questions or if your question is not listed here, please don't hesitate to reach out to our support team for further assistance.