Whether you are training a self-driving car, detecting animals with drones, or identifying car damage for insurance claims, the steps needed to effectively train a computer vision model at scale remain the same.
In this latest Data Science Central webinar, we’ll walk through best practices for managing a computer vision project including staffing, budgeting, and roles and responsibilities. Learn how to collect and label the data that will train and tune your machine learning algorithm, and which types of data labeling best fit your project along with the tools that will get the job done.
In this webinar, you’ll learn how to:
Identify key success factors when scoping a computer vision project
Determine what kind of source data you need to make it successful
Select tools that best fit your project
Label your dataset so your algorithms can learn and perform as designed
Meeta Dash, Director of Product - Figure Eight
Stephanie Glen, Editorial Director - Data Science Central