When does class start/end?
Classes begin promptly at 9:00 am, and typically end at 5:00 pm.
Training computer vision models is complex, iterative, and requires a vast amount of high-quality, relevant visual data. Traditionally, this process relies on visual data gathered from the real world...
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Training computer vision models is complex, iterative, and requires a vast amount of high-quality, relevant visual data. Traditionally, this process relies on visual data gathered from the real world with cameras and sensors, often manually labeled, to represent the scenarios and situations that the model needs to learn.
NVIDIA Omniverse™ Replicator is a powerful synthetic data generation (SDG) engine that produces physically simulated synthetic data for training deep neural networks (DNNs). It augments costly, laborious human-labeled data, which can be error-prone and incomplete, with diverse physically accurate data tailored to the needs of developers.
In this course, you’ll use Omniverse Replicator and the Omniverse Defects Generation Extension to generate synthetic data. Next, you’ll iterate on the dataset to train a DNN to find target objects (scratches) in a scene.
Introduction
Introduction to Synthetic Data Generation (SDG) With Omniverse Replicator
Headless SDG and Replicator YAML Extension
Integrating Dataset Iteration Into the Training Workflow
Assessment and Q&A