Detailed Course Outline
Introduction
- Meet the instructor.
- Create an account at courses.nvidia.com/join
Introduction to Synthetic Data Generation (SDG) With Omniverse Replicator
- Learn how to create a synthetic training dataset for later processing:
- Discuss the case for synthetic data.
- Learn the basics of the Replicator Python API for SDG.
- Create example datasets using Python scripts using an NVIDIA Omniverse application interface.
- Create a defects dataset using the Omniverse Defects Generation Extension and the Omniverse Defects demo pack.
- Modify the extension code to change the dataset generated.
Headless SDG and Replicator YAML Extension
- Learn to parameterize data generation offline using the Replicator YAML extension for faster iteration when creating new or refined datasets:
- Discuss the advantages and disadvantages of running Omniverse Replicator in headless mode.
- Learn to run Omniverse Replicator in headless mode using a configuration file.
- Iterate on changes to the configuration file to generate new datasets.
Integrating Dataset Iteration Into the Training Workflow
- Learn how to import a synthetic dataset into your workflow, train it, iterate on the dataset design, and export a model to be used for inference:
- Discuss practical guidelines and examples for training a perception dataset to find a target object.
- Fine-tune a visual transformer (ViT) model using NVIDIA TAO as the example workflow.
- Iterate on the model by improving the data to solve accuracy issues.
- Export the model for later deployment.
Assessment and Q&A
- Review key learnings.
- Take a code-based assessment to earn a certificate