Training computer vision models is complex, iterative, and requires a vast amount of high quality and 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. It augments costly, laborious human-labeled real-world data, which can be error prone and incomplete, with the ability to create large and diverse physically accurate data tailored to the needs of developers. This includes the ability to generate a broad range of diverse scenarios, including rare or dangerous conditions that can’t regularly or safely be experienced in the real world.
In this course, you will use NVIDIA Omniverse Replicator and the Omniverse Defect Extension to generate synthetic data. Next, you'll iterate on the dataset to train a deep neural network (DNN) to find target objects (scratches) in a scene.
Learning Objectives
By participating in this workshop, you’ll learn how to:
- Create a synthetic training dataset for later processing using NVIDIA Replicator
- Parameterize data generation offline with Replicator Composer for faster iteration when creating new or refined datasets
- Import a synthetic dataset into your workflow, train it, iterate on the design, and export a model to be used for inference
Upon completion, you'll be able to build an automated pipeline to create, train, and deploy synthetic data using 3-D assets.
Download workshop datasheet (PDF 165 KB)