Synthetic data

Synthetic datasets are a collection of data artificially generated rather than being collected from real-world observations or measurements. They are created using algorithms, simulations, or mathematical models to mimic the characteristics and patterns of real data. Synthetic datasets are a valuable tool to generate data for experimentation, testing, and development when obtaining real data is challenging, costly, or undesirable.

You might want to generate synthetic datasets for several reasons:

Cost Efficiency: Creating synthetic data can be more cost-effective and efficient than collecting large volumes of real data, especially in resource-constrained environments.

Data Augmentation: Synthetic datasets allow users to augment their real-world data with variations, which can improve model robustness and performance.

Data Diversity: Synthetic datasets enable the inclusion of uncommon or rare scenarios, enriching model training with a wider range of potential inputs.

Privacy and Security: When dealing with sensitive data, synthetic datasets provide a way to train models without exposing real information, enhancing privacy and security.

You can generate synthetic data directly from Edge Impulse using the Synthetic Data tab in the Data acquisition view. This tab provides a user-friendly interface to generate synthetic data for your projects. You can create synthetic datasets using a variety of tools and models.

Synthetic Data Tab

We have put together the following tutorials to help you get started with synthetic datasets generation:

Using integrated models directly available inside Edge Impulse Studio

• DALL-E Image Generation Block: Generate image datasets using Dall·E using the DALL-E model.
• Whisper Keyword Spotting Generation Block: Generate keyword-spotting datasets using the Whisper model. Ideal for keyword spotting and speech recognition applications.
• Eleven Labs Sound Generation Block: Generate sound datasets using the Eleven Labs model. Ideal for generating realistic sound effects for various applications.

Note that you will need an API Key/Access Token from the different providers to run the model used to generate the synthetic data.

If you want to create your own synthetic data block, see Custom synthetic data blocks.

Other tutorials

• Generate image datasets using Dall·E (Jupyter Notebook and Transformation block source code available).
• Generate keyword-spotting datasets (Jupyter Notebook source code available).
• Generate physics simulation datasets (Jupyter Notebook source code available).
• Generate synthetic datasets using NVIDIA Omniverse.