For many projects, you will need to constrain the EON Tuner to use steps that are defined by your hardware, your customers, or your expertise.
For example:
Your project requires to use a grayscale camera because you already purchased the hardware.
Your engineers have already spent hours working on a dedicated digital signal processing method that has been proven to work with your sensor data.
You have the feeling that a particular neural network architecture will be more suited for your project.
This is why we developed an extension of the EON Tuner: the EON Tuner Search Space.
Please read first the EON Tuner documentation to configure your Target, Task category and desired Time per inference.
The EON Tuner Search Space allows you to define the structure and constraints of your machine learning projects through the use of templates.
The Search Space works with templates. The templates can be considered as a config file where you define your constraints. Although templates may seem hard to use in the first place, once you understand the core concept, this tool is extremely powerful!
A blank template looks like the following:
To understand the core concepts, we recommend having a look at the available templates. We provide templates for different task categories as well as one for your current impulse if it has already been trained.
Elements inside an array are considered as parameters. This means, you can stack several combinations of inputBlocks|dspBlocks|learnBlocks
in your templates and each block
can contain several elements:
or
You can easily add pre-defined blocks using the + Add block section.
The actual availability of certain dimensions or options can depend on whether your project has full enterprise capabilities (projectHasFullEonTunerSearchSpace
). This might unlock additional valid values or remove restrictions on certain fields.
Fields within array
of array
structures (like dimension
or window
) allow for multi-dimensional setups where each sub-array represents a different configuration that the EON Tuner can evaluate.
Only available with Edge Impulse Professional and Enterprise Plans
Try our Professional Plan or FREE Enterprise Trial today.
The parameters set in the custom DSP block are automatically retrieved.
Example using a custom ToF (Time of Flight) pre-processing block:
Example using EfficientNet (available through a custom ML block) on a dataset containing images of 4 cats: