Install Go 1.15 or higher.
Clone this repository:
$ git clone https://github.com/edgeimpulse/linux-sdk-go
Find the example that you want to build and run
$ cd cmd/eimclassify $ go build
Run the example:
And follow instructions.
This SDK is also published to pkg.go.dev, so you can pull the package from there too.
Before you can classify data you'll first need to collect it. If you want to collect data from the camera or microphone on your system you can use the Edge Impulse CLI, and if you want to collect data from different sensors (like accelerometers or proprietary control systems) you can do so in a few lines of code.
To collect data from the camera or microphone, follow the getting started guide for your development board.
To collect data from other sensors you'll need to write some code to collect the data from an external sensor, wrap it in the Edge Impulse Data Acquisition format, and upload the data to the Ingestion service. Here's an end-to-end example.
To classify data (whether this is from the camera, the microphone, or a custom sensor) you'll need a model file. This model file contains all signal processing code, classical ML algorithms and neural networks - and typically contains hardware optimizations to run as fast as possible. To grab a model file:
Train your model in Edge Impulse.
Download the model file via:
$ edge-impulse-linux-runner --download modelfile.eim
This downloads the file into
modelfile.eim. (Want to switch projects? Add
Then you can start classifying realtime sensor data. We have examples for:
Updated about a month ago