Using Edge Impulse to train an image and audio recognition model Edge Impulse is an incredibly powerful tool which can be quickly used to train many kinds of models. Here we will make a demonstration of using transfer learning to train an image model to tell the difference between different images. We will demonstrate how Edge Impulse smooths data collection, model generation and live testing and how the models can be easily deployed to an edge device. As a bonus I will also demonstrate an audio recognition model.

1. Image and Audio
Recognition with Edge
Impulse
By John Staveley MVP
23/05/2023
https://uk.linkedin.com/in/johnstaveley/
@johnstaveley

2. Overview
 Creating projects in EdgeImpulse
 Linking a device
 Capturing and classifying data
 Choosing and refining a model
 Deploying and using an image recognition model
 Audio recognition in EdgeImpulse
 Deploying the audio recognition model

3. Creating
a project

4. DEMO

5. Set data
source
https://docs.edgeimpulse.co
m/docs/development-
platforms/fully-supported-
development-boards

6. Use your phone
 Use your mobile phone:
Add device – scan QR code for mobile phone
 Opens in web page - no app required
 Take 30-40 pictures from different angles
 Split into train and test data

7. Set labels

8. Create model
Transfer learning

9. Generate features

10. Generate model

11. Generate model
 Wait 20 minutes
https://stephenallwright.com/interpret-f1-score/

12. Training length v performance
 Training cycles : F1 Score : Time
 60 : 0% : 5 mins
 130 : 26.7% : 10 mins
 250 : 57.1% : 20 mins

13. Deploying the model
 Scan the QR code

14. Other Deployments
 PC
 WebAssembly
 Arduino
 Raspberry Pi
 Jetson Nano
 Export model in Tensorflow
 using Microsoft.ML.Tensorflow

15. Audio detection using Edge Impulse
 Data collection, split 80/20%
 Generate features
 Select and train model
 Deployment
 Download model
 Upload to Nicla voice with Arduino sketch

16. Conclusion
 Data acquisition, model creation and deployment are easy in EdgeImpulse
 Train the model for as long as possible
 Resultant model can be used in a number of platforms

17. Resources
 https://www.edgeimpulse.com
 https://www.youtube.com/watch?v=dY3OSiJyne0&t=105s
 https://docs.arduino.cc/tutorials/nicla-voice/getting-started-ml

18. Any Questions?
@johnstaveley
Slides:
https://www.slideshare.net/johnstaveley/

19. Question for the pub:
 What will data science as a profession look like in 10 years time?
 Will it all be just running the tools?