Edge Impulse Machine Learning

Set up Edge Impulse tools on Pi.

1. Update system: sudo apt update && sudo apt upgrade
2. Install Node.js: curl -sL https://deb.nodesource.com/setup_14.x | sudo bash -
3. Install dependencies: sudo apt install nodejs gcc g++ make build-essential
4. Install Edge Impulse CLI: sudo npm install -g edge-impulse-linux
5. Create free account at edgeimpulse.com.

Link your Pi to Edge Impulse project.

1. Run: edge-impulse-linux
2. Log in with your Edge Impulse credentials.
3. Create new project or select existing one.
4. Pi is now connected and ready to collect data.

Capture images or audio for your model.

1. Connect USB webcam to Pi.
2. In Edge Impulse dashboard, go to Data Acquisition.
3. Use Pi to capture images of objects you want to detect.
4. Capture at least 50-100 images per object class.
5. Label each image with the correct class.
6. For audio: use USB microphone to record sound samples.

Build a machine learning model.

1. In Edge Impulse, go to Create Impulse.
2. For object detection: add Image processing block and Object Detection learning block.
3. For audio: add Audio processing block and Classification learning block.
4. Generate features from your data.
5. Train the neural network (takes 5-10 minutes).
6. Review accuracy metrics.

Download and run trained model.

1. In Edge Impulse dashboard, go to Deployment.
2. Select 'Linux (ARMv7)' as target.
3. Download the model package.
4. Extract on Raspberry Pi.
5. Run: edge-impulse-linux-runner
6. Model now runs inference on Pi!

Verify detection works.

1. Point camera at trained objects.
2. Watch terminal for detection results and confidence scores.
3. For audio: make sounds the model was trained on.
4. Adjust camera angle or lighting for better results.