Thomas Garcia / vacuum-recognition-demo

This is your Edge Impulse project. From here you acquire new training data, design impulses and train models.

Creating your first impulse (100% complete)

Acquire data

Every Machine Learning project starts with data. You can capture data from a development board or your phone, or import data you already collected.

Let's collect some data

Design an impulse

Teach the model to interpret previously unseen data, based on historical data. Use this to categorize new data, or to find anomalies in sensor readings.

Getting started: Continuous motion recognition
Getting started: Responding to your voice
Getting started: Adding sight to your sensors

Deploy

Package the complete impulse up, from signal processing code to trained model, and deploy it on your device. This ensures that the impulse runs with low latency and without requiring a network connection.

Deploy your model

Download block output

Title Type Size
LogMFE training data NPY file 960 windows
LogMFE training labels NPY file 960 windows
LogMFE testing data NPY file 256 windows
LogMFE testing labels NPY file 256 windows
NN Classifier model TensorFlow Lite (float32) 21 KB
NN Classifier model TensorFlow Lite (int8 quantized) 11 KB
NN Classifier model TensorFlow Lite (int8 quantized with float32 input and output) 11 KB
NN Classifier model TensorFlow SavedModel 32 KB

Summary

Data collected
12m 40s

Project info

Project ID 31815
Project version 2