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.
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.
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.
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 |
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Summary
Data collected
12m 40sProject info
| Project ID | 32145 |
| Project version | 2 |
| License | No license attached |
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