Brainchip / Akida Image Classification
This is the finished Edge Impulse project for the tutorial 'Adding sight to your sensors'. From here you can acquire new training data, design impulses and train models.
About this project
Lamps and Plants and Bears, oh my!
Want to make the most of your device's camera and predict what is/is not present in your device's environment? Clone this project to build an embedded ML project to add intelligent sight to your device.
You can also follow our tutorial to guide you through building your image classification model, from data collection to deployment on embedded devices.
Sensor & Block Information
- Image data
- Image DSP block for normalizing image files
- Transfer Learning block with prediction outputs: "lamp", "plant", "unknown"
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 | |
|---|---|---|---|
| Image training data | NPY file | 221 windows | |
| Image training labels | NPY file | 221 windows | |
| Image testing data | NPY file | 55 windows | |
| Image testing labels | NPY file | 55 windows | |
| NN Classifier model | TensorFlow Lite (float32) | 74 KB | |
| NN Classifier model | TensorFlow Lite (int8 quantized) | 24 KB | |
| NN Classifier model | TensorFlow SavedModel | 75 KB | |
| NN Classifier model | Keras h5 model | 70 KB |
Clone project
Summary
Data collected
276 itemsProject info
| Project ID | 124788 |
| Project version | 1 |
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