Brainchip / Akida Image Classification Public

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!

Plant classification OpenMV

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.


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

You are viewing a public Edge Impulse project. Clone this project to add data or make changes.


Data collected
276 items

Project info

Project ID 124788
Project version 1