Kutluhan Aktar / IoT AI-driven Food Irradiation Classifier Public

Kutluhan Aktar / IoT AI-driven Food Irradiation Classifier

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

About this project

Project Description

This artificial neural network model (ANN) makes predictions on food irradiation dose levels (classes) based on ionizing radiation, weight, and visible light (color) measurements:

  • Regulated
  • Unsafe
  • Hazardous

After building my neural network model, I deployed my model as an Arduino library and uploaded it to Beetle ESP32-C3 so as to run inferences.

home_1.jpg

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
Raw data training data NPY file 486 windows
Raw data training labels NPY file 486 windows
Raw data testing data NPY file 54 windows
Raw data testing labels NPY file 54 windows
NN Classifier model TensorFlow Lite (float32) 13 KB
NN Classifier model TensorFlow Lite (int8 quantized) 5 KB
NN Classifier model TensorFlow SavedModel 18 KB
NN Classifier model Keras h5 model 13 KB

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Summary

Data collected
9m 0s

Project info

Project ID 113653
Project version 1
License No license attached