Kutluhan Aktar / IoT AI-driven Food Irradiation Classifier Public

Target: Cortex-M4F 80MHz Clone this project

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IoT AI-driven Food Irradiation Classifier

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.

Data (540 samples)

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Regulated.training.sample_285

ID: 108390003

Label: Regulated

Sample length: 1s

Hazardous.training.sample_523

ID: 108390113

Label: Hazardous

Sample length: 1s

Unsafe.training.sample_417

ID: 108389808

Label: Unsafe

Sample length: 1s

Unsafe.training.sample_143

ID: 108389894

Label: Unsafe

Sample length: 1s

Hazardous.training.sample_519

ID: 108390119

Label: Hazardous

Sample length: 1s

Unsafe.training.sample_311

ID: 108389846

Label: Unsafe

Sample length: 1s

Hazardous.training.sample_160

ID: 108390227

Label: Hazardous

Sample length: 1s

Hazardous.training.sample_440

ID: 108390134

Label: Hazardous

Sample length: 1s

Model accuracy (Unoptimized (float32))

Validation set

100.0%

Test set

96.3%

On-device performance (Cortex-M4F 80MHz, Quantized (int8))

Latency

4 ms.

Peak RAM usage

1.7K

Flash usage

21.4K

Run this model

On any device

Clone project

Dataset summary

Data collected

9m 0s

Sensors

weight, f1, f2, f3, f4, f5, f6, f7, f8, cpm, nsv, usv @ 1Hz

Labels

Hazardous, Regulated, Unsafe

Project info

Project ID	109647
Project version	1
License	No license attached
No. of views	40,332
No. of clones	3