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_456

ID: 108389958

Label: Regulated

Hazardous.training.sample_84

ID: 108390242

Label: Hazardous

Unsafe.training.sample_38

ID: 108389931

Label: Unsafe

Regulated.training.sample_102

ID: 108390070

Label: Regulated

Regulated.training.sample_184

ID: 108390044

Label: Regulated

Hazardous.training.sample_241

ID: 108390210

Label: Hazardous

Hazardous.training.sample_532

ID: 108390107

Label: Hazardous

Hazardous.training.sample_350

ID: 108390159

Label: Hazardous

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

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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	50,996
No. of clones	4