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)

View all

Regulated.training.sample_92

ID: 108390072

Label: Regulated

Sample length: 1s

Unsafe.training.sample_52

ID: 108389915

Label: Unsafe

Sample length: 1s

Unsafe.training.sample_53

ID: 108389914

Label: Unsafe

Sample length: 1s

Unsafe.training.sample_130

ID: 108389903

Label: Unsafe

Sample length: 1s

Unsafe.training.sample_139

ID: 108389891

Label: Unsafe

Sample length: 1s

Hazardous.training.sample_519

ID: 108390119

Label: Hazardous

Sample length: 1s

Hazardous.training.sample_432

ID: 108390140

Label: Hazardous

Sample length: 1s

Unsafe.training.sample_326

ID: 108389829

Label: Unsafe

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

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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	43,889
No. of clones	3