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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Unsafe.training.sample_402

ID: 108389817

Label: Unsafe

Sample length: 1s

Hazardous.training.sample_258

ID: 108390190

Label: Hazardous

Sample length: 1s

Regulated.training.sample_384

ID: 108389964

Label: Regulated

Sample length: 1s

Unsafe.training.sample_406

ID: 108389814

Label: Unsafe

Sample length: 1s

Hazardous.training.sample_353

ID: 108390158

Label: Hazardous

Sample length: 1s

Hazardous.training.sample_352

ID: 108390163

Label: Hazardous

Sample length: 1s

Unsafe.training.sample_216

ID: 108389882

Label: Unsafe

Sample length: 1s

Unsafe.training.sample_394

ID: 108389823

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	42,820
No. of clones	3