Kutluhan Aktar / IoT AI-driven Yogurt Processing Public
Target: Cortex-M4F 80MHz Clone this project

Test data

Set confidence thresholds
Model variants
Test 'Unoptimized (float32)' model Test 'Quantized (int8)' model
Set the 'expected outcome' for each sample to the desired outcome to automatically score the impulse.
Sample name Expected outcome Length Accuracy Result
Thinner.sample_40 Thinner 1s 100% 1 Thinner
Show classification Show in data acquisition
Thinner.sample_39 Thinner 1s 100% 1 Thinner
Show classification Show in data acquisition
Thinner.sample_38 Thinner 1s 100% 1 Thinner
Show classification Show in data acquisition
Thinner.sample_37 Thinner 1s 100% 1 Thinner
Show classification Show in data acquisition
Thinner.sample_36 Thinner 1s 100% 1 Thinner
Show classification Show in data acquisition
Optimum.sample_40 Optimum 1s 100% 1 Optimum
Show classification Show in data acquisition
Optimum.sample_39 Optimum 1s 100% 1 Optimum
Show classification Show in data acquisition
Optimum.sample_38 Optimum 1s 100% 1 Optimum
Show classification Show in data acquisition
Optimum.sample_37 Optimum 1s 100% 1 Optimum
Show classification Show in data acquisition
Optimum.sample_36 Optimum 1s 100% 1 Optimum
Show classification Show in data acquisition
Curdling.sample_40 Curdling 1s 100% 1 Curdling
Show classification Show in data acquisition
Curdling.sample_39 Curdling 1s 100% 1 Curdling
Show classification Show in data acquisition
Curdling.sample_38 Curdling 1s 100% 1 Curdling
Show classification Show in data acquisition
Curdling.sample_36 Curdling 1s 100% 1 Curdling
Show classification Show in data acquisition
Curdling.sample_37 Curdling 1s 100% 1 Curdling
Show classification Show in data acquisition

Model testing output

Results

Model version:
Accuracy
100.00%

Confusion matrix

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Feature explorer

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