Shebin Jose Jacob / Clog Detection With AI 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
flow.s29 flow 6s 100% 6 flow
Show classification Show in data acquisition
no_flow.s24 no_flow 6s 100% 6 no_flow
Show classification Show in data acquisition
no_flow.s16 no_flow 6s 100% 6 no_flow
Show classification Show in data acquisition
no_flow.s14 no_flow 6s 100% 6 no_flow
Show classification Show in data acquisition
no_flow.s11 no_flow 6s 100% 6 no_flow
Show classification Show in data acquisition
no_flow.s6 no_flow 6s 100% 6 no_flow
Show classification Show in data acquisition
no_flow.s3 no_flow 6s 100% 6 no_flow
Show classification Show in data acquisition
flow.s30 flow 6s 100% 6 flow
Show classification Show in data acquisition
flow.s27 flow 6s 100% 6 flow
Show classification Show in data acquisition
flow.s25 flow 6s 100% 6 flow
Show classification Show in data acquisition
flow.s21 flow 6s 100% 6 flow
Show classification Show in data acquisition
flow.s9 flow 6s 100% 6 flow
Show classification Show in data acquisition
clog.s26 clog 6s 100% 6 clog
Show classification Show in data acquisition
clog.s23 clog 6s 100% 6 clog
Show classification Show in data acquisition
clog.s20 clog 6s 100% 6 clog
Show classification Show in data acquisition
clog.s9 clog 6s 100% 6 clog
Show classification Show in data acquisition
clog.s4 clog 6s 100% 6 clog
Show classification Show in data acquisition
clog.s1 clog 6s 100% 6 clog
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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