Mithun / sony-analog-meter Public
Target: Sony Spresense (Cortex-M4F 156MHz) Clone this project

Test data

Set 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. Maximum absolute regression error is 0.1, set thresholds.
Sample name Expected outcome Accuracy Result Error
13.112821 13 0% 18.39 5.39 (41.5%)
Show classification Show in data acquisition
65.114844 65 0% 62.92 2.08 (3.2%)
Show classification Show in data acquisition
13.113245 13 0% 16.69 3.69 (28.4%)
Show classification Show in data acquisition
85.120914 85 0% 81.8 3.20 (3.8%)
Show classification Show in data acquisition
65.115455 65 0% 62.34 2.66 (4.1%)
Show classification Show in data acquisition
65.115643 65 0% 62.86 2.14 (3.3%)
Show classification Show in data acquisition
37.114512 37 0% 44.29 7.29 (19.7%)
Show classification Show in data acquisition
65.120014 65 0% 63.41 1.59 (2.4%)
Show classification Show in data acquisition
37.114327 37 0% 43.98 6.98 (18.9%)
Show classification Show in data acquisition
37.114327 copy 37 0% 43.98 6.98 (18.9%)
Show classification Show in data acquisition
85.121154 85 0% 82.79 2.21 (2.6%)
Show classification Show in data acquisition
37.114419 37 0% 43.51 6.51 (17.6%)
Show classification Show in data acquisition
85.121008 85 0% 81.16 3.84 (4.5%)
Show classification Show in data acquisition
85.131734 85 0% 79.79 5.21 (6.1%)
Show classification Show in data acquisition
65.130912 65 0% 67.97 2.97 (4.6%)
Show classification Show in data acquisition
37.124510 37 0% 42.04 5.04 (13.6%)
Show classification Show in data acquisition
65.125904 65 0% 67.42 2.42 (3.7%)
Show classification Show in data acquisition
0.123042 0 0% -2.53 2.53
Show classification Show in data acquisition
100.110509 100 0% 96.55 3.45 (3.4%)
Show classification Show in data acquisition
65.125959 65 0% 67.65 2.65 (4.1%)
Show classification Show in data acquisition
0.122946 0 0% -1.7 1.70
Show classification Show in data acquisition

Model testing output

Results

Model version:
Accuracy
0.00%
Mean squared error
16.37

Confusion matrix

valueuncertain
value--
f1 score0.00

Feature explorer

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