dghimi01 / Ping-Pong Public
Target: Arduino UNO Q (Qualcomm QRB2210) Clone this project

Test data

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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.

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Sample name Expected outcome F1 score Result
motor.6u4u80ef ball 100%
Show classification Show in data acquisition Show in feature explorer
motor.6u4u7r0p ball, motor 100%
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motor.6u4sq73c ball, motor 100%
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motor.6u4qunuu motor 66%
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motor.6u4quffa motor 80%
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motor.6u4qu840 motor 85%
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motor.6u4puuia motor 100%
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motor.6u4putfq motor 100%
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motor.6u4pjeg3 motor 100%
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motor.6u4pjc1j motor 100%
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motor.6u4pja8j motor 100%
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motor.6u4pj6p4 motor 100%
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motor.6u4pj65d motor 100%
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motor.6u4pj237 motor 100%
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motor.6u4pivui motor 100%
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motor.6u4pi98e motor 100%
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motor.6u4pi7e7 motor 100%
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testing.6u1t71op ball 100%
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Ball.6u1nal0l ball 100%
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Ball.6u1nakel ball 100%
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Ball.6u1n9oj1 ball 100%
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testing.6u1n7shu ball 100%
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Ball.6u1mceh4 ball 100%
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Ball.6u1mbps7 ball 100%
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Ball.6u1mbp49 ball 100%
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Ball.6u1mbf7a ball 100%
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Ball.6u1matf6 ball 100%
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Ball.6u1laanq ball 100%
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Ball.6u1l6osr ball 100%
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Ball.6u1l6gp6 ball 100%
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Ball.6u1l67ri ball 100%
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Ball.6u1kfj7e ball 100%
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Ball.6u1keqgp ball 100%
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Ball.6u1kepu5 ball 100%
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Ball.6u1keedn ball 100%
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Ball.6u1ke6cl ball 100%
Show classification Show in data acquisition Show in feature explorer
Ball.6u1kdr7j ball 100%
Show classification Show in data acquisition Show in feature explorer
Ball.6u1kdj42 ball 100%
Show classification Show in data acquisition Show in feature explorer
Ball.6u1kdfpf ball 100%
Show classification Show in data acquisition Show in feature explorer
Ball.6u1kddrm ball 100%
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Ball.6u1kdc4t ball 100%
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Ball.6tvp03b2 ball 100%
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Ball.6tvp015u ball 100%
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Ball.6tvovshs ball 100%
Show classification Show in data acquisition Show in feature explorer
Ball.6tvoi7do ball 100%
Show classification Show in data acquisition Show in feature explorer
Ball.6tvoi6b9 ball 100%
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Ball.6tvohv4t ball 100%
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Ball.6tvohpep ball 100%
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Background.6tvoh5l2 ball 100%
Show classification Show in data acquisition Show in feature explorer
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Model testing output

Results

Model version:
Accuracy
97.96%

Metrics for Object detection

Metric Value
Precision (non-background) 0.95
Recall (non-background) 1.00
F1 Score (non-background) 0.97

Confusion matrix

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Feature explorer

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