MJRoBot (Marcelo Rovai) / Periquito vs Robot - Img Class Public
Target: Arduino Nicla Vision (Cortex-M7 480MHz) 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 Accuracy Result
testing.48m7dfo8 periquito 0% 1 uncertain
Show classification Show in data acquisition
00000 background 100% 1 background
Show classification Show in data acquisition
00005 background 100% 1 background
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00009 background 100% 1 background
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00015 background 100% 1 background
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00017 background 100% 1 background
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00026 background 100% 1 background
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00028 background 100% 1 background
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00033 background 100% 1 background
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00044 background 100% 1 background
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00049 background 100% 1 background
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00050 background 100% 1 background
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00037 background 100% 1 background
Show classification Show in data acquisition
00000 robot 100% 1 robot
Show classification Show in data acquisition
00008 robot 100% 1 robot
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00007 robot 100% 1 robot
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00016 robot 100% 1 robot
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00014 robot 100% 1 robot
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00021 robot 100% 1 robot
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00026 robot 100% 1 robot
Show classification Show in data acquisition
00041 robot 100% 1 robot
Show classification Show in data acquisition
00044 robot 100% 1 robot
Show classification Show in data acquisition
00038 robot 100% 1 robot
Show classification Show in data acquisition
00031 robot 100% 1 robot
Show classification Show in data acquisition
00032 periquito 100% 1 periquito
Show classification Show in data acquisition
00037 periquito 100% 1 periquito
Show classification Show in data acquisition
00041 periquito 100% 1 periquito
Show classification Show in data acquisition
00042 periquito 100% 1 periquito
Show classification Show in data acquisition
00002 periquito 100% 1 periquito
Show classification Show in data acquisition
00007 periquito 100% 1 periquito
Show classification Show in data acquisition
00012 periquito 100% 1 periquito
Show classification Show in data acquisition
00017 periquito 100% 1 periquito
Show classification Show in data acquisition
00021 periquito 100% 1 periquito
Show classification Show in data acquisition
00026 periquito 100% 1 periquito
Show classification Show in data acquisition
00040 periquito 100% 1 periquito
Show classification Show in data acquisition

Model testing output

Results

Model version:
Accuracy
97.14%

Metrics for Transfer learning

Metric Value
Area under ROC Curve 1.00
Weighted average Precision 0.97
Weighted average Recall 0.97
Weighted average F1 score 0.97

Confusion matrix

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Feature explorer

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