ei-ready-device-testing / cars-in-parking-garage Public
Target: Qualcomm Dragonwing RB3 Gen 2 Development Kit 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 Precision score Result
cars1 vehicle, vehicle 100%
Show classification Show in data acquisition Show in feature explorer
frame vehicle, vehicle, vehicle 94%
Show classification Show in data acquisition Show in feature explorer
frame vehicle, vehicle, vehicle 100%
Show classification Show in data acquisition Show in feature explorer
frame vehicle, vehicle, vehicle 100%
Show classification Show in data acquisition Show in feature explorer
frame vehicle, vehicle, vehicle 96%
Show classification Show in data acquisition Show in feature explorer
frame vehicle, vehicle, vehicle, vehicle 89%
Show classification Show in data acquisition Show in feature explorer
frame - 100%
Show classification Show in data acquisition Show in feature explorer
frame - 100%
Show classification Show in data acquisition Show in feature explorer
frame - 100%
Show classification Show in data acquisition Show in feature explorer
frame vehicle, vehicle, vehicle, vehicle, vehicle 96%
Show classification Show in data acquisition Show in feature explorer
frame vehicle, vehicle, vehicle 100%
Show classification Show in data acquisition Show in feature explorer
frame vehicle, vehicle, vehicle 95%
Show classification Show in data acquisition Show in feature explorer
frame vehicle, vehicle, vehicle 100%
Show classification Show in data acquisition Show in feature explorer
frame vehicle, vehicle 95%
Show classification Show in data acquisition Show in feature explorer
frame vehicle, vehicle 95%
Show classification Show in data acquisition Show in feature explorer
frame vehicle, vehicle 95%
Show classification Show in data acquisition Show in feature explorer
frame vehicle, vehicle, vehicle 100%
Show classification Show in data acquisition Show in feature explorer
frame vehicle 89%
Show classification Show in data acquisition Show in feature explorer
frame vehicle, vehicle, vehicle, vehicle 100%
Show classification Show in data acquisition Show in feature explorer
frame vehicle, vehicle 100%
Show classification Show in data acquisition Show in feature explorer
frame vehicle, vehicle, vehicle, vehicle 94%
Show classification Show in data acquisition Show in feature explorer
frame vehicle 100%
Show classification Show in data acquisition Show in feature explorer
frame vehicle 69%
Show classification Show in data acquisition Show in feature explorer
frame vehicle, vehicle, vehicle, vehicle 63%
Show classification Show in data acquisition Show in feature explorer
frame vehicle, vehicle 100%
Show classification Show in data acquisition Show in feature explorer
frame vehicle 100%
Show classification Show in data acquisition Show in feature explorer
frame vehicle 89%
Show classification Show in data acquisition Show in feature explorer
frame vehicle 89%
Show classification Show in data acquisition Show in feature explorer
frame vehicle 100%
Show classification Show in data acquisition Show in feature explorer
frame vehicle, vehicle, vehicle, vehicle 97%
Show classification Show in data acquisition Show in feature explorer
frame vehicle, vehicle 100%
Show classification Show in data acquisition Show in feature explorer
frame vehicle, vehicle 100%
Show classification Show in data acquisition Show in feature explorer
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Model testing output

Results

Model version:
Accuracy
93.75%

Metrics for Object detection

Metric Value
mAP 0.58
mAP@[IoU=50] 0.91
mAP@[IoU=75] 0.69
mAP@[area=small] 0.24
mAP@[area=medium] 0.57
mAP@[area=large] 0.68
Recall@[max_detections=1] 0.32
Recall@[max_detections=10] 0.65
Recall@[max_detections=100] 0.65
Recall@[area=small] 0.40
Recall@[area=medium] 0.64
Recall@[area=large] 0.71

Confusion matrix

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Feature explorer

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