Edge Impulse Experts / Pick And Place Public
Target: Raspberry Pi 4 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.

Apply filters

Sample name Expected outcome Precision score Result
pnp_91 penguin, penguin, penguin, penguin, pig, pig 94%
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pnp_71 penguin, penguin, penguin, pig, pig, pig, pig 95%
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pnp_99 pig, pig, pig, pig 97%
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pnp_83 penguin, penguin 95%
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pnp_74 penguin, penguin 92%
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pnp_75 penguin, pig, pig, pig, pig 100%
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pnp_79 penguin, penguin, penguin, penguin, penguin 91%
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pnp_62 penguin, penguin, penguin, penguin, penguin, pig, pig 96%
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pnp_64 penguin, penguin, penguin, penguin 87%
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pnp_69 pig, pig, pig, pig 100%
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pnp_42 pig 89%
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pnp_41 pig 100%
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pnp_36 pig, pig 100%
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pnp_1 penguin, penguin, penguin, penguin, penguin, pig, pig, pig, pig 77%
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pnp_43 penguin, penguin, penguin, penguin, penguin 91%
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pnp_14 pig, pig 100%
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pnp_21 penguin, penguin, penguin, penguin, pig, pig, pig, pig 94%
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pnp_32 penguin, pig, pig, pig 94%
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pnp_16 penguin, penguin, penguin, penguin, penguin 93%
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pnp_101 penguin 89%
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Model testing output

Results

Model version:
Accuracy
95.00%

Metrics for Object detection

Metric Value
mAP 0.36
mAP@[IoU=50] 0.91
mAP@[IoU=75] 0.19
mAP@[area=small] 0.15
mAP@[area=medium] 0.38
mAP@[area=large] -1.00
Recall@[max_detections=1] 0.15
Recall@[max_detections=10] 0.47
Recall@[max_detections=100] 0.47
Recall@[area=small] 0.15
Recall@[area=medium] 0.49
Recall@[area=large] -1.00

Confusion matrix

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Feature explorer

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