ORR99 / cloned_first_project Public

This lists all test data. You can manage this data through Data acquisition.

Test data

Set the 'expected outcome' for each sample to the desired outcome to automatically score the impulse.

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Sample name	Expected outcome	Length	Accuracy	Result
testing.63nla2o5	testing	10s			Show classification Show in data acquisition Show in feature explorer
idle.623n1umc	idle	10s	100%	41 idle	Show classification Show in data acquisition Show in feature explorer
idle.623mumm2	idle	10s	100%	41 idle	Show classification Show in data acquisition Show in feature explorer
idle.623mrrtc	idle	10s	100%	41 idle	Show classification Show in data acquisition Show in feature explorer
idle.623mp8nf	idle	10s	100%	41 idle	Show classification Show in data acquisition Show in feature explorer
circle.623le343	circle	10s	100%	41 circle	Show classification Show in data acquisition Show in feature explorer
circle.623laelo	circle	10s	100%	41 circle	Show classification Show in data acquisition Show in feature explorer
circle.623l56rt	circle	10s	100%	41 circle	Show classification Show in data acquisition Show in feature explorer
up-down.621r801n	up-down	10s	100%	41 up-down	Show classification Show in data acquisition Show in feature explorer
up-down.621r7gud	up-down	10s	100%	41 up-down	Show classification Show in data acquisition Show in feature explorer
up-down.621qpvua	up-down	10s	100%	41 up-down	Show classification Show in data acquisition Show in feature explorer
left-right.621qi47s	left-right	10s	100%	41 left-right	Show classification Show in data acquisition Show in feature explorer
left-right.621qgtnr	left-right	10s	100%	41 left-right	Show classification Show in data acquisition Show in feature explorer
left-right.621qdm4g	left-right	10s	100%	41 left-right	Show classification Show in data acquisition Show in feature explorer
left-right.621q797r	left-right	10s	100%	41 left-right	Show classification Show in data acquisition Show in feature explorer

Model testing output

Results

Accuracy

100.00%

Metrics for Classifier

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

Confusion matrix

Feature explorer

Every learning block has a threshold. This can be the minimum confidence that a neural network needs to have, or the maximum anomaly score before a sample is tagged as an anomaly. You can configure these thresholds to tweak the sensitivity of these learning blocks. This affects both live classification and model testing.

Classifier

Minimum confidence rating

Should be numeric

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	Name
	O ORR99

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Target device

Define your target device requirements to inform model optimizations and performance calculations. No device yet? Use the default settings which you can change at any time.

Target device

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Specify the available RAM and ROM for the model's operation, along with the maximum allowed latency for your specific application. Not sure yet? Start with the defaults and modify them later on.

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| ms

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Test data

Model variants

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