Mukesh Sankhla / Image_Data_Object_ Detection 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
BACKGROUND37796	BACKGROUND	Show classification Show in data acquisition Show in feature explorer
BACKGROUND48773	BACKGROUND	Show classification Show in data acquisition Show in feature explorer
BACKGROUND57982	BACKGROUND	Show classification Show in data acquisition Show in feature explorer
HK78823	HK	Show classification Show in data acquisition Show in feature explorer
HK66411	HK	Show classification Show in data acquisition Show in feature explorer
HK53991	USA	Show classification Show in data acquisition Show in feature explorer
HK32868	HK	Show classification Show in data acquisition Show in feature explorer
USA46960	USA	Show classification Show in data acquisition Show in feature explorer
USA89359	USA	Show classification Show in data acquisition Show in feature explorer
USA30846	USA	Show classification Show in data acquisition Show in feature explorer
CN82754	CN	Show classification Show in data acquisition Show in feature explorer
CN58566	CN	Show classification Show in data acquisition Show in feature explorer
CN43110	CN	Show classification Show in data acquisition Show in feature explorer
IN70041	IN	Show classification Show in data acquisition Show in feature explorer
IN26513	IN	Show classification Show in data acquisition Show in feature explorer
IN40140	IN	Show classification Show in data acquisition Show in feature explorer

Model testing output

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.

Object detection

Score threshold

Should be numeric

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	Name
	Mukesh Sankhla

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Model testing output

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