Shebin Jose Jacob / Label Inspection 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	F1 score
1S1	S1	0%	Show classification Show in data acquisition Show in feature explorer
S20	S2	100%	Show classification Show in data acquisition Show in feature explorer
DC4	DC	100%	Show classification Show in data acquisition Show in feature explorer
DC12	DC	100%	Show classification Show in data acquisition Show in feature explorer
DC2	DC	0%	Show classification Show in data acquisition Show in feature explorer
DC15	DC	100%	Show classification Show in data acquisition Show in feature explorer
INV13	INV, INV	80%	Show classification Show in data acquisition Show in feature explorer
INV14	INV, INV	100%	Show classification Show in data acquisition Show in feature explorer
SM8	SM, SM	100%	Show classification Show in data acquisition Show in feature explorer
SM10	SM, SM	100%	Show classification Show in data acquisition Show in feature explorer
S17	S2	100%	Show classification Show in data acquisition Show in feature explorer
S10	S2	100%	Show classification Show in data acquisition Show in feature explorer
S14	S2	100%	Show classification Show in data acquisition Show in feature explorer
1S9	S1	100%	Show classification Show in data acquisition Show in feature explorer
1S13	S1	100%	Show classification Show in data acquisition Show in feature explorer
1S10	S1	100%	Show classification Show in data acquisition Show in feature explorer

Model testing output

Results

Accuracy

87.50%

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.

Object detection

Score threshold

Should be numeric

These users will be notified over email when this job finishes.

	Name
	Shebin Jose Jacob

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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

Processor family

Clock rate

| MHz

Max

Custom device name

Application budget

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.

RAM

| KB

Max

ROM

| KB

Max

Latency

| ms

Max

YEARLY

$400 /month

billed annually

SAVE 15%

MONTHLY

$475 /month

billed monthly

Test data

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Model testing output

Results

Accuracy

Confusion matrix

Feature explorer

Test data

Model variants

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Add data

Model testing output

Results

Accuracy

Confusion matrix

Feature explorer

Settings