Wing / boxing-moves 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
MReady.2u1mrb21	Ready	10s	92%	93 Ready, 7 uncertain, 1 Uppercut	Show classification Show in data acquisition Show in feature explorer
MHook.2u1motq7	Hook	10s	86%	87 Hook, 9 Cross, 5 uncertain	Show classification Show in data acquisition Show in feature explorer
MUppercut.2u1mij74	Uppercut	10s	99%	100 Uppercut, 1 uncertain	Show classification Show in data acquisition Show in feature explorer
MCross.2u1mcrqd	Cross	10s	74%	75 Cross, 14 Jab, 12 uncertain	Show classification Show in data acquisition Show in feature explorer
MJab.2u1mafrk	Jab	10s	88%	89 Jab, 6 Cross, 4 uncertain, 2 Ready	Show classification Show in data acquisition Show in feature explorer

Model testing output

Results

Accuracy

87.92%

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.

NN Classifier

Minimum confidence rating

Should be numeric

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	Name
	W Wing

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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

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