DY L / 蔬菜识别111 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
OIP-C	大白菜	0%	Show classification Show in data acquisition Show in feature explorer
OIP-C (6)	大白菜, 大白菜	0%	Show classification Show in data acquisition Show in feature explorer
OIP-C	土豆	0%	Show classification Show in data acquisition Show in feature explorer
OIP-C (7)	土豆	0%	Show classification Show in data acquisition Show in feature explorer
1515827015806	莴苣	0%	Show classification Show in data acquisition Show in feature explorer
1515827010211	莴苣	0%	Show classification Show in data acquisition Show in feature explorer
1515827001039	青瓜	0%	Show classification Show in data acquisition Show in feature explorer
1515827003230	青瓜	0%	Show classification Show in data acquisition Show in feature explorer
1515827073847	莲藕	0%	Show classification Show in data acquisition Show in feature explorer
1515827051423	莲藕	0%	Show classification Show in data acquisition Show in feature explorer

Model testing output

Results

Accuracy

0.00%

Metrics for Object detection

Metric	Value
Precision (non-background)	0.00
Recall (non-background)	0.00
F1 Score (non-background)	0.00

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

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	DL DY L

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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.

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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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Test data

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Model testing output

Results

Accuracy

Metrics for Object detection

Confusion matrix

Feature explorer

Test data

Model variants

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Model testing output

Results

Accuracy

Metrics for Object detection

Confusion matrix

Feature explorer

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