53.6% Precision and 97.5% Recall
The dataset used to train this model is not publicly available. This model uses precision and recall as its metrics. These are commonly used metrics in classification. This model achieves a recall of 97.52%, and precision of 53.64%. In practical terms this means that the model will most likely identify a license plate if it is present in an image but will also report about twice as many license plates as are truly present in a dataset.
A higher precision score indicates that the majority of labels predicted by the model for different classes are accurate.
Further information here.
A higher recall score indicates that the model finds and predicts correct labels for the majority of the classes it is supposed to find.
The only training information available about this model at its source repository is that the model was based upon the ResNet architecture.
Training dataset details are not publicly available.
Validation dataset details are not publicly available.
The input(s) to this model must adhere to the following specifications:
This model will output the following:
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