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Typical metrics used with segmentation problems are Recall, Precision and the F1 Score (similar or the same as the Dice score depending on the definition used). These can be evaluated per class or for all classes together, commonly referred to as micro and macro averages. Taking it further, you may wish to have a metric more robust to changes in the ...


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The most generic answer to this question is: the same metrics you use to evaluate the quality of your model during training or in test phase. (Plus the timing of inference if you're referring to computational efficiency) And I'm not referring to any specific metric yet cause that's really task dependent. But in general if you have a model that perform a task ...


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