These tools and metrics are designed to help AI actors develop and use trustworthy AI systems and applications that respect human rights and are fair, transparent, explainable, robust, secure and safe.
Dice score
The Dice score, also known as the Dice Similarity Coefficient, is a measure of the similarity between two sets of data, usually represented as binary arrays. In the context of image segmentation, for example, the Dice score can be used to evaluate the similarity between a predicted segmentation mask and the ground truth segmentation mask. The Dice score ranges from 0, indicating no overlap, to 1, indicating perfect overlap.
The Dice score is calculated as follows:
Dice score = 2 * (number of common elements) / (number of elements in set A + number of elements in set B)
In other words, the Dice score is equal to twice the size of the intersection divided by the sum of the sizes of the two sets. This means that the Dice score measures the proportion of overlap between the two sets, normalized by the size of the sets.
About the metric
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