Understanding IoU and Dice

Within the domain of semantic segmentation, quantitative evaluation of model performance necessitates robust metrics. Two commonly employed measures are the Intersection over Union (IoU) and the Dice coefficient.

The IoU, formally defined as the ratio of the area of intersection to the area of union between the predicted segmentation mask ($P$) and the ground truth mask ($G$), is expressed as:

This metric provides a direct assessment of the spatial overlap between the prediction and the target, ranging from 0 (no overlap) to 1 (perfect congruence).

The Dice coefficient, conversely, quantifies the similarity between the two masks as twice the area of their intersection divided by the sum of their individual areas:

The Dice coefficient, and its related loss function ($1−Dice$), often exhibits a comparatively lower sensitivity to class imbalance, particularly in scenarios involving small object instances.

While primarily utilized in computer vision tasks such as semantic segmentation, the fundamental principle of evaluating the degree of overlap between predicted and ground truth segments finds analogous applications in certain Natural Language Processing tasks. For instance, in named entity recognition, the extent of overlap between predicted and annotated entity spans can be assessed using similar conceptual frameworks, albeit adapted to the sequential nature of textual data.

In summary, both IoU and Dice serve as critical quantitative measures for evaluating the efficacy of segmentation models, with their respective characteristics rendering them suitable for varying analytical contexts and data distributions.