"Instance segmentation is an extension of object detection where we not only localize an object but also generate a binary mask for each single object detected. There are two main categories of instance segmentation algorithms: detection base instance segmentation algorithms and single-shot instance segmentation algorithms. The first approach generates higher quality masks, however, the latter one is faster. My previous article, Instance segmentation algorithms overview,"
"IoU The basic evaluation criterion for measuring the quality of the generated masks is Intersection over Union (IoU). It measures the intersection area between two bounding boxes or, in the case of instance segmentation, it measures the intersection area between two masks. The idea of IoU is best explained by the image below. Figure 1: Green - ground truth, red - prediction. Source: own elaboration based on image acquired from"
Instance segmentation extends object detection by producing a binary mask for each detected object. Two main algorithm families exist: detection-based methods, which produce higher-quality masks, and single-shot methods, which are faster. The primary evaluation metric for instance segmentation is mean Average Precision (mAP). Intersection over Union (IoU) measures overlap between predicted and ground-truth masks and ranges from 0 (no overlap) to 1 (perfect overlap). IoU equals zero whenever masks do not overlap, regardless of proximity. IoU is not fully differentiable and therefore unsuitable as a loss function. Generalized IoU provides a variant that penalizes distance between non-overlapping masks.
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