A summary of the currently proposed evaluation metrics.
| Metric | Advantage | Disadvantage |
|---|---|---|
| F1-score | Focuses on evaluating the presence of a predicted object corresponding to the ground truth object | Does not account for pixel-level errors |
| IoU (Jaccard Index) | Measures the conformance of shape between ground truth and prediction | Does not account for object level errors |
| Dice Similarity Coefficient (DSC) | Measure of pixel wise agreement between ground truth and prediction | Does not penalize detection errors |
| Aggregated Jaccard Index (AJI) | Penalizes both object level and pixel level errors | Over penalization owing to failed detections |
| Panoptic Quality (PQ) | Unified scoring of detection and segmentation | Dependent on IoU with a strict threshold and hence may result in a lower score |
| Metric | Advantage | Disadvantage |
|---|---|---|
| F1-score | Focuses on evaluating the presence of a predicted object corresponding to the ground truth object | Does not account for pixel-level errors |
| IoU (Jaccard Index) | Measures the conformance of shape between ground truth and prediction | Does not account for object level errors |
| Dice Similarity Coefficient (DSC) | Measure of pixel wise agreement between ground truth and prediction | Does not penalize detection errors |
| Aggregated Jaccard Index (AJI) | Penalizes both object level and pixel level errors | Over penalization owing to failed detections |
| Panoptic Quality (PQ) | Unified scoring of detection and segmentation | Dependent on IoU with a strict threshold and hence may result in a lower score |
Sharing content requires targeting cookies to be enabled. Please update your cookie preferences to use this feature.