The IOU metric is a pixel based metric which measures overlap using the number of pixels common between groundtruth and predictions divided by the total pixels across both.
IoU= (groundtruth ∩ prediction) /
(groundtruth u predictions)
The dice metric scores model performance by measuring overlap between groundthruth and predictions divided by sum of pixels of both groundtruth and predictions.
dice= 2 * (groundtruth ∩ prediction) /
(groundtruth + prediction)
Note: IoU and Dice metrics weigh factors differently, however both metrics are positively correlated. This means if model A is better than B then this is captured similarly in both metrics.
By plotting a confusion matrix which indicates ground-truth and predicted classes with number of pixels classified in each class, precision and recall is easily computed.
precision= true positives /
true positives + false positives
recall= true positives /
true positives + false negatives
| Accuracy | IoU | Dice Coefficient |
|---|---|---|
| Counts the number of correctly classified pixels | Counts pixels in both label and pred | Similar to IoU, has its own strengths |
| Not suitable for Imbalanced datasets | Counts pixels in either label and pred | Measures average performance to IoU’s measure of worst case performance. |
| High accuracy may not translate to quality predictions | Statistically correlates the counts (ratio) | |
| Accounts for imbalanced data by penalizing FP and FN |
** New shape based metrics would be added soon **