I train a DeepLabV3 and FCN (torchvision) with own dataset (2 classes: background and my object) and use the metrics calculated in the class ConfusionMatrix in the utils.py.
Can someone explain these metrics to me? I know the IoU, but what do “global correct” and “average row correct” mean?
Test: [ 0/32] eta: 0:00:18 time: 0.5879 data: 0.1397 max mem: 0
Test: Total time: 0:00:18
global correct: 99.5
average row correct: ['99.9', '88.2']
IoU: ['99.4', '86.9']
mean IoU: 93.2
Hi,
average row correct shows the accuracy per class - so the amount it predicted this class correct divided by the amount it predicted a pixel as this class. In the code this is given as acc = torch.diag(h) / h.sum(1), the diagonal of h are the correctly predicted pixels per class and the h.sum(1) is just the sum of each row in the ConfusionMatrix - so the total amount this class got predicted.
The global correct is your pixel accuracy, which tells you how much of your image got correctly classified - in the code: acc_global = torch.diag(h).sum() / h.sum().
Easy example
Actual Class
| Cat | Dog |
Predicted Cat | 5 | 2 |
class Dog | 3 | 4 |
average row correct:
cat accuracy: 5 / (5 + 2) = 0.71
dog accuracy: 4 / (3 + 4) = 0.57
global correct:
total acc = (5 + 4) / (5 + 2 + 3 + 4) = 0.64
IoU:
cat IoU = 5 / (2 + 5 + 3) = .5
dog IoU = 4 / (2 + 4 + 3) = .44
mIoU = .47
