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