mIoU for multi-class - deep-learning

Question

I would like to understand how mIoU is calculated for multi-class classification. The formula for each class is
IoU formula
and then the average is done over the classes to get the mIoU. However, I don't understand what happens for the classes that are not represented. The formula becomes a division by 0, so I ignore them and the average is only computed for the classes represented.
The problem is that when a prediction is wrong, the accuracy is really lowered. It adds another class to make the average. For instance : in semantic segmentation the ground-truth of an image is made of 4 classes (0,1,2,3) and 6 classes are represented over the dataset. The prediction is also made of 4 classes (0,1,4,5) but all the items classified in 2 and 3 (in the ground-truth) are classified in 4 and 5 (in the prediction). In this case should we calculate the mIoU over 6 classes ? Even if 4 classes are totally wrong and there respective IoU is 0 ? So the problem is that if just one pixel is predicted in a class that is not in the ground_truth, we have to divide by a higher denominator and it lows a lot the score.
Is it the correct way to compute the mIoU for multi-class (and the semantic segmentation) ?

Answer 1

Instead of calculating the miou of each image and then calculate the "mean" miou over all the images, I calculate the miou as one big image. If a class is not in the image and is not predicited, I set there respective iou equal to 1.
From scratch :
def miou(gt,pred,nbr_mask):
intersection = np.zeros(nbr_mask) # int = (A and B)
den = np.zeros(nbr_mask) # den = A + B = (A or B) + (A and B)
for i in range(len(gt)):
for j in range(height):
for k in range(width):
if pred[i][j][k]==gt[i][j][k]:
intersection[gt[i][j][k]]+=1
den[pred[i][j][k]] += 1
den[gt[i][j][k]] += 1
mIoU = 0
for i in range(nbr_mask):
if den[i]!=0:
mIoU+=intersection[i]/(den[i]-intersection[i])
else:
mIoU+=1
mIoU=mIoU/nbr_mask
return mIoU
With gt the array of ground truth labels and pred the prediction of theassociated images (have to correspond in the array and be the same size).

Answer 2

Adding to the previous answer, this is a great fast and efficient pytorch GPU implementation of calculating the mIOU and classswise IOU for a batch of size (N, H, W) (both pred mask and labels), taken from the NeurIPS 2021 paper "Few-Shot Segmentation via Cycle-Consistent Transformer", github repo available here.
def intersectionAndUnionGPU(output, target, K, ignore_index=255):
# 'K' classes, output and target sizes are N or N * L or N * H * W, each value in range 0 to K - 1.
assert (output.dim() in [1, 2, 3])
assert output.shape == target.shape
output = output.view(-1)
target = target.view(-1)
output[target == ignore_index] = ignore_index
intersection = output[output == target]
area_intersection = torch.histc(intersection, bins=K, min=0, max=K-1)
area_output = torch.histc(output, bins=K, min=0, max=K-1)
area_target = torch.histc(target, bins=K, min=0, max=K-1)
area_union = area_output + area_target - area_intersection
return area_intersection, area_union, area_target
Example usage:
output = torch.rand(4, 5, 224, 224) # model output; batch size=4; channels=5, H,W=224
preds = F.softmax(output, dim=1).argmax(dim=1) # (4, 224, 224)
labels = torch.randint(0,5, (4, 224, 224))
i, u, _ = intersectionAndUnionGPU(preds, labels, 5) # 5 is num_classes
classwise_IOU = i/u # tensor of size (num_classes)
mIOU = i.sum()/u.sum() # mean IOU, taking (i/u).mean() is wrong
Hope this helps everyone!
(A non-GPU implementation is available as well in the repo!)