IoU metrics Segmentation

I am trying to use IoU metrics in a segmentation problem. I just copied the metrics code and pasted into my notebook:

def dice(input:Tensor, targs:Tensor, iou:bool=True)->Rank0Tensor:
    "Dice coefficient metric for binary target. If iou=True, returns iou metric, classic for segmentation problems."
    n = targs.shape[0]
    input = input.argmax(dim=1).view(n,-1)
    targs = targs.view(n,-1)
    intersect = (input*targs).sum().float()
    union = (input+targs).sum().float()
    if not iou: return 2. * intersect / union
    else: return intersect / (union-intersect+1.0)

I have changed iou=True to get the IoU metrics and passed this as metrics:

metrics = dice
learn = Learner.create_unet(data, models.resnet34, metrics=metrics)


The plot is weird and I tried different learning rates (1e-2, 1e-02, etc)

Now, My validation numbers and metrics are all zeros. What am I doing wrong?

epoch  train_loss  valid_loss  dice    
1      0.138745    nan         0.000000  (01:17)
2      0.134525    nan         0.000000  (01:15)

Metric shouldn’t matter. In plot you have loss which is different. Also when we look your valid_loss I would say that there is some problem with your data or how you implement it. Can you tell us something about your data.


these both are the same

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@shakur any idea why your valid_loss is too high?

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I am still strugling to find out why?