Comments (2)
I have done something like that if I understand the question correctly. I am computing the variance over the heatmap to determine how confident it is:
def heatmap_variance(heatmaps):
# copied and modified:
# https://github.com/anibali/dsntnn/blob/4f20f5a85b56d007adef51e5158f5a6dca92794f/dsntnn/__init__.py#L233-L262
# mu = E[X]
values = [normalized_linspace(d, dtype=heatmaps.dtype, device=heatmaps.device) for d in heatmaps.size()[2:]]
mu = linear_expectation(heatmaps, values)
# var = E[(X - mu)^2]
values = [(a - b.squeeze(0)) ** 2 for a, b in zip(values, mu.split(1, -1))]
var = linear_expectation(heatmaps, values)
heatmap_size = torch.tensor(list(heatmaps.size()[2:]), dtype=var.dtype, device=var.device)
return var * (heatmap_size / 2) ** 2
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I have not done this myself, but you could try to derive a confidence score from the normalised heatmap by quantifying how "spread out" it is.
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Related Issues (20)
- Converting to onnx HOT 2
- Get confidence of prediciton per regressed coordinate HOT 8
- RuntimeError: expected flip dims axis >= 0, but got min flip dims=-1 HOT 1
- dsnt in testing phase HOT 8
- Working with 3D HOT 8
- Suggestion: Define X,Y grid so that they include -1 and 1 HOT 6
- DSNT support only 1 point in 1 heatmap? HOT 2
- Question when I use dsnt in my net HOT 8
- A question about the input and target HOT 4
- Is Frobenius computed correctly? HOT 2
- 3 dimension coordinate regression HOT 1
- increase batch size 1 to 16, it made wrong result. HOT 7
- For the normalized_linspace function HOT 2
- Pip install fails HOT 1
- Question re. occluded or missing points in training data HOT 2
- Use generated 2d-guassion heatmap as the regularization. HOT 2
- output coords are negative floats HOT 15
- Values outside (-1,1) HOT 1
- Trace warnings when trying to jit.trace a model HOT 11
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