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bug and question #4
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How you visualize generated label? |
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感谢提供标签生成和评估脚本
1.Little bug in your devkit
data/rasterize.py file in line 22 you have defination:
def mask_for_lines(lines, mask, thickness, idx)
while in line 53 and 55 you switch the order of the last two argument:
mask_for_lines(new_single_line, map_mask, idx, thickness)
this is not correct and it will influence the data flow of metric evaluation enoumously
2.Some data label generating details
the visualization of the label generated based on your orginal script is:
the visualization of the label generated after my modification is:
which is actually more reasonable
3.Question about the papers:
(1)
I actually read your paper thoroughly in great detail and reimplement your decoder part into my architecture with some changes. But I keep your direction predict part. One confusion I have is that: In your paper you said that you use softmax as the activation function for classification and your gt label are all zero except the two opposite direction, which is 1. I am wondering what kind of loss function you use for direction prediction. Do you use the ordinary cross entropy even though you have two 1's? Or do you use binary cross entropy like what multilabel prediction would usually do? But if later, Then why not use sigmoid as the activation functions.
(2)
It can be seen from your script that your bev map is [-30, 30, 0.15] [-15,15,0.15]. Is this also the default settings in Table 2 in your paper? This is not clear in your paper. Also I am wondering the influence of x-y-range and resolution (in your script it is 0.15) on the metric
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