Classes Number maybe not reasonable about espnetv2 HOT 12 CLOSED

Please use Cityscapes code to convert the labels

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ok...

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I now use s=0.5, and 9 classes, total 44000 images,The sky and building result is very good, but the person classes result is so terrible that cannot recognize sometimes, Could you give me some advice?

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How long did you train? Make sure your labels are correct

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About less than 1 hour a epoch, I trained 50 epoches, Label is checked repeatly.
I find when s=1.5, person is recognized in first few epoch, but s = 0.5, it is occured in about 20-30 epoch and other classes perform worse than person not be found.
Now, It is a little embarrassed that when other classes performs nice, people cannot be splited;
when people could, other classes looks not very satisfying...
the mIou is 40% level.

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Is your dataset balanced?

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the number of person is fewer, and I think the aera of the sky and building is much more bigger than person, but I 'm confused how to solve it, If you add extra augmentation to person images, other classes are also become more

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You can compute class-wise weights and pass it to loss function.

Let us assume that you have 2 classes whose area distribution in the dataset is 1000 and 50. Obviously, class 1 has more area and network will be biased towards this class. You can take inverse of these areas now to compute class-wise weights. Now you will have a weight vector with 0.001 and 0.02 as values. When you pass this vector to loss function, class with low pixel area is panelized more.

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ok，thanks for your idea.

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Hi, I find there are pretrained models for segmentation, but got state_dict key errors when load.

Unexpected key(s) in state_dict: "module.net.level1.conv.weight", "module.net.level1.bn.weight", "module.net.level1.bn.bias" ......"module.level5.4.bn.weight", "module.level5.4.act.weight"
Missing key(s) in state_dict: "module.level1.conv.weight", "module.level1.bn.running_var", "module.level1.bn.running_mean", "module.level1.bn.bias"......"module.project_l2.act.weight", "module.project_l1.1.conv.weight"
Is there something wrong?

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You need to first wrap the model inside the DataParallel wrapper and then load the weights, something like this:

model = net.EESPNet_Seg(20, s=1.0)
model = nn.DataParallel(model)
model.load_state_dict(torch.load('../pretrained/espnet.pth'))

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that's right, thank you

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