Comments (6)
Could you please tell me how the split of 'train', 'val', 'test' is done in advance in camvid_loader.py
? As I can see, the original dataset take them as a whole 701_StillsRaw_full
folder.
from pytorch-semseg.
I follow the split from this repo.
https://github.com/alexgkendall/SegNet-Tutorial
This is the repo from segnet author alex kendall.
from pytorch-semseg.
@andyhahaha Thanks a lot for reminding me, and I got the split from SegNet repo. But there exists some conflicts with the CamVid official site as far as i know:
- it claims 32 classes and here we can only see 11
- the label-color mapping is also not so matching with the
label_colous
list in thecamvid_loader.py
for lack of Roadmarking and Pavement
from pytorch-semseg.
@andyhahaha Furthermore, as for your original question, I think shrinking n_class
to 11 naively won't work, because if so, the network will output 11 classes while the ground truth still contains the 12nd class, this will lead to an unexpected error in cross entropy. And in my opinion you'd better set an ignore_index
to exclude the unlabeled "void" class, which can be refered to in the loader of CityScapes dataset as well.
from pytorch-semseg.
same problem, and I wonder whether the color map is not same with the origin paper. I try to test like blew:
label_colours = np.array([Sky, Building, Pole, Road, Pavement, Tree,
SignSymbol, Road_marking, Car,
Pedestrian, Fence, Unlabelled])
from pytorch-semseg.
@andyhahaha @d-li14 did you implement class balancing in the CamVid? How did you deal with unlabelled class?
from pytorch-semseg.
Related Issues (20)
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