Comments (2)
The unlabeled class is part of the class list provided by the dataset. Classes which are not of interest can be put in unlabeled
classes by using the mapping table.
We force the network to not learn this class through this line.
from enet-training.
i'm still quite confused here, if you set weight to 0 for unlabeled area, how do you punish the network when it falsely predict a background pixel as a road pixel? since weight is 0 there won't any loss for this pixel.
from enet-training.
Related Issues (20)
- What is the learning rate decay and preprocessing you used in your training? HOT 13
- Consider hosting the pretrained model on Github HOT 1
- model-cityscapes.net released HOT 1
- what is the test time per image on cpu HOT 1
- implementation different from paper? HOT 1
- Do you freeze weights of encoder while training the whole model HOT 1
- encoder weights HOT 5
- "assertion failed!" When trying to run demo.lua
- In frameimage.lua:17: module 'fastimage' not found: HOT 2
- Not able to reproduce the fps on tx2 HOT 3
- Confused about the camVid dataset used to train encoder HOT 5
- Is the error the same as the loss HOT 1
- lua/5.2/nn/JoinTable.lua:38: bad argument #1 to 'copy' (sizes do not match at /home/user/torch/extra/cutorch/lib/THC/THCTensorCopy.cu:31) HOT 1
- add one more class to pre-trained model
- Which encoder weights should I use as CNNEncoder?
- Cityscapes Test Result HOT 1
- Assertion `t >= 0 && t < n_classes` failed, HOT 1
- Training ENet using own Dataset
- there is no mask when i try the visualization
- input and target should be of same size
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from enet-training.