Comments (2)
closing since this is not related to the code released here.
from light-weight-refinenet.
Hi, @goldcarpenter , I also encounter this problem! When I set NUM_CLASSES = 2 for my own data to classify foreground and background, the loss down to 0 quickly. And after 5 epochs, i got mIOU=1 for all validation images! If you have solved this problem, please let me know, Thank you~
from light-weight-refinenet.
Related Issues (20)
- Having a hard time reproducing the results for NYU dataset HOT 4
- train mbv2 model HOT 2
- Error when importing miou_utils HOT 2
- LOSS meaniou no change HOT 3
- How does a single GPU run train?
- The following error occurs when changing a category to your own category HOT 2
- How to infer with my own trained model? HOT 3
- class dictionary HOT 4
- Cityscapes's Model
- RuntimeError: cuDNN error: CUDNN_STATUS_NOT_INITIALIZED HOT 2
- InvertedResidualBlock adding extra ConvBNReLU than the vanilla implementation HOT 1
- How to calculate FPS? HOT 4
- how to get the FLOPs? HOT 2
- Add CPU-only in serialization.py
- How to use ResNet-18 as backbone? HOT 2
- Visualizing the training process
- ./src/config HOT 2
- No datasets file
- Broken pipe HOT 5
- Some questions about transposing the data and results HOT 2
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