Comments (1)
Here are some ideas:
- Dice loss of 1 indicates no overlap between GT and predictions. Did you observe it on the training or validation set?
- Dice loss is supposed to be robust on highly imbalanced data but it would be still beneficial to sample data in a way that reduces imbalance (by sampling examples and/or providing crops with a larger GT mask area).
- I recommend you to play with the initial learning rate (maybe it is too high and causes model weights to end up in the area from which it is not able to recover)
- Try reducing the number of filters in the first layer
- If your problem is from a different domain, don't load the model with trained weights from the brain MRI dataset
- Debug you data loading pipeline and make sure that the input to the model and GT masks are correct
- Turn off data augmentation for testing if you have any
from brain-segmentation-pytorch.
Related Issues (20)
- Reproducibility issue HOT 5
- how to train on my dataset HOT 5
- Training on a custom dataset HOT 1
- in_channels parameter change causes size mismatch HOT 5
- ValueError: Sample larger than population or is negative HOT 2
- Example on Colab not segmenting the tumor HOT 13
- A puzzle about the code HOT 1
- How can I run the docker container if my GPU isn't nvidia
- Very large images on my dataset
- Run on the test data HOT 3
- Can I run the code on test folder without the masks??
- google colab
- how can I apply other loss functions
- IoU of the model
- Negative loss value HOT 3
- After UNet Inference, how to overlay / superimpose the different size predicted masks to the original image size?
- dice
- Error
- manifest for nvidia/cuda:10.0-cudnn7-devel-ubuntu18.04 not found: manifest unknown: manifest unknown
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from brain-segmentation-pytorch.