Comments (2)
from pretrained-backbones-unet.
Greetings @SamratSinghRathore,
The divergence/non-convergence of a model may depend on more than one situation. I recommend trying different parameters and different encoders. You may also consider freezing specific layers of the encoder. Another option is to freeze the entire encoder and train only the decoder.
In addition, please ensure to pre-process your data according to the backbone network model you have chosen.
Best regards.
Note: The separate line verbose is due to the 'tqdm' library. This problem may occur in the Jupyter Notebook. I will update the 'tqdm.auto' module.
Perhaps you can use this for a quick solution:
from tqdm import tqdm
tqdm._instances.clear()
from pretrained-backbones-unet.
Related Issues (8)
- Model saving functionality missing HOT 1
- Test script? HOT 2
- Missing argument error in trainer.py when setting lr_scheduler HOT 3
- Unable to train on small patches (64x48) HOT 3
- 'DataLoader' is not defined HOT 1
- DiceLoss question in pretrained-backbones-unet/backbones_unet/model /losses.py HOT 1
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from pretrained-backbones-unet.