24.04.2021 · Hi, I am trying to build a U-Net Multi-Class Segmentation model for the brain tumor dataset. I implemented the dice loss using nn.module and some guidance from other implementations on the internet. But during my training, my loss is fluctuating and not converging. If I train my model using CrossEntropyLoss it is converging well. When I was debugging with …
Apr 24, 2021 · Hi, I am trying to build a U-Net Multi-Class Segmentation model for the brain tumor dataset. I implemented the dice loss using nn.module and some guidance from other implementations on the internet. But during my training, my loss is fluctuating and not converging. If I train my model using CrossEntropyLoss it is converging well. When I was debugging with the required_gradient it seems to be ...
segmentation_models_pytorch.losses.dice; Source code for segmentation_models_pytorch.losses.dice. ... loss = 1.0-scores # Dice loss is undefined for non-empty classes # So we zero contribution of channel that does not have true pixels # NOTE: A better workaround would be to use loss term `mean(y_pred)` # for this case, ...
29.04.2020 · You can use dice_score for binary classes and then use binary maps for all the classes repeatedly to get a multiclass dice score. I'm assuming your images/segmentation maps are in the format (batch/index of image, height, width, class_map).. import numpy as np import matplotlib.pyplot as plt def dice_coef(y_true, y_pred): y_true_f = y_true.flatten() y_pred_f = …
3D U-Net model for volumetric semantic segmentation written in pytorch. ... BCEDiceLoss (Linear combination of BCE and Dice losses, i.e. alpha * BCE + beta ...
06.05.2020 · Hi!I trained the model on the ultrasonic grayscale image, since there are only two classes, I changed the code to net = UNet(n_channels=1, n_classes=1, bilinear=True), and when I trained, the loss (batch) was around 0.1, but the validation dice coeff was always low, like 7.218320015785669e-9.
Dice Loss¶. The Dice coefficient, or Dice-Sørensen coefficient, is a common metric for pixel segmentation that can also be modified to act as a loss ...
Jul 15, 2021 · After the first epoch, the loss decreased steeply and the mean Dice coefficient reached above 0.985. After that, the mean Dice coefficient gradually improved, showing its highest value (0.9916±0.0012) after the 9th epoch.
30.05.2019 · Hi Nikronic, Thanks for the links! However, None of these Unet implementation are using the pixel-weighted soft-max cross-entropy loss that is defined in the Unet paper (page 5).. I’ve tried to implement it myself using a modified version of this code to compute the weights which I multiply by the CrossEntropyLoss:. loss = …
17.11.2018 · Hey, I am training a simple Unet on dice and BCE loss on the Salt segmentation challenge on Kaggle. My model’s dice loss is going negative after awhile and soon after so does the BCE loss . In this example, I pick a data…
By default, all channels are included. log_loss: If True, loss computed as `- log (dice_coeff)`, otherwise `1 - dice_coeff` from_logits: If True, assumes input is raw logits smooth: Smoothness constant for dice coefficient (a) ignore_index: Label that indicates ignored pixels (does not contribute to loss) eps: A small epsilon for numerical ...
03.12.2020 · The problem is that your dice loss doesn't address the number of classes you have but rather assumes binary case, so it might explain the increase in your loss. You should implement generalized dice loss that accounts for all the classes and return the value for all of them. Something like the following: def dice_coef_9cat(y_true, y_pred ...
Nov 17, 2018 · Hey, I am training a simple Unet on dice and BCE loss on the Salt segmentation challenge on Kaggle. My model’s dice loss is going negative after awhile and soon after so does the BCE loss . In this example, I pick a dataset of only 5 examples and plan to overfit.