Comments (2)
Hi @vribeiro1 , thanks for your interest in our work! The code here is just an example for anyone interested in the SegAN idea to try it out. The details may not be the same as we used in the paper such as the preprocessing and the different value of adaptive loss. I didn't get time to organize and fix the code so there may even be some bugs as some other people have found, thus it just serves as an example and is not for reproducing the result.
If you are more interested in getting better performance than playing with the adversarial training idea, you can refer to our updated model we used for ISIC2018. You can find our document on ISIC2018 leaderboard under my name. For the new model we got much better performance than the model we used in the paper. We also tried ISIC2017 data with the new model, where we got over 86.7 dice with a single model while the result in the paper was from a model ensemble (the single model score was around 85.5 before ensemble if I remember correctly).
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Hi, @YuanXue1993 ! Thanks for the fast response. I'm very interested in adversarial training for semantic segmentation and I got pretty excited with your work. I could run your code almost end to end. With the versions I told, it runs pretty well. I'll see what I can do about not having the same results.
Thank you again! Best regards
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Related Issues (20)
- Reproducing the results HOT 3
- The loss of S and C optimization is different HOT 1
- How to configure the models and ground truth for multi-class segmentation?
- Why the discriminator output multiplies 2 and 4?
- Number of Learnable Parameters?
- why NetC.zero_grad()?
- WireframeRenderer
- Source code for multi-class segmentation requested HOT 1
- one of the variables needed for gradient computation has been modified by an inplace operation
- the link of dataset HOT 1
- Custom data set Discriminator loss tend to inf even with small learning rates
- there is an error in line 125 HOT 1
- I want to perform segmentation tasks on my own dataset, but there seems to be a problem with the Nets HOT 1
- Why do we need to clip gradient in netC? HOT 3
- pytorch version? HOT 1
- Errors HOT 1
- loss HOT 3
- Why is the Prediction results all Nan? HOT 3
- i got an error in the dataloader_val , raising an exception HOT 2
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