I trained the model on the ISIC dataset with the default experimental settings provided in the readme and sampled the same image using the weight files of 15,000, 65,000 and 100,000 iterations. Finally, I got the following three segmentation prediction images.
Obviously, it can be seen that the effect of 65,000 iterations is better than 15,000 iterations, but the result obtained by 100,000 iterations is a pure noise image. I wonder if you have encountered this problem, I suspect that diffusion also has a problem with over-fitting and I would like to ask if there is any way to mitigate this problem. Hope to get your answer, thank you!
I trained the model on the ISIC dataset with the default experimental settings provided in the readme and sampled the same image using the weight files of 15,000, 65,000 and 100,000 iterations. Finally, I got the following three segmentation prediction images.
Obviously, it can be seen that the effect of 65,000 iterations is better than 15,000 iterations, but the result obtained by 100,000 iterations is a pure noise image. I wonder if you have encountered this problem, I suspect that diffusion also has a problem with over-fitting and I would like to ask if there is any way to mitigate this problem. Hope to get your answer, thank you!