如何运用在自己的图片数据集上进行运行
gguyinging opened this issue · comments
我不知道我的理解是否正确,我想我的训练集是原图,用来进行加噪扩散和逆向扩散,我的测试集则是有噪声的一些图片,是吗?我想我的训练集是4000张.png的图片,保存在一个路径中,测试集图片如上,我不知道该如何更改?以及您程序中E://中的tool我需要下载码?
Yes, you should add Gaussian noise with a beta schedule to the original images and train the denoise model. Note that in the testing phase you need to try different t values to find the optimal one. Please ignore the additional system path in my code (that was the directory of the helper function file util.py).
好的老师,就是在OCT——dataloader中践行更改吗?
老师,你好。我遇到了在自己的图片数据集上进行运行,但是其loss value在一百左右,想问一下正常应该是多少?过大或过小可能的原因是什么?
目前程序中的loss value是不是对plot出的图片进行一个验证的损失?我可以加大验证集的数量进行一个平均?
The plot is just a simple observation of the training process. The loss is computed by loss = noise_estimation_loss(model, x, t, e, b), the MSE between the Gaussian noise prediction model(x, t) and the real noise e.
我在想损失值几百,会不会是因为加载图片的时候没有除以255,所以过大?您的损失值大约在什么范围内?
For diffusion applications, the intensity is usually normalized to [-1, 1], otherwise the scheduled small Gaussian noise is not sufficient to convert the image distribution to the pure Gaussian distribution. In this work, I normalized to [1, 3].
所以对于我的图片数据集,应该将强度归一化为【-1,1】,我将做出尝试,谢谢您的建议
There is a more recent code for both unconditional and conditional diffusion here https://github.com/DeweiHu/Medical-Semantic-Diffusion/tree/main/src, I think it is cleaner. You can refer to it if needed.
好的,谢谢您