justherozen / promix Goto Github PK

Hi, I'm Youngjae Kim.
I'm doing an experiment with custom data.
After a certain point (e.g. Epoch 37 Iter 5), the loss is still coming out as nan.
Has this ever happened to you?
If so, how did you solve it?

hi there,
老师请问，噪声的pt文件在哪里呢？

The cifar10 IDN 0.2 and 0.4 noise rate, I can only get about 93.6%-94% acc useing the code, and I can't find the reason. Could you tell me which hyper-parameters you use to train?

Thank you!

我在寻找一种可以解决以下问题的方法：
因为人失误造成的错标，或者说模棱两可的数据标成任意一个类都可以，这样产生的数据集本身存在问题。

可是我的分类任务可能差异性并不是很明显，属于细颗粒度的分类，不像狗和茶杯这样的类别，而是OK和NG这样的判定，可能稍微有一点裂缝没啥问题，裂缝太大才有问题，稍微有一点缺失没问题，缺失很大才有问题，您的这个方法可以应用在我这个任务上吗？像我这种二分类是不是使用回归的方法效果更好哇，希望能得到您的回复。

CIFAR10 seems to run but when I take CIFAR100 it gives this error?

Any idea.Is there any problem with the file 100_human.pt](https://github.com/UCSC-REAL/cifar-10-100n/blob/main/data/CIFAR-100_human.pt)

I see models are different

Hello, thank you for your nice work.
I want to know how to generate or download CIFAR-10_human.pt?

Which file needs to be run to reproduce the experiment?

cifar10:clean_label | Epoch [ 10/ 40] Iter[ 1/782] Net1 loss: 0.95 Net2 loss: 1.22
Traceback (most recent call last):
File "E:\pycharmproject\weak_supervised\ProMix-main\ProMix-main\Train_cifarn.py", line 463, in
train(epoch, dualnet.net1, dualnet.net2, optimizer1, total_trainloader, unlabeled_trainloader)
File "E:\pycharmproject\weak_supervised\ProMix-main\ProMix-main\Train_cifarn.py", line 181, in train
loss_fmix = fmix.loss(logits_fmix, (pseudo_label_c.detach()).long())
File "E:\pycharmproject\weak_supervised\ProMix-main\ProMix-main\fmix.py", line 94, in loss
return fmix_loss(y_pred, y, self.index, self.lam, train, self.reformulate)
File "E:\pycharmproject\weak_supervised\ProMix-main\ProMix-main\fmix.py", line 46, in fmix_loss
y1 = y1.max(1)[1]
RuntimeError: cannot perform reduction function max on tensor with no elements because the operation does not have an identity

on train_promin.py

        # inputs_x: strong augmentation
        # inputs_x2: weak augmentation

Code comments and actual code execution are exactly opposite. What should be the correct one? Is the comment written wrong?