Reduce multiclass image classification to binary classification in Pytorch-CodePudding

I am working on an stl-10 image dataset that consists of 10 different classes. I want to reduce this multiclass image classification problem to the binary class image classification such as class 1 Vs rest. I am using PyTorch torchvision to download and use the stl data but I am unable to do it as one Vs the rest.

train_data=torchvision.datasets.STL10(root='data',split='train',transform=data_transforms['train'], download=True)
test_data=torchvision.datasets.STL10(root='data',split='test',transform=data_transforms['val'], download=True)

train_dataloader = DataLoader(train_data,batch_size = 64,shuffle=True,num_workers=2)
test_dataloader = DataLoader(test_data,batch_size = 64,shuffle=True,num_workers=2)

CodePudding user response：

You need to relabel the image. At the beginning, class 0 corresponds to label 0, class 1 corresponds to label 1, ..., and class 10 corresponds to label 9. If you want to achieve binary classification, you need to change the label of the picture of category 1 (or other) to 0, and the picture of all other categories to 1.

CodePudding user response：

One way is to update label values at runtime before passing them to loss function in the training loop. Let's say we want to relabel class 5 as 1, and the rest as 0:

my_class_id = 5
for imgs, labels in train_dataloader:
    labels = torch.where(labels == my_class_id, 1, 0)
    ...

You may also need to do similar relabeling for test_dataloader. Also, I am not sure about the datatype of labels. If its float, change accordingly.