Reduce multiclass image classification to binary classification in Pytorch

For torchvision datasets, there is an inbuilt way to do this. You need to define a transformation function or class and add that into the target_transform while creating the dataset.

torchvision.datasets.STL10(root: str, split: str = 'train', folds: Union[int, NoneType] = None, transform: Union[Callable, NoneType] = None, target_transform: Union[Callable, NoneType] = None, download: bool = False)

Here is a working example for reference :

import torchvision
from torch.utils.data import DataLoader
from torchvision import transforms


class Multi2UniLabelTfm():
    def __init__(self,pos_label=5):
        if isinstance(pos_label,int) or isinstance(pos_label,float):
            pos_label = [pos_label,]
        self.pos_label = pos_label

    def __call__(self,y):
        # if y==self.pos_label:
        if y in self.pos_label:
            return 1
        else:
            return 0

if __name__=='__main__':

    test_tfms = transforms.Compose([
        transforms.ToTensor()
    ])
    data_transforms = {'val':test_tfms}


    #Original Labels
    # target_transform = None   

    # Label 5 is converted to 1. Rest are 0.
    # target_transform = Multi2UniLabelTfm(pos_label=5)     

    # Labels 5,6,7 are converted to 1. Rest are 0.
    target_transform = Multi2UniLabelTfm(pos_label=[5,6,7])
    test_data=torchvision.datasets.STL10(root='data',split='test',transform=data_transforms['val'], download=True, target_transform=target_transform)
    test_dataloader = DataLoader(test_data,batch_size = 64,shuffle=True,num_workers=2)

    for idx,(x,y) in enumerate(test_dataloader):
        print(idx,y)

        if idx == 5:
            break

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.

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.