Solved by ptrblck in post #2 
              
              
                You could adapt the test code to check all wrong predictions and store the passed images as shown here: 
import torchvision.transforms.functional as TF

def test(args, model, device, test_loader):
    model.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for data, target i…