When I trained my model several times with different seed setting. And I use 70% data for training, 30% data for testing. After training, test on the test set.
But different seed leads to very different test results, like the following:
seed: 41, 93, 142, 194, 245
test: 95%, 90%, 96.67%, 95%, 93.33%
The biggest difference is 6.67 %.
Can anyone help me and explain why?  I’m very confused Because I think different seed should get similar results when using the same training data and test data.
I used this function for seed setting and I used CPU only:
def setup_seed(seed):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
random.seed(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False

Thanks in advance.