Open ia-gu opened 1 month ago
Hi,
I am trying to fine-tune ViP-LLaVA with my own dataset. However, the performance of the model is different in each experiments, even though I fixed the random seed like below.
def set_global_seed(seed): os.environ['PYTHONHASHSEED'] = str(seed) random.seed(seed) np.random.seed(seed) torch.manual_seed(seed) torch.cuda.manual_seed(seed) torch.cuda.manual_seed_all(seed) set_seed(seed) torch.backends.cudnn.deterministic = True torch.backends.cudnn.benchmark = False
I found the same problem here, LLaVA 1.5, your basic model.
In your experiment, did you fix the random seed to get the same result in every experiment? If so, I would like to know how to fix it.
Thanks in advance.
Thanks for your question, No, I did not set the seed for my experiments.
I think given a trained checkpoint, at least for evaluation you can fix the seed.
Describe the issue
Hi,
I am trying to fine-tune ViP-LLaVA with my own dataset. However, the performance of the model is different in each experiments, even though I fixed the random seed like below.
I found the same problem here, LLaVA 1.5, your basic model.
In your experiment, did you fix the random seed to get the same result in every experiment? If so, I would like to know how to fix it.
Thanks in advance.