PROBLEMS REGARDING DIFFRENT SPIT OF DATASET

[hungphongtrn]

First, thank you all for this amazing work.

During testing, I came up with a problem like this. First, I trained the model with the 80:10:10 split on my dataset, resulted in 5103 training datasets. However, as I tested on another configure, which is 2:1 (train-test) split only, the test.py function return the error:

#test.py
RuntimeError: Error(s) in loading state_dict for IRRA:
	size mismatch for classifier.weight: copying a param with shape torch.Size([5103, 512]) from checkpoint, the shape in current model is torch.Size([4200, 512]).
	size mismatch for classifier.bias: copying a param with shape torch.Size([5103]) from checkpoint, the shape in current model is torch.Size([4200]).

So, in my observation, the weight partly depends on the initial dataset. So, in the cases when the dataset is being updated frequently, I would have to retrain the whole dataset. Am I missing something? I hope that I could get the answer from you all. Thank you a lot!