some question about code #19
Description
When I tried to reproduce the code, I encountered the following problems. I hope I can get your help.
- When I used the pre-training model you uploaded on GitHub, during the fine-tuning process, the training loss and test loss were always greater than 1, and the test-ACC remained about 0.55.
- In trainval.py, there is a loop (for exp_dict in exp_list:) before trainval(), which causes the model to train until the end of the loop. I want to know what is the function of this loop?
- During the pre-training and fine-tuning process, I noticed that the DataLoader generation was the same, with 64 classes for the training set, 16 for the validation set, and 20 for the test set. According to the model approach in your paper, pre-training should take 64 as the base class and divide it into training set and test set; The fine-tuning should use 20 as the Novel class and divide it into a training set and a test set during the fine-tuning process.Now I do not know how to divide the data set, which makes me very confused. I want to know whether I have a wrong understanding of the paper or the code.