Hi @ujin0415 , thank you for your interest, I would be happy to help you with your issues! I never experienced what you are describing, there is of course some variance from one run to the other (maybe +/-1 %), but never on that scale. Can you make a third run to see if the training is more stable now? There is no related settings in the training that I can think of that can explain your issue unfortunately. However, as I explained in the repo, the codebase has undergone a major refactoring before the public release, especially for the configuration system, so maybe there is some difference between the released configs and the one I used for the experiences in the paper. If you cannot reproduce the performance, can you share your configs and logs, and I will see if there is some misconfigured parameters. Let me know if you solved your issue!

Thank you for your kind reply! I tried several more runs but instability still exists.
Unfortunately, we didn't record the first two runs. So I attach our log file(It only contains 49 epochs :( ) for the last run.
output.txt

Thank you so much!

Can you share the full logs of an instable run? Did you also try to run the Swin Imagenet version? Fine-tuning the SOLIDER backbone has always been more difficult than fine-tuning the Imagenet one, I'm therefore wondering if your instabilities are related to SOLIDER. Unfortunately I'm very busy right now so I won't have the time to reproduce you experiments until late November. However, I recently made some runs with a private fork of this public repository, I everything went smoothly, so this might also be an issue related to your environment.