RosettaCommons / RosettaCommons/RFdiffusion
different protein samples
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Description
Dear Developers, thank you for your help!
I would like to ask: how does RF handle the problem of excessively large loss deviations across different timesteps t and different protein samples during training?
When I use rigid-body translation MSE loss, I find that the loss fluctuates a lot — ranging from thousands down to less than 1. The overall average loss also seems to oscillate, because it may be stuck in a shaking stage, almost without effective learning. Even when I print the average loss at t < 0.2, I find that it still does not show effective learning.
I look forward to your guidance, thank you! Wish you a pleasant day!
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- Read the whole issue, then the project's contributing guide.
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Research direction
No files, tests, or entry points are named. Start by locating the RF training loss calculation and tracing how rigid-body translation MSE is aggregated across timesteps and protein samples. Compare the per-sample and per-timestep loss distributions, with attention to t < 0.2; done means explaining whether the fluctuation is expected or identifying a concrete training issue.
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Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Bug
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100