text: Title: EMA with high decay results in worse performance because of 1) no zero-init and 2) no debias.
  Body: When using [ModelEMAV2](https://github.com/rwightman/pytorch-image-models/blob/a2727c1bf78ba0d7b5727f5f95e37fb7f8866b1f/timm/utils/model_ema.py#L82) with decay >=0.99999 and ~25k iterations, performance is worse than expected.
  
  Encountered this bug when fine-tuning on ImageNet with EMA.
  
  Fixed this locally by following the[ optax implementation of EMA](https://github.com/deepmind/optax/blob/252d152660300fc7fe22d214c5adbe75ffab0c4a/optax/_src/transform.py#L120-L158), posting here in case other people encounter the same thing.
  
  There are two things which differ in the optax implementation.
  1) [EMA is initialized with zeros](https://github.com/deepmind/optax/blob/252d152660300fc7fe22d214c5adbe75ffab0c4a/optax/_src/transform.py#L143-L147).
  2) [Bias correction is applied to EMA](https://github.com/deepmind/optax/blob/252d152660300fc7fe22d214c5adbe75ffab0c4a/optax/_src/transform.py#L103-L106).
  
  Apologies if I am missing something or mis-using the timm EMA implementation. Just figured this would be helpful to post in case others are using EMA with high decay. If I am not missing something, I'm happy to submit a PR for this.