Aleksei Shabanov

I opened the dedicated issue regarding this: https://github.com/OML-Team/open-metric-learning/issues/222

@isehmi But I guess, it's not a blocker for you If you want to work on this issue. Because you will install the repo via `git clone` from your fork,...

@isehmi I think after we start testing on windows we find more, so, the creating one particular issue is not needed now

@isehmi that's right! thank you

We are still looking for someone who can take care of this issue :)

Hi, Thank you for your interest! Does EVA-CLIP have any requirements different from what we have in OML? (You could check them in the `ci` folder of the repo). Here...

Deu to inactivity

@deepslug Nope, not enough resources. Do you want to try it? If so, the idea is that we want to adapt the approach from this [paper](https://paperswithcode.com/paper/recall-k-surrogate-loss-with-large-batches) and make FNMR@FMR metric...

Got it!

@deepslug thank you for you comment. I'd like to add that we've already implemented similar idea in OML. There is [SurrogatePricisonLoss](https://open-metric-learning.readthedocs.io/en/latest/contents/losses.html#surrogateprecision) -- differentiable version of Precision metric. Experiments showed it...