split miniimagenet to train val and test

[vainaixr]

hello,
i ran the code for classification on google colab, downloading files on google drive.

the images folder containing 60,000 images crashes as google drive cannot handle so many files within one folder.

is a modification in the code, that is split into train, test and val folders, that contain images possible, this is the link for these splits
https://meta-transfer-learning.yaoyao-liu.com/download/

thanks

ok I was able to run it

[lmzintgraf]

Glad to hear you got it to work! Sorry I didn't have time to look into this yet. Did you use the split or did it work without? I like the suggestion of using the split. If you managed to integrate this it would be great if you can open a pull request :)

[vainaixr]

I modified the classification code , I ran it with adabound optimizer, cosine annealing scheduler, and added dropout, should I create a pull request, I have modified certain things, I forked and uploaded it there.

I split dataset into train, val and test, and placed them in miniimagenet/{train}{val}{test} folders.

I added datetime, to save the results in new folder.

also, how to make cavia transductive, or proto cavia, just like we have transductive maml, and proto maml.

[lmzintgraf]

Cool! Did you run the experiment with those changes? I'm very curious what will happen and if you can get an improvement.

A pull request with just the train/va/test set split would be great.

I haven't looked into something like transductive / proto cavia. For Proto-MAML, are you referring to this paper https://arxiv.org/pdf/1903.03096.pdf? I think something similar can be done with CAVIA as well. I'm not familiary with transductive MAML, could you point me to a paper? (Also happy to continue this via email - luisa.zintgraf@cs.ox.ac.uk)