Hi @mboudiaf, I wanted to train the fine-tuned baseline from meta-data set (MDS) i.e. concatenate/union all the data sets and all the labels and then train in normal supervised learning. Is the right way to do this this:
I am mainly asking because there needs to be some sort of relabling that takes into account all the data set labels and wanted to know how that was done.
Hi @mboudiaf, I wanted to train the fine-tuned baseline from meta-data set (MDS) i.e. concatenate/union all the data sets and all the labels and then train in normal supervised learning. Is the right way to do this this:
https://github.com/mboudiaf/pytorch-meta-dataset/blob/c6d6922003380342ab2e3509425d96307aa925c5/example.py#L173
I am mainly asking because there needs to be some sort of relabling that takes into account all the data set labels and wanted to know how that was done.
Thank you!