CIFAR-100 Model #26
Description
In section 4.1 of the paper, you state:
To begin with, we compare FixMatch to various existing methods on the standard CIFAR-10, CIFAR-100, and SVHN benchmarks. As recommended by [31], we reimplemented all existing baselines and performed all experiments using the same codebase. In particular, we use the same network architecture (a Wide ResNet-28-2 [47] with 1.5M parameters) ...
Then in the Appendix, you state:
As mentioned in section 4, we used almost identical hyperparameters of FixMatch on CIFAR-10, CIFAR-100, SVHN and STL-10. Note that we used similar network architectures for these datasets, except that more convolution filters were used for CIFAR-100 to handle larger label space ...
I'm trying to reproduce this particular result, however "more convolution filters" isn't defined in the paper with a number. If my understanding is correct, the codebase is setup so that the default filters for the model is 32, and then you allow users to optionally configure this by passing a --filters argument to the main script. In other words, there's no single source of config files that we can review to understand how CIFAR-100 was treated differently.
Would you be able to provide these implementation details on CIFAR-100?