# Put pre-trained robust backbone here.

We adopt as backbone the pre-trained adversarially-robust CLIP models from [TeCoA](https://github.com/cvlab-columbia/ZSRobust4FoundationModel). The used pre-trained weights are provided [here](https://emckclac-my.sharepoint.com/:f:/g/personal/k19010102_kcl_ac_uk/EmZ98eFLv71FqQyqPLvWNTkBYNAKPyx_wYEDjNPx7smKCA?e=8AB51S) (provided by [APT (CVPR 2024)](https://github.com/jiamingzhang94/Adversarial-Prompt-Tuning)). To run the code, the pre-trained backbone models should be placed under this directory.  The code currently support architecture: ViT-B/32 (named `vitb32`). Taking an example of tuning ViT-B/32 at epsilon=4/255, the name of checkpoint is `vitb32_eps4.pth.tar`. Note that our code can be easily adapted to load other pre-trained models as backbone.