Code of BOFA: Bridge-Layer Orthogonal Low-Rank Fusion for CLIP-Based Class-Incremental Learning

Environment

1 torch 1.13.0

2 torchvision 0.15.0

Dataset

We provide the processed datasets as follows:

• CIFAR100: will be automatically downloaded by the code.
• CUB200: Google Drive: link or OneDrive link
• ImageNet-R: Google Drive: link or Onedrive: link
• ObjectNet: Onedrive: link You can also refer to the filelist and processing code if the file is too large to download.
• Cars: Google Drive: link or OneDrive: link
• UCF: Google Drive: link or OneDrive: link
• Aircraft: Google Drive: link or OneDrive: link
• Food: Google Drive: link or OneDrive: link
• SUN: OneDrive: link

These subsets are sampled from the original datasets. Please note that I do not have the right to distribute these datasets. If the distribution violates the license, I shall provide the filenames instead.

You need to modify the path of the datasets in ./utils/data.py according to your own path.

💡 Running scripts

To prepare your JSON files, refer to the settings in the exps folder and run the following command. All main experiments from the paper are already provided in the exps folder, you can simply execute them to reproduce the results found in the logs folder.

python main.py --config ./exps/[configname].json