RepAdapter

Official implementation of "Towards Efficient Visual Adaption via Structural Re-parameterization". Repadapter is a parameter-efficient and computationally friendly adapter for giant vision models, which can be seamlessly integrated into most vision models via structural re-parameterization. Compared to Full Tuning, RepAdapter saves up to 25% training time, 20% GPU memory, and 94.6% storage cost of ViT-B/16 on VTAB-1k.

Updates

• (2023/02/16) Release our RepAdapter project.

Data Preparation

We provide two ways for preparing VTAB-1k:

• Download the source datasets, please refer to NOAH.
• We provide the prepared datasets, which can be download from google drive.

After that, the file structure should look like:

$ROOT/data
|-- cifar
|-- caltech101
......
|-- diabetic_retinopathy

• Download the pretrained ViT-B/16 to ./ViT-B_16.npz

Training and Evaluation

1. Search the hyper-parameter s for RepAdapter (optional)

bash search_repblock.sh

2. Train RepAdapter

bash train_repblock.sh

3. Test RepAdapter

python test.py --method repblock --dataset <dataset-name> 

Citation

If this repository is helpful for your research, or you want to refer the provided results in your paper, consider cite:

@article{luo2023towards,
  title={Towards Efficient Visual Adaption via Structural Re-parameterization},
  author={Luo, Gen and Huang, Minglang and Zhou, Yiyi  and Sun, Xiaoshuai and Jiang, Guangnan and Wang, Zhiyu and Ji, Rongrong},
  journal={arXiv preprint arXiv:2302.08106},
  year={2023}
}