https://arxiv.org/abs/2308.12058
This repository is an official PyTorch implementation of DR-Tune: Improving Fine-tuning of Pretrained Visual Models by Distribution Regularization with Semantic Calibration (ICCV2023).
- python 3.9.7
- pytorch 1.13.1
- torchvision 0.14.1
- GPU NVIDIA GeForce RTX 2080 Ti
The datasets used in Table 1 can be downloaded via their official link.
The datasets used in Table 2 can be downloaded from here and see "vtab-1k".
The pretrained model checkpoints used in the paper can be found in the table below.
Please put the checkpoint in ./pretrained_models.
| Backbone architecture | Pretraining strategy | Url |
|---|---|---|
| ViT-B | Classification | Checkpoint |
| ViT-B | MAE | Checkpoint |
| ViT-L | MAE | Checkpoint |
| ResNet-50 | MoCo-v1 | Checkpoint |
| ResNet-50 | MoCo-v2 | Checkpoint |
| ResNet-50 | PCL | Checkpoint |
| ResNet-50 | HCSC | Checkpoint |
| ResNet-50 | SwAV | Checkpoint |
| ResNet-50/101/152 | InfoMin | Checkpoint |
| ResNeXt-101/152 | InfoMin | Checkpoint |
Fine-tuning a ResNet-50 pretrained by MoCo-v2 on CIFAR10.
CIFAR10 will be automatically downloaded to ./data.
bash train.sh 1 --cfg ./configs/cifar10_k2048_lr001.yaml
If you find our work helpful in your research, please cite it as:
@inproceedings{zhou2023dr,
title={DR-Tune: Improving Fine-tuning of Pretrained Visual Models by Distribution Regularization with Semantic Calibration},
author={Zhou, Nan and Chen, Jiaxin and Huang, Di},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
pages={1547--1556},
year={2023}
}