Zoo-Tuning

Code release for Zoo-Tuning: Adaptive Transfer from A Zoo of Models (ICML2021)

Pretrained Models

Pretrained Models	Reference
ImageNet Supervised	https://pytorch.org/vision/stable/models.html#id10
MoCo	https://github.com/facebookresearch/moco
Mask R-CNN	https://pytorch.org/vision/stable/models.html#id41
DeepLabV3	https://pytorch.org/vision/stable/models.html#deeplabv3
Keypoint R-CNN	https://pytorch.org/vision/stable/models.html#keypoint-r-cnn

For convenience, we also provide the pretrained models downloaded from these pages. Download

Datasets

Dataset	Download Link
CIFAR-100	Downloaded automatically from torchvision.
COCO-70	https://github.com/thuml/CoTuning
FGVC Aircraft	http://www.robots.ox.ac.uk/~vgg/data/fgvc-aircraft/
Stanford Cars	http://ai.stanford.edu/~jkrause/cars/car_dataset.html
MIT Indoors	http://web.mit.edu/torralba/www/indoor.html

Requirements

• Python 3.8
• PyTorch 1.8.0
• tqdm
• einops
• requests

Quick Start

• Download and prepare the pretrained models in pretrained_models/.
• Download the DATASET you need or prepare your own DATASET. Then change the dataset paths of get_data_loader() in main.py to the directory of the DATASET.
• We provide the training script, train.sh. Complete the configuration of experiments, then bash train.sh for training and testing.

Citation

If you find this code or our paper useful, please consider citing:

@inproceedings{shu2021zoo,
  title={Zoo-Tuning: Adaptive Transfer from a Zoo of Models},
  author={Shu, Yang and Kou, Zhi and Cao, Zhangjie and Wang, Jianmin and Long, Mingsheng},
  booktitle={International Conference on Machine Learning},
  pages={9626--9637},
  year={2021},
  organization={PMLR}
}

Contact

If you have any problems about our code, feel free to contact

• [email protected]
• [email protected]