zoo-tuning's Introduction

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}
}