Awesome
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()
inmain.py
to the directory of the DATASET. - We provide the training script,
train.sh
. Complete the configuration of experiments, thenbash train.sh
for training and testing.
Citation
If you find this code or our paper useful, please consider citing:<br>
@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<br>