https://github.com/tianyu139/tangent-model-composition

Code for Tangent Model Composition for Ensembling and Continual Fine-tuning (ICCV 2023) and Tangent Transformers for Composition, Privacy and Removal (ICLR 2024)
https://github.com/tianyu139/tangent-model-composition

Last synced: 9 days ago
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Code for Tangent Model Composition for Ensembling and Continual Fine-tuning (ICCV 2023) and Tangent Transformers for Composition, Privacy and Removal (ICLR 2024)

• Host: GitHub
• URL: https://github.com/tianyu139/tangent-model-composition
• Owner: tianyu139
• License: mit
• Created: 2023-07-20T05:45:53.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2024-05-14T16:51:51.000Z (6 months ago)
• Last Synced: 2024-08-02T15:27:30.026Z (3 months ago)
• Language: Python
• Homepage:
• Size: 225 KB
• Stars: 5
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Tangent Model Composition (ICCV 2023, ICLR 2024)

![TMC](tmc.png)

Official code repository for 

- [Tangent Model Composition for Ensembling and Continual Fine-tuning (ICCV 2023)](https://arxiv.org/abs/2307.08114) 

- [Tangent Transformers for Composition, Privacy and Removal (ICLR 2024)](https://arxiv.org/abs/2307.08122)

### Requirements

Our repository is based on PyTorch. We use Torch 1.12 and Python 3.9, other versions have not been tested.

In addition, the following packages are also needed:

```

pip install hydra-core==1.2.0

```

### Datasets

Create a folder for storing datasets in the main directory 

```

mkdir data

```

We provide example scripts for setting up MIT-67 and Oxford Pets in the `setup` directory

```

bash setup/setup_mit.sh

bash setup/setup_oxfordpets.sh

```

### Reproducing results

Our results for the Class Incremental (Class-IL) setting and Data Incremental (Data-IL) can be 

reproduced using 

```

bash scripts/compose.sh

```

and changing the variables appropriately.

For composition tasks on Tangent Transformers, an example script can be found in

```

bash scripts/compose_vit.sh

```

which can be adapted to one's needs. To obtain the best hyperparameters for each dataset, please refer

to Appendix A of the [original paper](https://arxiv.org/abs/2307.08122)

If you find this useful for your work, please consider citing

```

@inproceedings{liu2023tangent,

  title={Tangent Model Composition for Ensembling and Continual Fine-tuning},

  author={Liu, Tian Yu and Soatto, Stefano},

  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},

  pages={18676--18686},

  year={2023}

}

@inproceedings{liu2024tangent,

  title={Tangent Transformers for Composition, Privacy and Removal},

  author={Liu, Tian Yu and Golatkar, Aditya and Soatto, Stefano},

  journal={The Twelfth International Conference on Learning Representations},

  year={2024},

  url={https://arxiv.org/abs/2307.08122}

}

```