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Towards Model-Agnostic Dataset Condensation by Heterogeneous Models (ECCV 2024, Oral)

Official PyTorch implementation for the ECCV 2024 paper:

Towards Model-Agnostic Dataset Condensation by Heterogeneous Models
Jun-Yeong Moon, Jung Uk Kim $^\dagger$, Gyeong-Moon Park $^\dagger$

Installation

We recommend using a Conda environment to manage dependencies. You can create the required environment by running:

conda create -f environment.yml

Alternatively, you can manually install the necessary packages:

• python>=3.10
• torch>=2.1.0
• timm
• matplotlib
• scikit-learn

Dataset Condensation

To generate a condensed dataset, execute the following script:

./scripts/run_dual.sh CIFAR10 aug_kmeans 10 128 5e-3 ConvNet 0.01 ViT_Tiny_ft 0.001 ./PATH

• Note: All models except ConvNet should have either _ft (pretrained) or _scratch (random initialization) appended to their names. You can find the implementations for ConvNet, ViG-ti, s, b in the models directory. Additionally, any ResNet or Vision Transformer available in the timm library can be used as models.

ConvNet, ViG-ti, s, b is implemented in the models directory.

Also, ResNets, and Vision Transformers that is available in timm library can be used as a model.

Evaluate the Condensed Dataset

To evaluate the condensed dataset, use the following command:

./scripts/run_test_condensation.sh CIFAR10 2000 128 ./PATH --ft

• Note: Append --ft or --scratch at the end of the command depending on the model type used (pretrained or randomly initialized).
• The PATH should include synthetic_images.pth

Acknowledgements

This code is inspired by and builds upon several pioneering works, including:

• Vision GNN (NeurIPS2022)
• CAFE (CVPR2022)
• MTT (CVPR2022)
• IDC (ICML22)
• IDM (CVPR2023)
• DREAM (ICCV2023)

We are grateful to these authors and the wider research community for their contributions.

Citation

@misc{moon2024modelagnosticdatasetcondensationheterogeneous,
      title={Towards Model-Agnostic Dataset Condensation by Heterogeneous Models}, 
      author={Jun-Yeong Moon and Jung Uk Kim and Gyeong-Moon Park},
      year={2024},
      eprint={2409.14538},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2409.14538}, 
}

• Note: The citation information will be updated.

If you experience any problems or discover a bug, please feel free to reach out via email, submit an issue, or submit a pull request.

Your feedback is invaluable in helping us improve, and we will review and address the matter as promptly as possible.