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Mamba-FSCIL: Dynamic Adaptation with Selective State Space Model for Few-Shot Class-Incremental Learning

This is the official repository for "Mamba-FSCIL: Dynamic Adaptation with Selective State Space Model for Few-Shot Class-Incremental Learning."

Mamba-FSCIL: Dynamic Adaptation with Selective State Space Model for Few-Shot Class-Incremental Learning
Xiaojie Li^1, Yibo Yang^2, Jianlong Wu^1, Bernard Ghanem^2, Liqiang Nie^1, Min Zhang^1
^1Harbin Institute of Technology (Shenzhen), ^2King Abdullah University of Science and Technology (KAUST)

Mamba-FSCIL Framework

📒 Updates

  • 22 Aug: We updated the arXiv version with additional experiments.
  • 20 Jul: We released the code of our paper.
  • 8 Jul: We released the first version of our paper.

🔨 Installation

Follow these steps to set up your environment:

  • Create and activate a new Conda environment:

    conda create --name mambafscil python=3.10 -y
conda activate mambafscil

  • Install CUDA and cuDNN: Follow the official CUDA installation instructions.

  • Install PyTorch and torchvision:

    • Using pip: 
      pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cu113
    • Using conda: 
      conda install pytorch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 cudatoolkit=11.3 -c pytorch

  • Install MMCV, OpenCV, and other dependencies:

    pip install -U openmim
mim install mmcv-full==1.7.0
pip install opencv-python matplotlib einops rope timm==0.6.12 scikit-learn==1.1.3 yapf==0.40.1
git clone https://github.com/state-spaces/mamba.git; cd mamba; git checkout v1.2.0.post1; pip install .

  • Clone the repository and set up the directory:

    git clone https://github.com/xiaojieli0903/Mamba-FSCIL.git
cd Mamba-FSCIL; mkdir ./data

➡️ Data Preparation

  • Download datasets from this link provided by NC-FSCIL.

  • Organize the datasets in the ./data folder:

    --data
  ----cifar/
  ----CUB_200_2011/
  ----miniimagenet/

🚀 Training

Execute the provided scripts to start training:

CIFAR

sh train_cifar.sh
Session 0 1 2 3 4 5 6 7 8
Mamba-FSCIL 82.8 77.85 73.69 69.67 66.89 63.66 61.48 59.74 57.51

[Base Log] [Incremental Log]

Mini Imagenet

sh train_miniimagenet.sh
Session 0 1 2 3 4 5 6 7 8
Mamba-FSCIL 84.93 80.02 74.61 71.33 69.15 65.62 62.38 60.93 59.36

[Base Log] [Incremental Log]

CUB

sh train_cub.sh
Session 0 1 2 3 4 5 6 7 8 9 10
Mamba-FSCIL 80.9 76.26 72.97 70.14 67.83 65.74 65.43 64.12 62.31 62.12 61.65

[Base Log] [Incremental Log]

✏️ Citation

If you find our work useful in your research, please consider citing:

@article{li2024mamba,
  title={Mamba-FSCIL: Dynamic Adaptation with Selective State Space Model for Few-Shot Class-Incremental Learning},
  author={Li, Xiaojie and Yang, Yibo and Wu, Jianlong and Ghanem, Bernard and Nie, Liqiang and Zhang, Min},
  journal={arXiv preprint arXiv:2407.06136},
  year={2024}
}

👍 Acknowledgments

This codebase builds on FSCIL.Thank you to all the contributors.