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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<br> Xiaojie Li^1, Yibo Yang^2, Jianlong Wu^1, Bernard Ghanem^2, Liqiang Nie^1, Min Zhang^1<br> ^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.