Awesome
Ofiicial Implementation for Mamba-ND: Selective State Space Modeling for Multi-Dimensional Data
Updates
- Jul 1: Our paper was accepted at ECCV 2024. We also released K400 results and checkpoints.
Model Zoo
Image Classification
Checkpoints available at
Syntax | Acc | Weight |
---|---|---|
Mamba2D-S/8 | 81.7 | weight |
Mamba2D-B/8 | 83.0 | weight |
Video Classification
Syntax | Acc | Weight |
---|---|---|
UCF-101 (Scratch) | 89.6 | weight |
HMDB-51 (Scratch) | 60.9 | weight |
K400 | 81.9 | weight |
3D Segmentation
Syntax | Feature Size | Dice | Weight |
---|---|---|---|
Mamba3D-S/16 | 32 | 83.1 | weight |
Mamba3D-S+/16 | 32 | 83.9 | weight |
Mamba3D-B/16 | 32 | 82.7 | weight |
Mamba3D-B/16 | 64 | 84.7 | weight |
Environment Setup
pip install causal-conv1d>=1.2.0
git install git+https://github.com/state-spaces/mamba.git
For image classification, mmpretrain is required. For video classification, mmaction is required. Please see offical documentation for installation instructions.
Training
Please see refer to the following instructions for each task:
Image classification Video classification Video classification (K400 Pretraining) 3D segmentation
Citation
@article{li2024mamba,
title={Mamba-ND: Selective State Space Modeling for Multi-Dimensional Data},
author={Li, Shufan and Singh, Harkanwar and Grover, Aditya},
journal={arXiv preprint arXiv:2402.05892},
year={2024}
}